html_url stringlengths 48 51 | title stringlengths 5 280 | comments stringlengths 63 51.8k | body stringlengths 0 36.2k ⌀ | comment_length int64 16 1.52k | text stringlengths 159 54.1k | embeddings listlengths 768 768 |
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https://github.com/huggingface/datasets/issues/4863 | TFDS wiki_dialog dataset to Huggingface dataset | Nvm, I was able to port it to huggingface datasets, will upload to the hub soon | ## Adding a Dataset
- **Name:** *Wiki_dialog*
- **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A
- **Paper: https://arxiv.org/abs/2205.09073
- **Data: https://github.com/google-research/dialog-inpainting
- **Motivation:** *Research and Development on biggest corpus of dialog data*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
| 16 | TFDS wiki_dialog dataset to Huggingface dataset
## Adding a Dataset
- **Name:** *Wiki_dialog*
- **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A
- **Paper: https://arxiv.org/abs/2205.09073
- **Data: https://github.com/google-research/dialog-inpainting
- **Motivation:** *Research and Development on biggest corpus of dialog data*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
Nvm, I was able to port it to huggingface datasets, will upload to the hub soon | [
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https://github.com/huggingface/datasets/issues/4862 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code | Hi hi, instead of using `download_and_extract` function, I only use `download` function: `base_dir = Path(dl_manager.download(urls))`. It turns out that the code works for `datasets==2.2.2`, however, it doesn't work with `datasets==2.4.0`. | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
| 30 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
Hi hi, instead of using `download_and_extract` function, I only use `download` function: `base_dir = Path(dl_manager.download(urls))`. It turns out that the code works for `datasets==2.2.2`, however, it doesn't work with `datasets==2.4.0`. | [
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https://github.com/huggingface/datasets/issues/4862 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code | Hi @yana-xuyan, thanks for reporting.
Indeed you already found the answer: an Excel file should be just downloaded and not downloaded-and-extracted.
The reason why is that if you call also extract, our library will try to infer the compression format (and extract it). And Excel files are viewed as ZIP files and extracted as so (into a directory). This is because the Office Open XML is indeed a zipped file under the hood): https://en.wikipedia.org/wiki/Office_Open_XML
> Office Open XML (also informally known as OOXML) is a **zipped**, XML-based file format
```python
import zipfile
zipfile.is_zipfile("filename.xlsx")
```
returns `True`. | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
| 96 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
Hi @yana-xuyan, thanks for reporting.
Indeed you already found the answer: an Excel file should be just downloaded and not downloaded-and-extracted.
The reason why is that if you call also extract, our library will try to infer the compression format (and extract it). And Excel files are viewed as ZIP files and extracted as so (into a directory). This is because the Office Open XML is indeed a zipped file under the hood): https://en.wikipedia.org/wiki/Office_Open_XML
> Office Open XML (also informally known as OOXML) is a **zipped**, XML-based file format
```python
import zipfile
zipfile.is_zipfile("filename.xlsx")
```
returns `True`. | [
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https://github.com/huggingface/datasets/issues/4862 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code | Hi @albertvillanova, thank you for your reply! Do you have any clue on why the same error still exists with `datasets==2.4.0` even after I don't extract the downloaded file? FYI, if I downgrade to `datasets==2.2.2`, the code works fine. | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
| 39 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
Hi @albertvillanova, thank you for your reply! Do you have any clue on why the same error still exists with `datasets==2.4.0` even after I don't extract the downloaded file? FYI, if I downgrade to `datasets==2.2.2`, the code works fine. | [
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https://github.com/huggingface/datasets/issues/4862 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code | I guess this has to do with the cache: you should remove the previously-wrongly generated directory from the cache; otherwise `datasets` tries to re-use it. | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
| 25 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
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https://github.com/huggingface/datasets/issues/4861 | Using disk for memory with the method `from_dict` | This issue was also causing an OOM in @nateraw 's workflow and shows again that behavior is confusing - we should definitely switch to using the disk IMO | **Is your feature request related to a problem? Please describe.**
I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previous iteration. After some iterations, I have an OOM error.
**Describe the solution you'd like**
The method `from_dict` loads the data in RAM. It could be good to add an option to use the disk instead.
**Describe alternatives you've considered**
To solve the problem, I have to do an intermediate step where I save the new datasets at each iteration with `save_to_disk`. Once it's done, I open them all and concatenate them.
| 28 | Using disk for memory with the method `from_dict`
**Is your feature request related to a problem? Please describe.**
I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previous iteration. After some iterations, I have an OOM error.
**Describe the solution you'd like**
The method `from_dict` loads the data in RAM. It could be good to add an option to use the disk instead.
**Describe alternatives you've considered**
To solve the problem, I have to do an intermediate step where I save the new datasets at each iteration with `save_to_disk`. Once it's done, I open them all and concatenate them.
This issue was also causing an OOM in @nateraw 's workflow and shows again that behavior is confusing - we should definitely switch to using the disk IMO | [
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https://github.com/huggingface/datasets/issues/4858 | map() function removes columns when input_columns is not None | Hi! Thanks for reporting! This looks like a bug. I've just opened a PR with the fix. | ## Describe the bug
The map function, removes features from the dataset that are not present in the _input_columns_ list of columns, despite the columns being removed not mentioned in the _remove_columns_ argument.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"a" : [1,2,3],"b" : [0,1,0], "c" : [2,4,5]})
def double(x,y):
x = x*2
y = y*2
return {"d" : x, "e" : y}
ds.map(double, input_columns=["a","c"])
```
## Expected results
```
Dataset({
features: ['a', 'b', 'c', 'd', 'e'],
num_rows: 3
})
```
## Actual results
```
Dataset({
features: ['a', 'c', 'd', 'e'],
num_rows: 3
})
```
In this specific example feature **b** should not be removed.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: linux (colab)
- Python version: 3.7.13
- PyArrow version: 6.0.1
| 17 | map() function removes columns when input_columns is not None
## Describe the bug
The map function, removes features from the dataset that are not present in the _input_columns_ list of columns, despite the columns being removed not mentioned in the _remove_columns_ argument.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"a" : [1,2,3],"b" : [0,1,0], "c" : [2,4,5]})
def double(x,y):
x = x*2
y = y*2
return {"d" : x, "e" : y}
ds.map(double, input_columns=["a","c"])
```
## Expected results
```
Dataset({
features: ['a', 'b', 'c', 'd', 'e'],
num_rows: 3
})
```
## Actual results
```
Dataset({
features: ['a', 'c', 'd', 'e'],
num_rows: 3
})
```
In this specific example feature **b** should not be removed.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: linux (colab)
- Python version: 3.7.13
- PyArrow version: 6.0.1
Hi! Thanks for reporting! This looks like a bug. I've just opened a PR with the fix. | [
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https://github.com/huggingface/datasets/issues/4857 | No preprocessed wikipedia is working on huggingface/datasets | Thanks for reporting @aninrusimha.
Please, note that the preprocessed datasets are still available, as described in the dataset card, e.g.: https://huggingface.co/datasets/wikipedia
```python
ds = load_dataset("wikipedia", "20220301.en")
``` | ## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
| 27 | No preprocessed wikipedia is working on huggingface/datasets
## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
Thanks for reporting @aninrusimha.
Please, note that the preprocessed datasets are still available, as described in the dataset card, e.g.: https://huggingface.co/datasets/wikipedia
```python
ds = load_dataset("wikipedia", "20220301.en")
``` | [
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https://github.com/huggingface/datasets/issues/4857 | No preprocessed wikipedia is working on huggingface/datasets | This is working now, but I was getting an error a few days ago when running an existing script. Unfortunately I did not do a proper bug report, but for some reason I was unable to load the dataset due to a request being made to the wikimedia website. However, its working now. Thanks for the reply! | ## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
| 57 | No preprocessed wikipedia is working on huggingface/datasets
## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
This is working now, but I was getting an error a few days ago when running an existing script. Unfortunately I did not do a proper bug report, but for some reason I was unable to load the dataset due to a request being made to the wikimedia website. However, its working now. Thanks for the reply! | [
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https://github.com/huggingface/datasets/issues/4856 | file missing when load_dataset with openwebtext on windows | I have tried to extract ```0015896-b1054262f7da52a0518521e29c8e352c.txt``` from ```17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz``` with 7-zip
and put the file into cache_path ```F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa```
there is still raise the same error and I find the file was removed from cache_path after I run the run_mlm.py with ```python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base```. | ## Describe the bug
0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz with 7-zip.
## Steps to reproduce the bug
```sh
python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base
```
or
```python
from datasets import load_dataset
load_dataset("openwebtext", None, cache_dir=None, use_auth_token=None)
```
## Expected results
Loading is successful
## Actual results
Traceback (most recent call last):
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 795, in _download_and_prepare
raise OSError(
OSError: Cannot find data file.
Original error:
[Errno 22] Invalid argument: 'F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa/0015896-b1054262f7da52a0518521e29c8e352c.txt'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: windows
- Python version: 3.8.5
- PyArrow version: 9.0.0
| 55 | file missing when load_dataset with openwebtext on windows
## Describe the bug
0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz with 7-zip.
## Steps to reproduce the bug
```sh
python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base
```
or
```python
from datasets import load_dataset
load_dataset("openwebtext", None, cache_dir=None, use_auth_token=None)
```
## Expected results
Loading is successful
## Actual results
Traceback (most recent call last):
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 795, in _download_and_prepare
raise OSError(
OSError: Cannot find data file.
Original error:
[Errno 22] Invalid argument: 'F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa/0015896-b1054262f7da52a0518521e29c8e352c.txt'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: windows
- Python version: 3.8.5
- PyArrow version: 9.0.0
I have tried to extract ```0015896-b1054262f7da52a0518521e29c8e352c.txt``` from ```17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz``` with 7-zip
and put the file into cache_path ```F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa```
there is still raise the same error and I find the file was removed from cache_path after I run the run_mlm.py with ```python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base```. | [
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] |
https://github.com/huggingface/datasets/issues/4855 | Dataset Viewer issue for super_glue | Thanks for reporting @wzsxxa.
However the "super_glue" dataset is rendered properly by the Dataset preview: https://huggingface.co/datasets/super_glue | ### Link
https://huggingface.co/datasets/super_glue
### Description
can't view super_glue dataset on the web page
### Owner
_No response_ | 16 | Dataset Viewer issue for super_glue
### Link
https://huggingface.co/datasets/super_glue
### Description
can't view super_glue dataset on the web page
### Owner
_No response_
Thanks for reporting @wzsxxa.
However the "super_glue" dataset is rendered properly by the Dataset preview: https://huggingface.co/datasets/super_glue | [
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https://github.com/huggingface/datasets/issues/4852 | Bug in multilingual_with_para config of exams dataset and checksums error | Hi @albertvillanova. Unfortunately I still get this error. Is this because the merge has yet to be released? Is there a way to track the release? | ## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksumError:
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/train_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/dev_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_vi_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_vi_with_para.jsonl.tar.gz']
```
CC: @thesofakillers | 26 | Bug in multilingual_with_para config of exams dataset and checksums error
## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksumError:
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/train_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/dev_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_vi_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_vi_with_para.jsonl.tar.gz']
```
CC: @thesofakillers
Hi @albertvillanova. Unfortunately I still get this error. Is this because the merge has yet to be released? Is there a way to track the release? | [
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https://github.com/huggingface/datasets/issues/4852 | Bug in multilingual_with_para config of exams dataset and checksums error | Hi @thesofakillers, yes you are right: the fix will be available after next release (it was planned for today; Monday at the latest).
In the meantime, you can use the version of the `exams` on our main branch by passing `revision` to `load_dataset`:
```python
ds = load_dataset("exams", revision="main")
``` | ## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksumError:
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/train_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/dev_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_vi_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_vi_with_para.jsonl.tar.gz']
```
CC: @thesofakillers | 49 | Bug in multilingual_with_para config of exams dataset and checksums error
## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksumError:
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/train_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/dev_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_vi_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_vi_with_para.jsonl.tar.gz']
```
CC: @thesofakillers
Hi @thesofakillers, yes you are right: the fix will be available after next release (it was planned for today; Monday at the latest).
In the meantime, you can use the version of the `exams` on our main branch by passing `revision` to `load_dataset`:
```python
ds = load_dataset("exams", revision="main")
``` | [
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https://github.com/huggingface/datasets/issues/4840 | Dataset Viewer issue for darragh/demo_data_raw3 | do you have an idea of why it can occur @huggingface/datasets? The dataset consists of a single parquet file. | ### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No | 19 | Dataset Viewer issue for darragh/demo_data_raw3
### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No
do you have an idea of why it can occur @huggingface/datasets? The dataset consists of a single parquet file. | [
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https://github.com/huggingface/datasets/issues/4840 | Dataset Viewer issue for darragh/demo_data_raw3 | Thanks for reporting @severo.
I'm not able to reproduce that error. I get instead:
```
FileNotFoundError: [Errno 2] No such file or directory: 'orix/data/ChiSig/唐合乐-9-3.jpg'
```
Which pyarrow version are you using? Mine is 6.0.1. | ### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No | 34 | Dataset Viewer issue for darragh/demo_data_raw3
### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No
Thanks for reporting @severo.
I'm not able to reproduce that error. I get instead:
```
FileNotFoundError: [Errno 2] No such file or directory: 'orix/data/ChiSig/唐合乐-9-3.jpg'
```
Which pyarrow version are you using? Mine is 6.0.1. | [
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https://github.com/huggingface/datasets/issues/4840 | Dataset Viewer issue for darragh/demo_data_raw3 | Apparently, there is something weird with that Parquet file: its schema is:
```
images: extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>>
```
I have forced a right schema:
```python
from datasets import Features, Image, load_dataset
features = Features({"images": Image()})
ds = datasets.load_dataset("parquet", split="train", data_files="train-00000-of-00001.parquet", features=features)
```
and then recreated a new Parquet file:
```python
ds.to_parquet("train.parquet")
```
Now this Parquet file has the right schema:
```
images: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
```
and can be loaded normally:
```python
In [26]: ds = load_dataset("parquet", split="train", data_files="dataset.parquet")
n [27]: ds
Out[27]:
Dataset({
features: ['images'],
num_rows: 20
})
``` | ### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No | 97 | Dataset Viewer issue for darragh/demo_data_raw3
### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No
Apparently, there is something weird with that Parquet file: its schema is:
```
images: extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>>
```
I have forced a right schema:
```python
from datasets import Features, Image, load_dataset
features = Features({"images": Image()})
ds = datasets.load_dataset("parquet", split="train", data_files="train-00000-of-00001.parquet", features=features)
```
and then recreated a new Parquet file:
```python
ds.to_parquet("train.parquet")
```
Now this Parquet file has the right schema:
```
images: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
```
and can be loaded normally:
```python
In [26]: ds = load_dataset("parquet", split="train", data_files="dataset.parquet")
n [27]: ds
Out[27]:
Dataset({
features: ['images'],
num_rows: 20
})
``` | [
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https://github.com/huggingface/datasets/issues/4829 | Misalignment between card tag validation and docs | (Note that the doc is aligned with the hub validation rules, and the "ground truth" is the hub validation rules given that they apply to all datasets, not just the canonical ones) | ## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: Union[str, List[str]]`
They should be aligned.
CC: @julien-c
| 32 | Misalignment between card tag validation and docs
## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: Union[str, List[str]]`
They should be aligned.
CC: @julien-c
(Note that the doc is aligned with the hub validation rules, and the "ground truth" is the hub validation rules given that they apply to all datasets, not just the canonical ones) | [
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https://github.com/huggingface/datasets/issues/4829 | Misalignment between card tag validation and docs | Instead of our own implementation, we now use `huggingface_hub`'s `DatasetCardData`, which has the correct type hint, so I think we can close this issue. | ## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: Union[str, List[str]]`
They should be aligned.
CC: @julien-c
| 24 | Misalignment between card tag validation and docs
## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: Union[str, List[str]]`
They should be aligned.
CC: @julien-c
Instead of our own implementation, we now use `huggingface_hub`'s `DatasetCardData`, which has the correct type hint, so I think we can close this issue. | [
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https://github.com/huggingface/datasets/issues/4804 | streaming dataset with concatenating splits raises an error | Hi! Only the name of a particular split ("train", "test", ...) is supported as a split pattern if `streaming=True`. We plan to address this limitation soon. | ## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```sh
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>()
3 # error
4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)
1 frames
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1030 splits_generator = splits_generators[split]
1031 else:
-> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
1033
1034 # Create a dataset for each of the given splits
ValueError: Bad split: train+validation. Available splits: ['validation', 'train']
```
[Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing)
## Expected results
load successfully or throws an error saying it is not supported.
## Actual results
above
## Environment info
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0 (windows11 x64)
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| 26 | streaming dataset with concatenating splits raises an error
## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```sh
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>()
3 # error
4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)
1 frames
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1030 splits_generator = splits_generators[split]
1031 else:
-> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
1033
1034 # Create a dataset for each of the given splits
ValueError: Bad split: train+validation. Available splits: ['validation', 'train']
```
[Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing)
## Expected results
load successfully or throws an error saying it is not supported.
## Actual results
above
## Environment info
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0 (windows11 x64)
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Hi! Only the name of a particular split ("train", "test", ...) is supported as a split pattern if `streaming=True`. We plan to address this limitation soon. | [
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] |
https://github.com/huggingface/datasets/issues/4804 | streaming dataset with concatenating splits raises an error | yes, same error occurs.
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-3-a6ae02d63899>](https://localhost:8080/#) in <cell line: 5>()
3 # error
4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)
1 frames
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1265 splits_generator = splits_generators[split]
1266 else:
-> 1267 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
1268
1269 # Create a dataset for each of the given splits
ValueError: Bad split: train+validation. Available splits: ['train', 'validation']
```
google colab, `datasets==2.12.0`
```
- huggingface_hub version: 0.14.1
- Platform: Linux-5.10.147+-x86_64-with-glibc2.31
- Python version: 3.10.11
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /root/.cache/huggingface/token
- Has saved token ?: False
- Configured git credential helpers:
- FastAI: 2.7.12
- Tensorflow: 2.12.0
- Torch: 2.0.0+cu118
- Jinja2: 3.1.2
- Graphviz: 0.20.1
- Pydot: 1.4.2
- Pillow: 8.4.0
- hf_transfer: N/A
- gradio: N/A
- ENDPOINT: https://huggingface.co/
- HUGGINGFACE_HUB_CACHE: /root/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /root/.cache/huggingface/assets
- HF_TOKEN_PATH: /root/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
```
| ## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```sh
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>()
3 # error
4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)
1 frames
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1030 splits_generator = splits_generators[split]
1031 else:
-> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
1033
1034 # Create a dataset for each of the given splits
ValueError: Bad split: train+validation. Available splits: ['validation', 'train']
```
[Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing)
## Expected results
load successfully or throws an error saying it is not supported.
## Actual results
above
## Environment info
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0 (windows11 x64)
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| 201 | streaming dataset with concatenating splits raises an error
## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```sh
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>()
3 # error
4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)
1 frames
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1030 splits_generator = splits_generators[split]
1031 else:
-> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
1033
1034 # Create a dataset for each of the given splits
ValueError: Bad split: train+validation. Available splits: ['validation', 'train']
```
[Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing)
## Expected results
load successfully or throws an error saying it is not supported.
## Actual results
above
## Environment info
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0 (windows11 x64)
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
yes, same error occurs.
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-3-a6ae02d63899>](https://localhost:8080/#) in <cell line: 5>()
3 # error
4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)
1 frames
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1265 splits_generator = splits_generators[split]
1266 else:
-> 1267 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
1268
1269 # Create a dataset for each of the given splits
ValueError: Bad split: train+validation. Available splits: ['train', 'validation']
```
google colab, `datasets==2.12.0`
```
- huggingface_hub version: 0.14.1
- Platform: Linux-5.10.147+-x86_64-with-glibc2.31
- Python version: 3.10.11
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /root/.cache/huggingface/token
- Has saved token ?: False
- Configured git credential helpers:
- FastAI: 2.7.12
- Tensorflow: 2.12.0
- Torch: 2.0.0+cu118
- Jinja2: 3.1.2
- Graphviz: 0.20.1
- Pydot: 1.4.2
- Pillow: 8.4.0
- hf_transfer: N/A
- gradio: N/A
- ENDPOINT: https://huggingface.co/
- HUGGINGFACE_HUB_CACHE: /root/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /root/.cache/huggingface/assets
- HF_TOKEN_PATH: /root/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
```
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https://github.com/huggingface/datasets/issues/4804 | streaming dataset with concatenating splits raises an error | Hi!, still not fixed this, the truth is that it is an important update for what we want to train the entire dataset because we want to train fast, also should be enabled the function "[train:18%]" for streaming | ## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```sh
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>()
3 # error
4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)
1 frames
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1030 splits_generator = splits_generators[split]
1031 else:
-> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
1033
1034 # Create a dataset for each of the given splits
ValueError: Bad split: train+validation. Available splits: ['validation', 'train']
```
[Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing)
## Expected results
load successfully or throws an error saying it is not supported.
## Actual results
above
## Environment info
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0 (windows11 x64)
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| 38 | streaming dataset with concatenating splits raises an error
## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```sh
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>()
3 # error
4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)
1 frames
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1030 splits_generator = splits_generators[split]
1031 else:
-> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
1033
1034 # Create a dataset for each of the given splits
ValueError: Bad split: train+validation. Available splits: ['validation', 'train']
```
[Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing)
## Expected results
load successfully or throws an error saying it is not supported.
## Actual results
above
## Environment info
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0 (windows11 x64)
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Hi!, still not fixed this, the truth is that it is an important update for what we want to train the entire dataset because we want to train fast, also should be enabled the function "[train:18%]" for streaming | [
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] |
https://github.com/huggingface/datasets/issues/4803 | Support `pipeline` argument in inspect.py functions | Now: the preview (first-rows) works, but not the conversion to parquet. See https://huggingface.co/datasets/wikipedia/viewer/20220301.de/train
```
_split_generators() missing 1 required positional argument: 'pipeline'
Error code: UnexpectedError
``` | **Is your feature request related to a problem? Please describe.**
The `wikipedia` dataset requires a `pipeline` argument to build the list of splits:
https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py#L937
But this is currently not supported in `get_dataset_config_info`:
https://github.com/huggingface/datasets/blob/main/src/datasets/inspect.py#L373-L375
which is called by other functions, e.g. `get_dataset_split_names`.
**Additional context**
The dataset viewer is not working out-of-the-box on `wikipedia` for this reason:
https://huggingface.co/datasets/wikipedia/viewer
<img width="637" alt="Capture d’écran 2022-08-08 à 12 01 16" src="https://user-images.githubusercontent.com/1676121/183461838-5330783b-0269-4ba7-a999-314cde2023d8.png">
| 25 | Support `pipeline` argument in inspect.py functions
**Is your feature request related to a problem? Please describe.**
The `wikipedia` dataset requires a `pipeline` argument to build the list of splits:
https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py#L937
But this is currently not supported in `get_dataset_config_info`:
https://github.com/huggingface/datasets/blob/main/src/datasets/inspect.py#L373-L375
which is called by other functions, e.g. `get_dataset_split_names`.
**Additional context**
The dataset viewer is not working out-of-the-box on `wikipedia` for this reason:
https://huggingface.co/datasets/wikipedia/viewer
<img width="637" alt="Capture d’écran 2022-08-08 à 12 01 16" src="https://user-images.githubusercontent.com/1676121/183461838-5330783b-0269-4ba7-a999-314cde2023d8.png">
Now: the preview (first-rows) works, but not the conversion to parquet. See https://huggingface.co/datasets/wikipedia/viewer/20220301.de/train
```
_split_generators() missing 1 required positional argument: 'pipeline'
Error code: UnexpectedError
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https://github.com/huggingface/datasets/issues/4802 | `with_format` behavior is inconsistent on different datasets | Hi! You can get a `torch.Tensor` if you do the following:
```python
raw = load_dataset("beans", split="train")
raw = raw.select(range(100))
preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans")
from datasets import Array3D
features = raw.features.copy()
features["pixel_values"] = datasets.Array3D(shape=(3, 224, 224), dtype="float32")
def preprocess_func(examples):
imgs = [img.convert("RGB") for img in examples["image"]]
return preprocessor(imgs)
data = raw.map(preprocess_func, batched=True, features=features)
print(type(data[0]["pixel_values"]))
data = data.with_format("torch", columns=["pixel_values"])
print(type(data[0]["pixel_values"]))
```
The reason for this "inconsistency" in the default case is the way PyArrow infers the type of multi-dim arrays (in this case, the `pixel_values` column). If the type is not specified manually, PyArrow assumes it is a dynamic-length sequence (it needs to know the type before writing the first batch to a cache file, and it can't be sure the array is fixed ahead of time; `ArrayXD` is our way of telling that the dims are fixed), so it already fails to convert the corresponding array to NumPy properly (you get an array of `np.object` arrays). And `with_format("torch")` replaces NumPy arrays with Torch tensors, so this bad formatting propagates. | ## Describe the bug
I found a case where `with_format` does not transform the dataset to the requested format.
## Steps to reproduce the bug
Run:
```python
from transformers import AutoTokenizer, AutoFeatureExtractor
from datasets import load_dataset
raw = load_dataset("glue", "sst2", split="train")
raw = raw.select(range(100))
tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled")
def preprocess_func(examples):
return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True)
data = raw.map(preprocess_func, batched=True)
print(type(data[0]["input_ids"]))
data = data.with_format("torch", columns=["input_ids"])
print(type(data[0]["input_ids"]))
```
printing as expected:
```python
<class 'list'>
<class 'torch.Tensor'>
```
Then run:
```python
raw = load_dataset("beans", split="train")
raw = raw.select(range(100))
preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans")
def preprocess_func(examples):
imgs = [img.convert("RGB") for img in examples["image"]]
return preprocessor(imgs)
data = raw.map(preprocess_func, batched=True)
print(type(data[0]["pixel_values"]))
data = data.with_format("torch", columns=["pixel_values"])
print(type(data[0]["pixel_values"]))
```
Printing, unexpectedly
```python
<class 'list'>
<class 'list'>
```
## Expected results
`with_format` should transform into the requested format; it's not the case.
## Actual results
`type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979
- Platform: Linux
- Python version: 3.9.12
- PyArrow version: 7.0.0
| 167 | `with_format` behavior is inconsistent on different datasets
## Describe the bug
I found a case where `with_format` does not transform the dataset to the requested format.
## Steps to reproduce the bug
Run:
```python
from transformers import AutoTokenizer, AutoFeatureExtractor
from datasets import load_dataset
raw = load_dataset("glue", "sst2", split="train")
raw = raw.select(range(100))
tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled")
def preprocess_func(examples):
return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True)
data = raw.map(preprocess_func, batched=True)
print(type(data[0]["input_ids"]))
data = data.with_format("torch", columns=["input_ids"])
print(type(data[0]["input_ids"]))
```
printing as expected:
```python
<class 'list'>
<class 'torch.Tensor'>
```
Then run:
```python
raw = load_dataset("beans", split="train")
raw = raw.select(range(100))
preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans")
def preprocess_func(examples):
imgs = [img.convert("RGB") for img in examples["image"]]
return preprocessor(imgs)
data = raw.map(preprocess_func, batched=True)
print(type(data[0]["pixel_values"]))
data = data.with_format("torch", columns=["pixel_values"])
print(type(data[0]["pixel_values"]))
```
Printing, unexpectedly
```python
<class 'list'>
<class 'list'>
```
## Expected results
`with_format` should transform into the requested format; it's not the case.
## Actual results
`type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979
- Platform: Linux
- Python version: 3.9.12
- PyArrow version: 7.0.0
Hi! You can get a `torch.Tensor` if you do the following:
```python
raw = load_dataset("beans", split="train")
raw = raw.select(range(100))
preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans")
from datasets import Array3D
features = raw.features.copy()
features["pixel_values"] = datasets.Array3D(shape=(3, 224, 224), dtype="float32")
def preprocess_func(examples):
imgs = [img.convert("RGB") for img in examples["image"]]
return preprocessor(imgs)
data = raw.map(preprocess_func, batched=True, features=features)
print(type(data[0]["pixel_values"]))
data = data.with_format("torch", columns=["pixel_values"])
print(type(data[0]["pixel_values"]))
```
The reason for this "inconsistency" in the default case is the way PyArrow infers the type of multi-dim arrays (in this case, the `pixel_values` column). If the type is not specified manually, PyArrow assumes it is a dynamic-length sequence (it needs to know the type before writing the first batch to a cache file, and it can't be sure the array is fixed ahead of time; `ArrayXD` is our way of telling that the dims are fixed), so it already fails to convert the corresponding array to NumPy properly (you get an array of `np.object` arrays). And `with_format("torch")` replaces NumPy arrays with Torch tensors, so this bad formatting propagates. | [
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https://github.com/huggingface/datasets/issues/4799 | video dataset loader/parser | Hi! We've just started discussing the video support in `datasets` (decoding backends, video feature type, etc.), so I believe we should have something tangible by the end of this year.
Also, if you have additional video features in mind that you would like to see, feel free to let us know | you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem. | 51 | video dataset loader/parser
you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem.
Hi! We've just started discussing the video support in `datasets` (decoding backends, video feature type, etc.), so I believe we should have something tangible by the end of this year.
Also, if you have additional video features in mind that you would like to see, feel free to let us know | [
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https://github.com/huggingface/datasets/issues/4799 | video dataset loader/parser | Hey @mariosasko, I was wondering if there's a way to load video data currently in the library?
Alternatively is there a way I could hack it through the dataset.from_dict() method? I tried to hack it, but the issue I run into is that earlier I was doing a `cast_column()` call for the `Image` feature, but now I'm not sure about to do if I want the dataset to have the following keys when I call from_dict on it:
`{"caption":[list of text captions], "video_frames": [list of image lists with one image list corresponding to one video]}`
Maybe something like `cast_column("video_frames", List(Image))` ..
(This is assuming I have already extracted frames from video) | you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem. | 111 | video dataset loader/parser
you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem.
Hey @mariosasko, I was wondering if there's a way to load video data currently in the library?
Alternatively is there a way I could hack it through the dataset.from_dict() method? I tried to hack it, but the issue I run into is that earlier I was doing a `cast_column()` call for the `Image` feature, but now I'm not sure about to do if I want the dataset to have the following keys when I call from_dict on it:
`{"caption":[list of text captions], "video_frames": [list of image lists with one image list corresponding to one video]}`
Maybe something like `cast_column("video_frames", List(Image))` ..
(This is assuming I have already extracted frames from video) | [
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] |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | @mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:
```
from datasets import Dataset, Features, Image, Value
import pandas as pd
import requests
import PIL
# we need to define the features ourselves
features = Features({
'a': Value(dtype='int32'),
'b': Image(),
})
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = PIL.Image.open(requests.get(url, stream=True).raw)
df = pd.DataFrame({"a": [1, 2],
"b": [image, image]})
dataset = Dataset.from_pandas(df, features=features)
```
results in
```
ArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')
```
Will the PR linked above also fix that? | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 113 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
@mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:
```
from datasets import Dataset, Features, Image, Value
import pandas as pd
import requests
import PIL
# we need to define the features ourselves
features = Features({
'a': Value(dtype='int32'),
'b': Image(),
})
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = PIL.Image.open(requests.get(url, stream=True).raw)
df = pd.DataFrame({"a": [1, 2],
"b": [image, image]})
dataset = Dataset.from_pandas(df, features=features)
```
results in
```
ArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')
```
Will the PR linked above also fix that? | [
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | I would expect this to work, but it doesn't. Shouldn't be too hard to fix tho (in a subsequent PR). | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 20 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
I would expect this to work, but it doesn't. Shouldn't be too hard to fix tho (in a subsequent PR). | [
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | Hi @mariosasko just wanted to check in if there is a PR to follow for this. I was looking to create a demo app using this. If it's not working I can just use byte encoded images in the dataset which are not displayed. | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 44 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
Hi @mariosasko just wanted to check in if there is a PR to follow for this. I was looking to create a demo app using this. If it's not working I can just use byte encoded images in the dataset which are not displayed. | [
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | I was just pointed here by @mariosasko, meanwhile I found a workaround using `encode_example` like so:
```
from datasets import load_from_disk, Dataset
DATASET_PATH = "/hf/m4-master/data/cm4/cm4-10000-v0.1"
ds1 = load_from_disk(DATASET_PATH)
ds2 = Dataset.from_dict(mapping={k: [] for k in ds1[99].keys()},
features=ds1.features
)
for i in range(2):
# could add several representative items here
row = ds1[99]
row_encoded = ds2.features.encode_example(row)
ds2 = ds2.add_item(row_encoded)
``` | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 59 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
I was just pointed here by @mariosasko, meanwhile I found a workaround using `encode_example` like so:
```
from datasets import load_from_disk, Dataset
DATASET_PATH = "/hf/m4-master/data/cm4/cm4-10000-v0.1"
ds1 = load_from_disk(DATASET_PATH)
ds2 = Dataset.from_dict(mapping={k: [] for k in ds1[99].keys()},
features=ds1.features
)
for i in range(2):
# could add several representative items here
row = ds1[99]
row_encoded = ds2.features.encode_example(row)
ds2 = ds2.add_item(row_encoded)
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | Hmm, interesting. If I create the dataset on the fly:
```
from datasets import load_from_disk, Dataset
DATASET_PATH = "/hf/m4-master/data/cm4/cm4-10000-v0.1"
ds1 = load_from_disk(DATASET_PATH)
ds2 = Dataset.from_dict(mapping={k: [v]*2 for k, v in ds1[99].items()},
features=ds1.features)
```
it doesn't fail with the error in the OP, as `from_dict` performs `encode_batch`.
However if I try to use this dataset it fails now with:
```
Traceback (most recent call last):
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 524, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/fingerprint.py", line 480, in wrapper
out = func(self, *args, **kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2775, in _map_single
batch = apply_function_on_filtered_inputs(
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2655, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2347, in decorated
result = f(decorated_item, *args, **kwargs)
File "debug_leak2.py", line 235, in split_pack_and_pad
images.append(image_transform(image.convert("RGB")))
AttributeError: 'dict' object has no attribute 'convert'
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "debug_leak2.py", line 418, in <module>
train_loader, val_loader = get_dataloaders()
File "debug_leak2.py", line 348, in get_dataloaders
dataset = dataset.map(mapper, batch_size=32, batched=True, remove_columns=dataset.column_names, num_proc=4)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2500, in map
transformed_shards[index] = async_result.get()
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py", line 771, in get
raise self._value
AttributeError: 'dict' object has no attribute 'convert'
```
but if I create that same dataset one item at a time as in the previous comment's code snippet it doesn't fail.
The features of this dataset are set to:
```
{'texts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),
'images': Sequence(feature=Image(decode=True, id=None), length=-1, id=None)}
``` | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 264 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
Hmm, interesting. If I create the dataset on the fly:
```
from datasets import load_from_disk, Dataset
DATASET_PATH = "/hf/m4-master/data/cm4/cm4-10000-v0.1"
ds1 = load_from_disk(DATASET_PATH)
ds2 = Dataset.from_dict(mapping={k: [v]*2 for k, v in ds1[99].items()},
features=ds1.features)
```
it doesn't fail with the error in the OP, as `from_dict` performs `encode_batch`.
However if I try to use this dataset it fails now with:
```
Traceback (most recent call last):
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 524, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/fingerprint.py", line 480, in wrapper
out = func(self, *args, **kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2775, in _map_single
batch = apply_function_on_filtered_inputs(
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2655, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2347, in decorated
result = f(decorated_item, *args, **kwargs)
File "debug_leak2.py", line 235, in split_pack_and_pad
images.append(image_transform(image.convert("RGB")))
AttributeError: 'dict' object has no attribute 'convert'
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "debug_leak2.py", line 418, in <module>
train_loader, val_loader = get_dataloaders()
File "debug_leak2.py", line 348, in get_dataloaders
dataset = dataset.map(mapper, batch_size=32, batched=True, remove_columns=dataset.column_names, num_proc=4)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2500, in map
transformed_shards[index] = async_result.get()
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py", line 771, in get
raise self._value
AttributeError: 'dict' object has no attribute 'convert'
```
but if I create that same dataset one item at a time as in the previous comment's code snippet it doesn't fail.
The features of this dataset are set to:
```
{'texts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),
'images': Sequence(feature=Image(decode=True, id=None), length=-1, id=None)}
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | > @mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:
>
> ```
> from datasets import Dataset, Features, Image, Value
> import pandas as pd
> import requests
> import PIL
>
> # we need to define the features ourselves
> features = Features({
> 'a': Value(dtype='int32'),
> 'b': Image(),
> })
>
> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
> image = PIL.Image.open(requests.get(url, stream=True).raw)
>
> df = pd.DataFrame({"a": [1, 2],
> "b": [image, image]})
>
> dataset = Dataset.from_pandas(df, features=features)
> ```
>
> results in
>
> ```
> ArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')
> ```
>
> Will the PR linked above also fix that?
It looks like the problem still exists.
Any news ? Any good workaround ?
Thank you | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 159 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
> @mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:
>
> ```
> from datasets import Dataset, Features, Image, Value
> import pandas as pd
> import requests
> import PIL
>
> # we need to define the features ourselves
> features = Features({
> 'a': Value(dtype='int32'),
> 'b': Image(),
> })
>
> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
> image = PIL.Image.open(requests.get(url, stream=True).raw)
>
> df = pd.DataFrame({"a": [1, 2],
> "b": [image, image]})
>
> dataset = Dataset.from_pandas(df, features=features)
> ```
>
> results in
>
> ```
> ArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')
> ```
>
> Will the PR linked above also fix that?
It looks like the problem still exists.
Any news ? Any good workaround ?
Thank you | [
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | There is a workaround:
Create a loader python scrypt and upload the dataset to huggingface.
Here is an example how to do that:
https://huggingface.co/datasets/jamescalam/image-text-demo/tree/main
and Here are videos with explanations:
https://www.youtube.com/watch?v=lqK4ocAKveE and https://www.youtube.com/watch?v=ODdKC30dT8c | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 33 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
There is a workaround:
Create a loader python scrypt and upload the dataset to huggingface.
Here is an example how to do that:
https://huggingface.co/datasets/jamescalam/image-text-demo/tree/main
and Here are videos with explanations:
https://www.youtube.com/watch?v=lqK4ocAKveE and https://www.youtube.com/watch?v=ODdKC30dT8c | [
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | > Any update on this? I'm still facing this issure. Any workaround?
I was able to resolve my issue with a quick workaround:
```
from collections import defaultdict
from datasets import Dataset
data = defaultdict(list)
for idx in tqdm(range( len(dataloader)),desc="Captioning..."):
img = dataloader[idx]
data['image'].append(img)
data['text'].append(f"{img_{idx}})
dataset = Dataset.from_dict(data)
dataset = dataset.filter(lambda example: example['image'] is not None)
dataset = dataset.filter(lambda example: example['text'] is not None)
dataset.push_to_hub(path-to-repo', private=False)
```
Hope it helps!
Happy coding | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 72 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
> Any update on this? I'm still facing this issure. Any workaround?
I was able to resolve my issue with a quick workaround:
```
from collections import defaultdict
from datasets import Dataset
data = defaultdict(list)
for idx in tqdm(range( len(dataloader)),desc="Captioning..."):
img = dataloader[idx]
data['image'].append(img)
data['text'].append(f"{img_{idx}})
dataset = Dataset.from_dict(data)
dataset = dataset.filter(lambda example: example['image'] is not None)
dataset = dataset.filter(lambda example: example['text'] is not None)
dataset.push_to_hub(path-to-repo', private=False)
```
Hope it helps!
Happy coding | [
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | > > Any update on this? I'm still facing this issure. Any workaround?
>
> I was able to resolve my issue with a quick workaround:
>
> ```
> from collections import defaultdict
> from datasets import Dataset
>
> data = defaultdict(list)
> for idx in tqdm(range( len(dataloader)),desc="Captioning..."):
> img = dataloader[idx]
> data['image'].append(img)
> data['text'].append(f"{img_{idx}})
>
> dataset = Dataset.from_dict(data)
> dataset = dataset.filter(lambda example: example['image'] is not None)
> dataset = dataset.filter(lambda example: example['text'] is not None)
>
> dataset.push_to_hub(path-to-repo', private=False)
> ```
>
> Hope it helps! Happy coding
It works!! | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 96 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
> > Any update on this? I'm still facing this issure. Any workaround?
>
> I was able to resolve my issue with a quick workaround:
>
> ```
> from collections import defaultdict
> from datasets import Dataset
>
> data = defaultdict(list)
> for idx in tqdm(range( len(dataloader)),desc="Captioning..."):
> img = dataloader[idx]
> data['image'].append(img)
> data['text'].append(f"{img_{idx}})
>
> dataset = Dataset.from_dict(data)
> dataset = dataset.filter(lambda example: example['image'] is not None)
> dataset = dataset.filter(lambda example: example['text'] is not None)
>
> dataset.push_to_hub(path-to-repo', private=False)
> ```
>
> Hope it helps! Happy coding
It works!! | [
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | I had a similar issue to @NielsRogge where I was unable to create a dataset from a Pandas DataFrame containing PIL.Images.
I found another workaround that works in this case which involves converting the DataFrame to a python dictionary, and then creating a dataset from said python dictionary.
This is a generic example of my workaround. The example assumes that you have your data in a Pandas DataFrame variable called "dataframe" plus a dictionary of your data's features in a variable called "features".
```
import datasets
dictionary = dataframe.to_dict(orient='list')
dataset = datasets.Dataset.from_dict(dictionary, features=features)
``` | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 94 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
I had a similar issue to @NielsRogge where I was unable to create a dataset from a Pandas DataFrame containing PIL.Images.
I found another workaround that works in this case which involves converting the DataFrame to a python dictionary, and then creating a dataset from said python dictionary.
This is a generic example of my workaround. The example assumes that you have your data in a Pandas DataFrame variable called "dataframe" plus a dictionary of your data's features in a variable called "features".
```
import datasets
dictionary = dataframe.to_dict(orient='list')
dataset = datasets.Dataset.from_dict(dictionary, features=features)
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | cc @mariosasko this issue has been open for 2 years, would be great to resolve it :) | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 17 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
cc @mariosasko this issue has been open for 2 years, would be great to resolve it :) | [
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https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | I have the same issue, my current workaround is saving the dataframe to a csv and then loading the dataset from the csv. Would also appreciate it a fix :) | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
| 30 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
I have the same issue, my current workaround is saving the dataframe to a csv and then loading the dataset from the csv. Would also appreciate it a fix :) | [
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https://github.com/huggingface/datasets/issues/4795 | Missing MBPP splits | Thanks for reporting this as well, @stadlerb.
I suggest waiting for the answer of the data owners... | (@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**.
If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected.
The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples:
> We specify a train and test split to use for evaluation. Specifically:
>
> * Task IDs 11-510 are used for evaluation.
> * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples.
I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974.
Regarding the "sanitized" split the paper states the following:
> For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning.
The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split. | 17 | Missing MBPP splits
(@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**.
If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected.
The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples:
> We specify a train and test split to use for evaluation. Specifically:
>
> * Task IDs 11-510 are used for evaluation.
> * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples.
I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974.
Regarding the "sanitized" split the paper states the following:
> For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning.
The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split.
Thanks for reporting this as well, @stadlerb.
I suggest waiting for the answer of the data owners... | [
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https://github.com/huggingface/datasets/issues/4795 | Missing MBPP splits | @albertvillanova The first author of the paper responded to the upstream issue:
> Task IDs 11-510 are the 500 test problems. We use 90 problems (511-600) for validation and then remaining 374 for fine-tuning (601-974). The other problems can be used as desired, either for training or few-shot prompting (although this should be specified). | (@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**.
If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected.
The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples:
> We specify a train and test split to use for evaluation. Specifically:
>
> * Task IDs 11-510 are used for evaluation.
> * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples.
I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974.
Regarding the "sanitized" split the paper states the following:
> For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning.
The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split. | 54 | Missing MBPP splits
(@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**.
If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected.
The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples:
> We specify a train and test split to use for evaluation. Specifically:
>
> * Task IDs 11-510 are used for evaluation.
> * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples.
I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974.
Regarding the "sanitized" split the paper states the following:
> For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning.
The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split.
@albertvillanova The first author of the paper responded to the upstream issue:
> Task IDs 11-510 are the 500 test problems. We use 90 problems (511-600) for validation and then remaining 374 for fine-tuning (601-974). The other problems can be used as desired, either for training or few-shot prompting (although this should be specified). | [
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https://github.com/huggingface/datasets/issues/4795 | Missing MBPP splits | Thanks for the follow-up, @stadlerb.
Would you be willing to open a Pull Request to address this issue? :wink: | (@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**.
If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected.
The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples:
> We specify a train and test split to use for evaluation. Specifically:
>
> * Task IDs 11-510 are used for evaluation.
> * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples.
I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974.
Regarding the "sanitized" split the paper states the following:
> For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning.
The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split. | 19 | Missing MBPP splits
(@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**.
If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected.
The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples:
> We specify a train and test split to use for evaluation. Specifically:
>
> * Task IDs 11-510 are used for evaluation.
> * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples.
I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974.
Regarding the "sanitized" split the paper states the following:
> For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning.
The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split.
Thanks for the follow-up, @stadlerb.
Would you be willing to open a Pull Request to address this issue? :wink: | [
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https://github.com/huggingface/datasets/issues/4792 | Add DocVQA | Thanks for proposing, @NielsRogge.
Please, note this dataset requires registering in their website and their Terms and Conditions state we cannot distribute their URL:
```
1. You will NOT distribute the download URLs
...
``` | ## Adding a Dataset
- **Name:** DocVQA
- **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this information.
- **Paper:** https://arxiv.org/abs/2007.00398
- **Data:** https://www.docvqa.org/datasets/docvqa
- **Motivation:** Models like LayoutLM and Donut in the Transformers library are fine-tuned on DocVQA. Would be very handy to directly load this dataset from the hub.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
| 35 | Add DocVQA
## Adding a Dataset
- **Name:** DocVQA
- **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this information.
- **Paper:** https://arxiv.org/abs/2007.00398
- **Data:** https://www.docvqa.org/datasets/docvqa
- **Motivation:** Models like LayoutLM and Donut in the Transformers library are fine-tuned on DocVQA. Would be very handy to directly load this dataset from the hub.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
Thanks for proposing, @NielsRogge.
Please, note this dataset requires registering in their website and their Terms and Conditions state we cannot distribute their URL:
```
1. You will NOT distribute the download URLs
...
``` | [
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https://github.com/huggingface/datasets/issues/4791 | Dataset Viewer issue for Team-PIXEL/rendered-wikipedia-english | Thanks for reporting. It's a known issue that should be fixed soon. Meanwhile, I had to manually trigger the dataset viewer. It's OK now.
Note that the extreme aspect ratio of the images generates another issue, that we're inspecting. | ### Link
https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train
### Description
The dataset can be loaded fine but the viewer shows this error:
```
Server Error
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
I'm guessing this is because I recently renamed the dataset. Based on related issues (e.g. https://github.com/huggingface/datasets/issues/4759) , is there something server-side that needs to be refreshed?
### Owner
Yes | 39 | Dataset Viewer issue for Team-PIXEL/rendered-wikipedia-english
### Link
https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train
### Description
The dataset can be loaded fine but the viewer shows this error:
```
Server Error
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
I'm guessing this is because I recently renamed the dataset. Based on related issues (e.g. https://github.com/huggingface/datasets/issues/4759) , is there something server-side that needs to be refreshed?
### Owner
Yes
Thanks for reporting. It's a known issue that should be fixed soon. Meanwhile, I had to manually trigger the dataset viewer. It's OK now.
Note that the extreme aspect ratio of the images generates another issue, that we're inspecting. | [
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https://github.com/huggingface/datasets/issues/4784 | Add Multiface dataset | Hey @nandwalritik! Thanks for offering to help!
This dataset might be somewhat complex and I'm concerned about it being 65 TB, which would be quite expensive to host. @lhoestq @mariosasko I would love your input if you think it's worth adding this dataset. | ## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps
- **Data:** https://github.com/facebookresearch/multiface
The whole dataset is 65TB though, so I'm not sure
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
| 43 | Add Multiface dataset
## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps
- **Data:** https://github.com/facebookresearch/multiface
The whole dataset is 65TB though, so I'm not sure
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
Hey @nandwalritik! Thanks for offering to help!
This dataset might be somewhat complex and I'm concerned about it being 65 TB, which would be quite expensive to host. @lhoestq @mariosasko I would love your input if you think it's worth adding this dataset. | [
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https://github.com/huggingface/datasets/issues/4784 | Add Multiface dataset | Thanks for proposing this interesting dataset, @osanseviero.
Please note that the data files are already hosted in a third-party server: e.g. the index of data files for entity "6795937" is at https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/index.html
- audio.tar: https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/audio.tar
- ...
Therefore, in principle, we don't need to host them on our Hub: it would be enough to just implement a loading script in the corresponding Hub dataset repo, e.g. "facebook/multiface"... | ## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps
- **Data:** https://github.com/facebookresearch/multiface
The whole dataset is 65TB though, so I'm not sure
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
| 67 | Add Multiface dataset
## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps
- **Data:** https://github.com/facebookresearch/multiface
The whole dataset is 65TB though, so I'm not sure
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
Thanks for proposing this interesting dataset, @osanseviero.
Please note that the data files are already hosted in a third-party server: e.g. the index of data files for entity "6795937" is at https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/index.html
- audio.tar: https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/audio.tar
- ...
Therefore, in principle, we don't need to host them on our Hub: it would be enough to just implement a loading script in the corresponding Hub dataset repo, e.g. "facebook/multiface"... | [
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] |
https://github.com/huggingface/datasets/issues/4782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | Thanks for reporting @conceptofmind.
Could you please give details about your environment?
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
``` | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
``` | 42 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
```
Thanks for reporting @conceptofmind.
Could you please give details about your environment?
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
``` | [
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https://github.com/huggingface/datasets/issues/4782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | Hi @albertvillanova ,
Here is the environment information:
```
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.27
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
```
Thanks,
Enrico | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
``` | 31 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
```
Hi @albertvillanova ,
Here is the environment information:
```
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.27
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
```
Thanks,
Enrico | [
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https://github.com/huggingface/datasets/issues/4782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | I think this issue is solved here https://discuss.huggingface.co/t/minhash-deduplication/19992/12?u=loubnabnl, this only happens for very large datasets we will update it in CodeParrot code | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
``` | 22 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
```
I think this issue is solved here https://discuss.huggingface.co/t/minhash-deduplication/19992/12?u=loubnabnl, this only happens for very large datasets we will update it in CodeParrot code | [
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https://github.com/huggingface/datasets/issues/4782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | Hi @loubnabnl,
Yes, the issue is solved in the discussion thread.
I will close this issue.
Thank you again for all of your help.
Enrico | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
``` | 25 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
```
Hi @loubnabnl,
Yes, the issue is solved in the discussion thread.
I will close this issue.
Thank you again for all of your help.
Enrico | [
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https://github.com/huggingface/datasets/issues/4776 | RuntimeError when using torchaudio 0.12.0 to load MP3 audio file | Requiring torchaudio<0.12.0 isn't really a viable solution because that implies torch<0.12.0 which means no sm_86 CUDA support which means no RTX 3090 support in PyTorch.
But in my case, the error only occurs if `_fallback_load` resolves to `_fail_load` inside torchaudio 0.12.0 which is only the case if FFMPEG initialization failed: https://github.com/pytorch/audio/blob/b1f510fa5681e92ee82bdc6b2d1ed896799fc32c/torchaudio/backend/sox_io_backend.py#L36-L47
That means the proper solution for torchaudio>=0.12.0 is to check `torchaudio._extension._FFMPEG_INITIALIZED` and if it is False, then we need to remind the user to install a dynamically linked ffmpeg 4.1.8 and then maybe call `torchaudio._extension._init_ffmpeg()` to force a user-visible exception showing the missing ffmpeg dynamic library name.
On my system, installing
- libavcodec.so.58
- libavdevice.so.58
- libavfilter.so.7
- libavformat.so.58
- libavutil.so.56
- libswresample.so.3
- libswscale.so.5
from ffmpeg 4.1.8 made HF datasets 2.3.2 work just fine with torchaudio 0.12.1+cu116:
```python3
import sox, torchaudio, datasets
print('torchaudio', torchaudio.__version__)
print('datasets', datasets.__version__)
torchaudio._extension._init_ffmpeg()
print(torchaudio._extension._FFMPEG_INITIALIZED)
waveform, sample_rate = torchaudio.load('/workspace/.cache/huggingface/datasets/downloads/extracted/8e5aa88585efa2a4c74c6664b576550d32b7ff9c3d1d17cc04f44f11338c3dc6/cv-corpus-8.0-2022-01-19/en/clips/common_voice_en_100038.mp3', format='mp3')
print(waveform.shape)
```
```
torchaudio 0.12.1+cu116
datasets 2.3.2
True
torch.Size([1, 369792])
``` | Current version of `torchaudio` (0.12.0) raises a RuntimeError when trying to use `sox_io` backend but non-Python dependency `sox` is not installed:
https://github.com/pytorch/audio/blob/2e1388401c434011e9f044b40bc8374f2ddfc414/torchaudio/backend/sox_io_backend.py#L21-L29
```python
def _fail_load(
filepath: str,
frame_offset: int = 0,
num_frames: int = -1,
normalize: bool = True,
channels_first: bool = True,
format: Optional[str] = None,
) -> Tuple[torch.Tensor, int]:
raise RuntimeError("Failed to load audio from {}".format(filepath))
```
Maybe we should raise a more actionable error message so that the user knows how to fix it.
UPDATE:
- this is an incompatibility of latest torchaudio (0.12.0) and the sox backend
TODO:
- [x] as a temporary solution, we should recommend installing torchaudio<0.12.0
- #4777
- #4785
- [ ] however, a stable solution must be found for torchaudio>=0.12.0
Related to:
- https://github.com/huggingface/transformers/issues/18379 | 156 | RuntimeError when using torchaudio 0.12.0 to load MP3 audio file
Current version of `torchaudio` (0.12.0) raises a RuntimeError when trying to use `sox_io` backend but non-Python dependency `sox` is not installed:
https://github.com/pytorch/audio/blob/2e1388401c434011e9f044b40bc8374f2ddfc414/torchaudio/backend/sox_io_backend.py#L21-L29
```python
def _fail_load(
filepath: str,
frame_offset: int = 0,
num_frames: int = -1,
normalize: bool = True,
channels_first: bool = True,
format: Optional[str] = None,
) -> Tuple[torch.Tensor, int]:
raise RuntimeError("Failed to load audio from {}".format(filepath))
```
Maybe we should raise a more actionable error message so that the user knows how to fix it.
UPDATE:
- this is an incompatibility of latest torchaudio (0.12.0) and the sox backend
TODO:
- [x] as a temporary solution, we should recommend installing torchaudio<0.12.0
- #4777
- #4785
- [ ] however, a stable solution must be found for torchaudio>=0.12.0
Related to:
- https://github.com/huggingface/transformers/issues/18379
Requiring torchaudio<0.12.0 isn't really a viable solution because that implies torch<0.12.0 which means no sm_86 CUDA support which means no RTX 3090 support in PyTorch.
But in my case, the error only occurs if `_fallback_load` resolves to `_fail_load` inside torchaudio 0.12.0 which is only the case if FFMPEG initialization failed: https://github.com/pytorch/audio/blob/b1f510fa5681e92ee82bdc6b2d1ed896799fc32c/torchaudio/backend/sox_io_backend.py#L36-L47
That means the proper solution for torchaudio>=0.12.0 is to check `torchaudio._extension._FFMPEG_INITIALIZED` and if it is False, then we need to remind the user to install a dynamically linked ffmpeg 4.1.8 and then maybe call `torchaudio._extension._init_ffmpeg()` to force a user-visible exception showing the missing ffmpeg dynamic library name.
On my system, installing
- libavcodec.so.58
- libavdevice.so.58
- libavfilter.so.7
- libavformat.so.58
- libavutil.so.56
- libswresample.so.3
- libswscale.so.5
from ffmpeg 4.1.8 made HF datasets 2.3.2 work just fine with torchaudio 0.12.1+cu116:
```python3
import sox, torchaudio, datasets
print('torchaudio', torchaudio.__version__)
print('datasets', datasets.__version__)
torchaudio._extension._init_ffmpeg()
print(torchaudio._extension._FFMPEG_INITIALIZED)
waveform, sample_rate = torchaudio.load('/workspace/.cache/huggingface/datasets/downloads/extracted/8e5aa88585efa2a4c74c6664b576550d32b7ff9c3d1d17cc04f44f11338c3dc6/cv-corpus-8.0-2022-01-19/en/clips/common_voice_en_100038.mp3', format='mp3')
print(waveform.shape)
```
```
torchaudio 0.12.1+cu116
datasets 2.3.2
True
torch.Size([1, 369792])
``` | [
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] |
https://github.com/huggingface/datasets/issues/4775 | Streaming not supported in Theivaprakasham/wildreceipt | Thanks for reporting @NitishkKarra.
The root source of the issue is that streaming mode is not supported out-of-the-box for that dataset, because it contains a TAR file.
We have opened a discussion in the corresponding Hub dataset page, pointing out this issue: https://huggingface.co/datasets/Theivaprakasham/wildreceipt/discussions/1
I'm closing this issue here, so this discussion is transferred there instead. | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | 55 | Streaming not supported in Theivaprakasham/wildreceipt
### Link
_No response_
### Description
_No response_
### Owner
_No response_
Thanks for reporting @NitishkKarra.
The root source of the issue is that streaming mode is not supported out-of-the-box for that dataset, because it contains a TAR file.
We have opened a discussion in the corresponding Hub dataset page, pointing out this issue: https://huggingface.co/datasets/Theivaprakasham/wildreceipt/discussions/1
I'm closing this issue here, so this discussion is transferred there instead. | [
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https://github.com/huggingface/datasets/issues/4772 | AssertionError when using label_cols in to_tf_dataset | Hi @lehrig, this is caused by the data collator renaming "label" to "labels". If you set `label_cols=["labels"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames columns. | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
| 53 | AssertionError when using label_cols in to_tf_dataset
## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
Hi @lehrig, this is caused by the data collator renaming "label" to "labels". If you set `label_cols=["labels"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames columns. | [
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https://github.com/huggingface/datasets/issues/4772 | AssertionError when using label_cols in to_tf_dataset | Thanks - and wow, that appears like a strange side-effect of the data collator. Is that really needed?
Why not make it more explicit? For example, extend `DefaultDataCollator` with an optional property `label_col_name` to be used as label column; only when it is not provided default to `labels` (and document that this happens) for backwards-compatibility? | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
| 55 | AssertionError when using label_cols in to_tf_dataset
## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
Thanks - and wow, that appears like a strange side-effect of the data collator. Is that really needed?
Why not make it more explicit? For example, extend `DefaultDataCollator` with an optional property `label_col_name` to be used as label column; only when it is not provided default to `labels` (and document that this happens) for backwards-compatibility? | [
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https://github.com/huggingface/datasets/issues/4772 | AssertionError when using label_cols in to_tf_dataset | Haha, I honestly have no idea why our data collators rename `"label"` (the standard label column name in our datasets) to `"labels"` (the standard label column name input to our models). It's been a pain point when I design TF data pipelines, though, because I don't want to hardcode things like that - especially in `datasets`, because the renaming is something that happens purely at the `transformers` end. I don't think I could make the change in the data collators themselves at this point, because it would break backward compatibility for everything in PyTorch as well as TF.
In the most recent version of `transformers` we added a [prepare_tf_dataset](https://huggingface.co/docs/transformers/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset) method to our models which takes care of these details for you, and even chooses appropriate columns and labels for the model you're using. In future we might make that the officially recommended way to convert HF datasets to `tf.data.Dataset`. | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
| 149 | AssertionError when using label_cols in to_tf_dataset
## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
Haha, I honestly have no idea why our data collators rename `"label"` (the standard label column name in our datasets) to `"labels"` (the standard label column name input to our models). It's been a pain point when I design TF data pipelines, though, because I don't want to hardcode things like that - especially in `datasets`, because the renaming is something that happens purely at the `transformers` end. I don't think I could make the change in the data collators themselves at this point, because it would break backward compatibility for everything in PyTorch as well as TF.
In the most recent version of `transformers` we added a [prepare_tf_dataset](https://huggingface.co/docs/transformers/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset) method to our models which takes care of these details for you, and even chooses appropriate columns and labels for the model you're using. In future we might make that the officially recommended way to convert HF datasets to `tf.data.Dataset`. | [
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] |
https://github.com/huggingface/datasets/issues/4772 | AssertionError when using label_cols in to_tf_dataset | Interesting, that'd be great especially for clarity. https://huggingface.co/docs/datasets/use_with_tensorflow#data-loading already improved clarity, yet, all those options will still confuse people. Looking forward to those advances in the hope there'll be only 1 way in the future ;)
Anyways, I am happy for the time being with the work-around you provided. Thank you! | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
| 51 | AssertionError when using label_cols in to_tf_dataset
## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
Interesting, that'd be great especially for clarity. https://huggingface.co/docs/datasets/use_with_tensorflow#data-loading already improved clarity, yet, all those options will still confuse people. Looking forward to those advances in the hope there'll be only 1 way in the future ;)
Anyways, I am happy for the time being with the work-around you provided. Thank you! | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`. | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 16 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`. | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs
> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.
That's unexpected, can you share the code you're running ? | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 41 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs
> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.
That's unexpected, can you share the code you're running ? | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | here is the code snippet
```python
# add faiss index
source_dataset = load_dataset(source_path)
queries = load_dataset(query_path)
gpu = [0,1,2,3]
source_dataset.add_faiss_index(
"embedding",
device=gpu,
)
# batch query
batch_size = 32
for i in tqdm(range(0, len(queries), batch_size)):
if i + batch_size >= len(queries):
batched_queries = queries[i:]
else:
batched_queries = queries[i:i+batch_size]
batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)
scores, candidates = source_dataset.get_nearest_examples_batch(
"embedding",
batched_query_embeddings,
k=5
)
``` | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 65 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
here is the code snippet
```python
# add faiss index
source_dataset = load_dataset(source_path)
queries = load_dataset(query_path)
gpu = [0,1,2,3]
source_dataset.add_faiss_index(
"embedding",
device=gpu,
)
# batch query
batch_size = 32
for i in tqdm(range(0, len(queries), batch_size)):
if i + batch_size >= len(queries):
batched_queries = queries[i:]
else:
batched_queries = queries[i:i+batch_size]
batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)
scores, candidates = source_dataset.get_nearest_examples_batch(
"embedding",
batched_query_embeddings,
k=5
)
``` | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | The code looks all good to me, do you see all the GPUs being utilized ? What version of faiss are you using ? | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 24 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
The code looks all good to me, do you see all the GPUs being utilized ? What version of faiss are you using ? | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | I can see the memory usage of all the GPUs.
My version of `faiss-gpu` is `1.7.2` | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 16 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
I can see the memory usage of all the GPUs.
My version of `faiss-gpu` is `1.7.2` | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | It looks all good to me then ^^ though you said you didn't experienced speed improvements by adding more GPUs ? What size is your source dataset and what time differences did you experience ? | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 35 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
It looks all good to me then ^^ though you said you didn't experienced speed improvements by adding more GPUs ? What size is your source dataset and what time differences did you experience ? | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | query set: 1e6
source dataset: 1e6
embedding size: 768
index: Flat
topk: 20
GPU: V100
The time taken to traverse the query set once is about 1.5h, which is almost not influenced by the value of query batch size or the number of GPUs according to my experiments. | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 48 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
query set: 1e6
source dataset: 1e6
embedding size: 768
index: Flat
topk: 20
GPU: V100
The time taken to traverse the query set once is about 1.5h, which is almost not influenced by the value of query batch size or the number of GPUs according to my experiments. | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hmmm the number of GPUs should divide the time, something is going wrong. Can you check that adding more GPU does divide the memory used per GPU ? Maybe it can be worth looking at similar issues in the FAISS repository or create a noew issue over there to understand what's going on | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 53 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
Hmmm the number of GPUs should divide the time, something is going wrong. Can you check that adding more GPU does divide the memory used per GPU ? Maybe it can be worth looking at similar issues in the FAISS repository or create a noew issue over there to understand what's going on | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | > Can you check that adding more GPU does divide the memory used per GPU
The memory used per GPU is unchanged while adding more GPU. Is this unexpected?
I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆 | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 51 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
> Can you check that adding more GPU does divide the memory used per GPU
The memory used per GPU is unchanged while adding more GPU. Is this unexpected?
I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆 | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | > I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆
Oh indeed that's possible, I wasn't sure. Anyway you can check that calling get_nearest_examples_batch simply calls search under the hood:
https://github.com/huggingface/datasets/blob/f90f71fbbb33889fe75a3ffc101cdf16a88a3453/src/datasets/search.py#L375 | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 44 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
> I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆
Oh indeed that's possible, I wasn't sure. Anyway you can check that calling get_nearest_examples_batch simply calls search under the hood:
https://github.com/huggingface/datasets/blob/f90f71fbbb33889fe75a3ffc101cdf16a88a3453/src/datasets/search.py#L375 | [
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] |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Here is a runnable script.
Multi-GPU searching still does not work in my experiments.
```python
import os
from tqdm import tqdm
import numpy as np
import datasets
from datasets import Dataset
class DPRSelector:
def __init__(self, source, target, index_name, gpu=None):
self.source = source
self.target = target
self.index_name = index_name
cache_path = 'embedding.faiss'
if not os.path.exists(cache_path):
self.source.add_faiss_index(
column="embedding",
index_name=index_name,
device=gpu,
)
self.source.save_faiss_index(index_name, cache_path)
else:
self.source.load_faiss_index(
index_name,
cache_path,
device=gpu
)
print('index builded!')
def build_dataset(self, top_k, batch_size):
print('start search')
for i in tqdm(range(0, len(self.target), batch_size)):
if i + batch_size >= len(self.target):
batched_queries = self.target[i:]
else:
batched_queries = self.target[i:i+batch_size]
batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)
search_res = self.source.get_nearest_examples_batch(
self.index_name,
batched_query_embeddings,
k=top_k
)
print('finish search')
def get_pseudo_dataset():
pseudo_dict = {"embedding": np.zeros((1000000, 768), dtype=np.float32)}
print('generate pseudo data')
dataset = Dataset.from_dict(pseudo_dict)
def list_to_array(data):
return {"embedding": [np.array(vector, dtype=np.float32) for vector in data["embedding"]]}
dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)
print('build dataset')
return dataset
if __name__=="__main__":
np.random.seed(42)
source_dataset = get_pseudo_dataset()
target_dataset = get_pseudo_dataset()
gpu = [0,1,2,3,4,5,6,7]
selector = DPRSelector(source_dataset, target_dataset, "embedding", gpu=gpu)
selector.build_dataset(top_k=20, batch_size=32)
``` | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 163 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
Here is a runnable script.
Multi-GPU searching still does not work in my experiments.
```python
import os
from tqdm import tqdm
import numpy as np
import datasets
from datasets import Dataset
class DPRSelector:
def __init__(self, source, target, index_name, gpu=None):
self.source = source
self.target = target
self.index_name = index_name
cache_path = 'embedding.faiss'
if not os.path.exists(cache_path):
self.source.add_faiss_index(
column="embedding",
index_name=index_name,
device=gpu,
)
self.source.save_faiss_index(index_name, cache_path)
else:
self.source.load_faiss_index(
index_name,
cache_path,
device=gpu
)
print('index builded!')
def build_dataset(self, top_k, batch_size):
print('start search')
for i in tqdm(range(0, len(self.target), batch_size)):
if i + batch_size >= len(self.target):
batched_queries = self.target[i:]
else:
batched_queries = self.target[i:i+batch_size]
batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)
search_res = self.source.get_nearest_examples_batch(
self.index_name,
batched_query_embeddings,
k=top_k
)
print('finish search')
def get_pseudo_dataset():
pseudo_dict = {"embedding": np.zeros((1000000, 768), dtype=np.float32)}
print('generate pseudo data')
dataset = Dataset.from_dict(pseudo_dict)
def list_to_array(data):
return {"embedding": [np.array(vector, dtype=np.float32) for vector in data["embedding"]]}
dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)
print('build dataset')
return dataset
if __name__=="__main__":
np.random.seed(42)
source_dataset = get_pseudo_dataset()
target_dataset = get_pseudo_dataset()
gpu = [0,1,2,3,4,5,6,7]
selector = DPRSelector(source_dataset, target_dataset, "embedding", gpu=gpu)
selector.build_dataset(top_k=20, batch_size=32)
``` | [
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] |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Maybe @albertvillanova you can take a look ? I won't be available in the following days | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 16 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
Maybe @albertvillanova you can take a look ? I won't be available in the following days | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hi @xwwwwww I'm investigating it, but I'm not an expert in Faiss. In principle, it is weird that your code does not work properly because it seems right... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 28 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
Hi @xwwwwww I'm investigating it, but I'm not an expert in Faiss. In principle, it is weird that your code does not work properly because it seems right... | [
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] |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | > Have you tried passing `gpu=-1` and check if there is a speedup?
yes, there is a speed up using GPU compared with CPU. | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 24 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
> Have you tried passing `gpu=-1` and check if there is a speedup?
yes, there is a speed up using GPU compared with CPU. | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | When passing `device=-1`, ALL existing GPUs are used (multi GPU): this is the maximum speedup you can get. To know the number of total GPUs:
```
import faiss
ngpus = faiss.get_num_gpus()
print(ngpus)
```
When passing a list of integers to `device`, then only that number of GPUs are used (multi GPU as well)
- the speedup should be proportional (more or less) to the ratio of the number of elements passed to `device` over `ngpus`
- if this is not the case, then there is an issue in the implementation of this use case (however, I have reviewed the code and in principle I can't find any evident bug)
When passing a positive integer to `device`, then only a single GPU is used.
- this time should be more or less proportional to the time when passing `device=-1` over `ngpus` | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 140 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
When passing `device=-1`, ALL existing GPUs are used (multi GPU): this is the maximum speedup you can get. To know the number of total GPUs:
```
import faiss
ngpus = faiss.get_num_gpus()
print(ngpus)
```
When passing a list of integers to `device`, then only that number of GPUs are used (multi GPU as well)
- the speedup should be proportional (more or less) to the ratio of the number of elements passed to `device` over `ngpus`
- if this is not the case, then there is an issue in the implementation of this use case (however, I have reviewed the code and in principle I can't find any evident bug)
When passing a positive integer to `device`, then only a single GPU is used.
- this time should be more or less proportional to the time when passing `device=-1` over `ngpus` | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Thanks for your help!
Have you run the code and replicated the same experimental results (i.e., no speedup while increasing the number of GPUs)? | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 24 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
Thanks for your help!
Have you run the code and replicated the same experimental results (i.e., no speedup while increasing the number of GPUs)? | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | I can confirm `add_faiss_index` calls `index = faiss.index_cpu_to_gpus_list(index, gpus=list(device))`.
Could this be an issue with your environment ? Could you try running with 1 and 8 GPUs with a code similar to[ this one from the FAISS examples](https://github.com/facebookresearch/faiss/blob/main/tutorial/python/5-Multiple-GPUs.py) but using `gpu_index = faiss.index_cpu_to_gpus_list(cpu_index, gpus=list(device))`, and see if the speed changes ? | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 51 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
I can confirm `add_faiss_index` calls `index = faiss.index_cpu_to_gpus_list(index, gpus=list(device))`.
Could this be an issue with your environment ? Could you try running with 1 and 8 GPUs with a code similar to[ this one from the FAISS examples](https://github.com/facebookresearch/faiss/blob/main/tutorial/python/5-Multiple-GPUs.py) but using `gpu_index = faiss.index_cpu_to_gpus_list(cpu_index, gpus=list(device))`, and see if the speed changes ? | [
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hi, I test the FAISS example and the speed indeed changes. I set `nb=1000000`, `nq=1000000` and `d=64`
| num GPUS | time cost |
| -------- | --------- |
| 1 | 28.53 |
| 5 | 7.16 |
| While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 39 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
Hi, I test the FAISS example and the speed indeed changes. I set `nb=1000000`, `nq=1000000` and `d=64`
| num GPUS | time cost |
| -------- | --------- |
| 1 | 28.53 |
| 5 | 7.16 |
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https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Ok the benchmark is great, not sure why it doesn't speed up the index in your case though. You can try running the benchmark with the same settings as your actual dataset
```
query set: 1e6
source dataset: 1e6
embedding size: 768
index: Flat
topk: 20
GPU: V100
```
Note that you can still pass a FAISS index you built yourself to a dataset using https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index_from_external_arrays | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 66 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
Ok the benchmark is great, not sure why it doesn't speed up the index in your case though. You can try running the benchmark with the same settings as your actual dataset
```
query set: 1e6
source dataset: 1e6
embedding size: 768
index: Flat
topk: 20
GPU: V100
```
Note that you can still pass a FAISS index you built yourself to a dataset using https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index_from_external_arrays | [
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] |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | > Here is a runnable script. Multi-GPU searching still does not work in my experiments.
>
> ```python
> import os
> from tqdm import tqdm
> import numpy as np
> import datasets
> from datasets import Dataset
>
> class DPRSelector:
>
> def __init__(self, source, target, index_name, gpu=None):
> self.source = source
> self.target = target
> self.index_name = index_name
>
> cache_path = 'embedding.faiss'
>
> if not os.path.exists(cache_path):
> self.source.add_faiss_index(
> column="embedding",
> index_name=index_name,
> device=gpu,
> )
> self.source.save_faiss_index(index_name, cache_path)
> else:
> self.source.load_faiss_index(
> index_name,
> cache_path,
> device=gpu
> )
> print('index builded!')
>
> def build_dataset(self, top_k, batch_size):
> print('start search')
>
> for i in tqdm(range(0, len(self.target), batch_size)):
> if i + batch_size >= len(self.target):
> batched_queries = self.target[i:]
> else:
> batched_queries = self.target[i:i+batch_size]
>
>
> batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)
> search_res = self.source.get_nearest_examples_batch(
> self.index_name,
> batched_query_embeddings,
> k=top_k
> )
>
> print('finish search')
>
>
> def get_pseudo_dataset():
> pseudo_dict = {"embedding": np.zeros((1000000, 768), dtype=np.float32)}
> print('generate pseudo data')
>
> dataset = Dataset.from_dict(pseudo_dict)
> def list_to_array(data):
> return {"embedding": [np.array(vector, dtype=np.float32) for vector in data["embedding"]]}
> dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)
>
> print('build dataset')
> return dataset
>
>
>
> if __name__=="__main__":
>
> np.random.seed(42)
>
>
> source_dataset = get_pseudo_dataset()
> target_dataset = get_pseudo_dataset()
>
> gpu = [0,1,2,3,4,5,6,7]
> selector = DPRSelector(source_dataset, target_dataset, "embedding", gpu=gpu)
>
> selector.build_dataset(top_k=20, batch_size=32)
> ```
By the way, have you run this toy example and replicated my experiment results? I think it is a more direct way to figure this out :) | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 270 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
> Here is a runnable script. Multi-GPU searching still does not work in my experiments.
>
> ```python
> import os
> from tqdm import tqdm
> import numpy as np
> import datasets
> from datasets import Dataset
>
> class DPRSelector:
>
> def __init__(self, source, target, index_name, gpu=None):
> self.source = source
> self.target = target
> self.index_name = index_name
>
> cache_path = 'embedding.faiss'
>
> if not os.path.exists(cache_path):
> self.source.add_faiss_index(
> column="embedding",
> index_name=index_name,
> device=gpu,
> )
> self.source.save_faiss_index(index_name, cache_path)
> else:
> self.source.load_faiss_index(
> index_name,
> cache_path,
> device=gpu
> )
> print('index builded!')
>
> def build_dataset(self, top_k, batch_size):
> print('start search')
>
> for i in tqdm(range(0, len(self.target), batch_size)):
> if i + batch_size >= len(self.target):
> batched_queries = self.target[i:]
> else:
> batched_queries = self.target[i:i+batch_size]
>
>
> batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)
> search_res = self.source.get_nearest_examples_batch(
> self.index_name,
> batched_query_embeddings,
> k=top_k
> )
>
> print('finish search')
>
>
> def get_pseudo_dataset():
> pseudo_dict = {"embedding": np.zeros((1000000, 768), dtype=np.float32)}
> print('generate pseudo data')
>
> dataset = Dataset.from_dict(pseudo_dict)
> def list_to_array(data):
> return {"embedding": [np.array(vector, dtype=np.float32) for vector in data["embedding"]]}
> dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)
>
> print('build dataset')
> return dataset
>
>
>
> if __name__=="__main__":
>
> np.random.seed(42)
>
>
> source_dataset = get_pseudo_dataset()
> target_dataset = get_pseudo_dataset()
>
> gpu = [0,1,2,3,4,5,6,7]
> selector = DPRSelector(source_dataset, target_dataset, "embedding", gpu=gpu)
>
> selector.build_dataset(top_k=20, batch_size=32)
> ```
By the way, have you run this toy example and replicated my experiment results? I think it is a more direct way to figure this out :) | [
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] |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hi,
I have a similar question and would like to know if there's any progress in this issue.
`dataset.add_faiss_index(column="embedding")`, this takes around 5minutes to add the index.
`dataset.add_faiss_index(column="embedding", device=-1)`, this ran for more than 10minutes and still didn't complete execution.
Now, I don't understand why that's the case as I expected for GPU the indexing should be faster | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 58 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
Hi,
I have a similar question and would like to know if there's any progress in this issue.
`dataset.add_faiss_index(column="embedding")`, this takes around 5minutes to add the index.
`dataset.add_faiss_index(column="embedding", device=-1)`, this ran for more than 10minutes and still didn't complete execution.
Now, I don't understand why that's the case as I expected for GPU the indexing should be faster | [
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https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | Hi @SaulLu, thanks for reporting.
I think offline mode is not supported for datasets containing only data files (without any loading script). I'm having a look into this... | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
| 28 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
Hi @SaulLu, thanks for reporting.
I think offline mode is not supported for datasets containing only data files (without any loading script). I'm having a look into this... | [
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https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | Thanks for your feedback!
To give you a little more info, if you don't set the offline mode flag, the script will load the cache. I first noticed this behavior with the `evaluate` library, and while trying to understand the downloading flow I realized that I had a similar error with datasets. | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
| 52 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
Thanks for your feedback!
To give you a little more info, if you don't set the offline mode flag, the script will load the cache. I first noticed this behavior with the `evaluate` library, and while trying to understand the downloading flow I realized that I had a similar error with datasets. | [
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https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | #5331 will be helpful to fix this, as it updates the cache directory template to be aligned with the other datasets | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
| 21 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
#5331 will be helpful to fix this, as it updates the cache directory template to be aligned with the other datasets | [
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https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | This issue has been fixed in `datasets` 2.16 by https://github.com/huggingface/datasets/pull/6493. The cache is now working properly :)
You just have to update `datasets`:
```
pip install -U datasets
``` | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
| 29 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
This issue has been fixed in `datasets` 2.16 by https://github.com/huggingface/datasets/pull/6493. The cache is now working properly :)
You just have to update `datasets`:
```
pip install -U datasets
``` | [
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https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | Can you share some code to reproduce your issue ?
Also make sure your cache was populated with recent versions of `datasets`. Datasets cached with old versions may not be reloadable in offline mode, though we did our best to keep as much backward compatibility as possible. | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
| 47 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
Can you share some code to reproduce your issue ?
Also make sure your cache was populated with recent versions of `datasets`. Datasets cached with old versions may not be reloadable in offline mode, though we did our best to keep as much backward compatibility as possible. | [
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https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | I'm not sure if this is related @lhoestq but I am experiencing a similar issue when using offline mode:
```bash
$ python -c "from datasets import load_dataset; load_dataset('openai_humaneval', split='test')"
$ HF_DATASETS_OFFLINE=1 python -c "from datasets import load_dataset; load_dataset('openai_humaneval', split='test')"
Using the latest cached version of the dataset since openai_humaneval couldn't be found on the Hugging Face Hub (offline mode is enabled).
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/load.py", line 2265, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py", line 122, in __init__
config_name, version, hash = _find_hash_in_cache(
File "/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py", line 48, in _find_hash_in_cache
raise ValueError(
ValueError: Couldn't find cache for openai_humaneval for config 'default'
Available configs in the cache: ['openai_humaneval']
``` | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
| 126 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
I'm not sure if this is related @lhoestq but I am experiencing a similar issue when using offline mode:
```bash
$ python -c "from datasets import load_dataset; load_dataset('openai_humaneval', split='test')"
$ HF_DATASETS_OFFLINE=1 python -c "from datasets import load_dataset; load_dataset('openai_humaneval', split='test')"
Using the latest cached version of the dataset since openai_humaneval couldn't be found on the Hugging Face Hub (offline mode is enabled).
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/load.py", line 2265, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py", line 122, in __init__
config_name, version, hash = _find_hash_in_cache(
File "/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py", line 48, in _find_hash_in_cache
raise ValueError(
ValueError: Couldn't find cache for openai_humaneval for config 'default'
Available configs in the cache: ['openai_humaneval']
``` | [
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https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | > Thanks for reporting @BramVanroy, I managed to reproduce and I opened a fix here: #6741
Thanks a lot！ I have faced the same problem. Can I use your fix code to directly replace the existing version code? I noticed that this fix has not been merged yet. Will it affect other functionalities?
| ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
| 53 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
> Thanks for reporting @BramVanroy, I managed to reproduce and I opened a fix here: #6741
Thanks a lot！ I have faced the same problem. Can I use your fix code to directly replace the existing version code? I noticed that this fix has not been merged yet. Will it affect other functionalities?
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https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | I just merged the fix, you can install `datasets` from source or wait for the patch release which will be out in the coming days | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
| 25 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
I just merged the fix, you can install `datasets` from source or wait for the patch release which will be out in the coming days | [
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] |
https://github.com/huggingface/datasets/issues/4759 | Dataset Viewer issue for Toygar/turkish-offensive-language-detection | I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.
<img width="1557" alt="Capture d’écran 2022-07-28 à 09 17 39" src="https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png">
| ### Link
https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection
### Description
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
Hi, I provided train.csv, test.csv and valid.csv files. However, viewer says dataset does not exist.
Should I need to do anything else?
### Owner
Yes | 23 | Dataset Viewer issue for Toygar/turkish-offensive-language-detection
### Link
https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection
### Description
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
Hi, I provided train.csv, test.csv and valid.csv files. However, viewer says dataset does not exist.
Should I need to do anything else?
### Owner
Yes
I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.
<img width="1557" alt="Capture d’écran 2022-07-28 à 09 17 39" src="https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png">
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https://github.com/huggingface/datasets/issues/4755 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size | I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:
```
import datasets
import transformers
pretrained = 'deepset/tinyroberta-squad2'
tokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)
questions = ['Can you tell me why?', 'What time is it?']
contexts = ['This is context zero', 'Another paragraph goes here']
def tok(questions, contexts):
return tokenizer(text=questions,
text_pair=contexts,
truncation='only_second',
return_overflowing_tokens=True,
)
print(tok(questions, contexts)['overflow_to_sample_mapping'])
assert tok(questions, contexts)['overflow_to_sample_mapping'] == [0, 1] # PASSES
def tok2(d):
return tok(d['question'], d['context'])
def tok2(d):
return tok(d['question'], d['context'])
ds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})
tokens = ds.map(tok2, batched=True, batch_size=1)
print(tokens['overflow_to_sample_mapping'])
assert tokens['overflow_to_sample_mapping'] == [0, 1] # FAILS produces [0,0]
```
Note that even if the batch size would be larger, there will be instances where we will not have a lot of data, and end up using small batches. This can occur e.g. if `n_proc` causes batches to be underfill. I imagine it can also occur in other ways, e.g. the final leftover batch at the end. | ## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.
## Steps to reproduce the bug
1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field
## Expected results
`[0, 1, 2]`
## Actual results
`[0, 0, 0]`
Notes:
1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
| 170 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size
## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.
## Steps to reproduce the bug
1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field
## Expected results
`[0, 1, 2]`
## Actual results
`[0, 0, 0]`
Notes:
1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:
```
import datasets
import transformers
pretrained = 'deepset/tinyroberta-squad2'
tokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)
questions = ['Can you tell me why?', 'What time is it?']
contexts = ['This is context zero', 'Another paragraph goes here']
def tok(questions, contexts):
return tokenizer(text=questions,
text_pair=contexts,
truncation='only_second',
return_overflowing_tokens=True,
)
print(tok(questions, contexts)['overflow_to_sample_mapping'])
assert tok(questions, contexts)['overflow_to_sample_mapping'] == [0, 1] # PASSES
def tok2(d):
return tok(d['question'], d['context'])
def tok2(d):
return tok(d['question'], d['context'])
ds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})
tokens = ds.map(tok2, batched=True, batch_size=1)
print(tokens['overflow_to_sample_mapping'])
assert tokens['overflow_to_sample_mapping'] == [0, 1] # FAILS produces [0,0]
```
Note that even if the batch size would be larger, there will be instances where we will not have a lot of data, and end up using small batches. This can occur e.g. if `n_proc` causes batches to be underfill. I imagine it can also occur in other ways, e.g. the final leftover batch at the end. | [
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https://github.com/huggingface/datasets/issues/4755 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size | A larger batch size does _not_ have this behavior:
```
def tok2(d):
return tok(d['question'], d['context'])
ds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})
tokens = ds.map(tok2, batched=True, batch_size=2)
print(tokens['overflow_to_sample_mapping'])
assert tokens['overflow_to_sample_mapping'] == [0, 1] # PASSES
``` | ## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.
## Steps to reproduce the bug
1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field
## Expected results
`[0, 1, 2]`
## Actual results
`[0, 0, 0]`
Notes:
1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
| 35 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size
## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.
## Steps to reproduce the bug
1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field
## Expected results
`[0, 1, 2]`
## Actual results
`[0, 0, 0]`
Notes:
1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
A larger batch size does _not_ have this behavior:
```
def tok2(d):
return tok(d['question'], d['context'])
ds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})
tokens = ds.map(tok2, batched=True, batch_size=2)
print(tokens['overflow_to_sample_mapping'])
assert tokens['overflow_to_sample_mapping'] == [0, 1] # PASSES
``` | [
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https://github.com/huggingface/datasets/issues/4755 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size | I was trying the [Question answering](https://huggingface.co/learn/nlp-course/chapter7/7#question-answering) tutorial on Hugging face when i faced the same problem. The preprocessing step is [here](https://huggingface.co/learn/nlp-course/chapter7/7#processing-the-validation-data). i have changed ```max_length=200, stride=50```,
```
validation_dataset = raw_datasets['validation'].select(range(8)).map(
preprocess_validation_examples,
batched=True,
remove_columns=raw_datasets["validation"].column_names,
num_proc=1
)
print(validation_dataset['overflow_to_sample_mapping'])
print(validation_dataset['example_id'])
```
result
```
[0, 1, 2, 3, 4, 5, 6, 7]
['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ee',
'56be4db0acb8001400a502ef', '56be4db0acb8001400a502f0', '56be8e613aeaaa14008c90d1',
'56be8e613aeaaa14008c90d2', '56be8e613aeaaa14008c90d3']
```
when ```num_proc=2```, result -
```
[0, 1, 2, 3, 0, 1, 2, 3]
['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ee',
'56be4db0acb8001400a502ef', '56be4db0acb8001400a502f0', '56be8e613aeaaa14008c90d1',
'56be8e613aeaaa14008c90d2', '56be8e613aeaaa14008c90d3']
```
when ```num_proc=3```, result -
```
[0, 1, 2, 0, 1, 2, 0, 1]
['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ee',
'56be4db0acb8001400a502ef', '56be4db0acb8001400a502f0', '56be8e613aeaaa14008c90d1',
'56be8e613aeaaa14008c90d2', '56be8e613aeaaa14008c90d3']
```
The```overflow_to_sample_mapping``` changes with ```num_proc```, but ```example_id``` field remains the same . It seems that each process in ```map``` has its own counter for overflow_to_sample_mapping. If you are using ```overflow_to_sample_mapping``` inside the ```preprocess_validation_examples``` function, then there is no issue. | ## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.
## Steps to reproduce the bug
1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field
## Expected results
`[0, 1, 2]`
## Actual results
`[0, 0, 0]`
Notes:
1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
| 139 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size
## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.
## Steps to reproduce the bug
1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field
## Expected results
`[0, 1, 2]`
## Actual results
`[0, 0, 0]`
Notes:
1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
I was trying the [Question answering](https://huggingface.co/learn/nlp-course/chapter7/7#question-answering) tutorial on Hugging face when i faced the same problem. The preprocessing step is [here](https://huggingface.co/learn/nlp-course/chapter7/7#processing-the-validation-data). i have changed ```max_length=200, stride=50```,
```
validation_dataset = raw_datasets['validation'].select(range(8)).map(
preprocess_validation_examples,
batched=True,
remove_columns=raw_datasets["validation"].column_names,
num_proc=1
)
print(validation_dataset['overflow_to_sample_mapping'])
print(validation_dataset['example_id'])
```
result
```
[0, 1, 2, 3, 4, 5, 6, 7]
['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ee',
'56be4db0acb8001400a502ef', '56be4db0acb8001400a502f0', '56be8e613aeaaa14008c90d1',
'56be8e613aeaaa14008c90d2', '56be8e613aeaaa14008c90d3']
```
when ```num_proc=2```, result -
```
[0, 1, 2, 3, 0, 1, 2, 3]
['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ee',
'56be4db0acb8001400a502ef', '56be4db0acb8001400a502f0', '56be8e613aeaaa14008c90d1',
'56be8e613aeaaa14008c90d2', '56be8e613aeaaa14008c90d3']
```
when ```num_proc=3```, result -
```
[0, 1, 2, 0, 1, 2, 0, 1]
['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ee',
'56be4db0acb8001400a502ef', '56be4db0acb8001400a502f0', '56be8e613aeaaa14008c90d1',
'56be8e613aeaaa14008c90d2', '56be8e613aeaaa14008c90d3']
```
The```overflow_to_sample_mapping``` changes with ```num_proc```, but ```example_id``` field remains the same . It seems that each process in ```map``` has its own counter for overflow_to_sample_mapping. If you are using ```overflow_to_sample_mapping``` inside the ```preprocess_validation_examples``` function, then there is no issue. | [
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] |
https://github.com/huggingface/datasets/issues/4752 | DatasetInfo issue when testing multiple configs: mixed task_templates | I've narrowed down the issue to the `dataset_module_factory` which already creates a `dataset_infos.json` file down in the `.cache/modules/dataset_modules/..` folder. That JSON file already contains the wrong task_templates for `unfiltered`. | ## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data from unfiltered.json.gz (I'd want this without splits, just one chunk of data, but that does not seem possible?)
- filtered_sentiment: `review_sentiment` as ClassLabel, TextClassification task with `review_sentiment` as label. Gets train/test split from respective json.gz files
- filtered_rating: `review_rating0` as ClassLabel, TextClassification task with `review_rating0` as label. Gets train/test split from respective json.gz files
This might be a bit tedious to reproduce, so I am sorry, but these are the steps:
- Clone datasets -> `datasets/` and install it
- Clone `https://huggingface.co/datasets/BramVanroy/hebban-reviews` into `datasets/datasets` so that you have a new folder `datasets/datasets/hebban-reviews/`.
- Replace the HebbanReviews class with this new one:
```python
class HebbanReviews(datasets.GeneratorBasedBuilder):
"""The Hebban book reviews dataset."""
BUILDER_CONFIGS = [
HebbanReviewsConfig(
name="unfiltered",
description=_HEBBAN_REVIEWS_UNFILTERED_DESCRIPTION,
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_sentiment",
description=f"This config has the negative, neutral, and positive sentiment scores as ClassLabel in the 'review_sentiment' column.\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_rating",
description=f"This config has the 5-class ratings as ClassLabel in the 'review_rating0' column (which is a variant of 'review_rating' that starts counting from 0 instead of 1).\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
)
]
DEFAULT_CONFIG_NAME = "filtered_sentiment"
_URLS = {
"train": "train.jsonl.gz",
"test": "test.jsonl.gz",
"unfiltered": "unfiltered.jsonl.gz",
}
def _info(self):
features = {
"review_title": datasets.Value("string"),
"review_text": datasets.Value("string"),
"review_text_without_quotes": datasets.Value("string"),
"review_n_quotes": datasets.Value("int32"),
"review_n_tokens": datasets.Value("int32"),
"review_rating": datasets.Value("int32"),
"review_rating0": datasets.Value("int32"),
"review_author_url": datasets.Value("string"),
"review_author_type": datasets.Value("string"),
"review_n_likes": datasets.Value("int32"),
"review_n_comments": datasets.Value("int32"),
"review_url": datasets.Value("string"),
"review_published_date": datasets.Value("string"),
"review_crawl_date": datasets.Value("string"),
"lid": datasets.Value("string"),
"lid_probability": datasets.Value("float32"),
"review_sentiment": datasets.features.ClassLabel(names=["negative", "neutral", "positive"]),
"review_sentiment_label": datasets.Value("string"),
"book_id": datasets.Value("int32"),
}
if self.config.name == "filtered_sentiment":
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_sentiment")]
elif self.config.name == "filtered_rating":
# For CrossEntropy, our classes need to start at index 0 -- not 1
features["review_rating0"] = datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"])
features["review_sentiment"] = datasets.Value("int32")
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_rating0")]
elif self.config.name == "unfiltered": # no ClassLabels in unfiltered
features["review_sentiment"] = datasets.Value("int32")
task_templates = None
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
print("AT INFO", self.config.name, task_templates)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage="https://huggingface.co/datasets/BramVanroy/hebban-reviews",
citation=_HEBBAN_REVIEWS_CITATION,
task_templates=task_templates,
license="cc-by-4.0"
)
def _split_generators(self, dl_manager):
if self.config.name.startswith("filtered"):
files = dl_manager.download_and_extract({"train": "train.jsonl.gz",
"test": "test.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": files["test"]
},
),
]
elif self.config.name == "unfiltered":
files = dl_manager.download_and_extract({"train": "unfiltered.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
]
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
def _generate_examples(self, data_file):
lines = Path(data_file).open(encoding="utf-8").readlines()
for line_idx, line in enumerate(lines):
row = json.loads(line)
yield line_idx, row
```
- finally, run `datasets-cli test ./datasets/hebban-reviews/ --save_infos --all_configs` from within the topmost `datasets` directory
## Expected results
Succeeding tests for three different configs.
## Actual results
I printed out the values that are given to `DatasetInfo` for config name and task_templates, as you can see. There, as expected, I get `unfiltered None`. I also modified datasets/info.py and added this line [at L.170](https://github.com/huggingface/datasets/blob/f5847a304aa1b38b3a3c54a8318b4df60f1299bc/src/datasets/info.py#L170):
```python
print("INTERNALLY AT INFO.PY", self.config_name, self.task_templates)
```
to my surprise, here I get `unfiltered [TextClassification(task='text-classification', text_column='review_text_without_quotes', label_column='review_sentiment')]`. So one way or another, here I suddenly see that `unfiltered` now does have a task_template -- even though that is not what is written in the data loading script, as the first print statement correctly shows.
I do not quite understand how, but it seems that the config name and task_templates get mixed.
This ultimately leads to the following error, but this trace may not be very useful in itself:
```
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\hebban-U6poXNQd\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "c:\dev\python\hebban\datasets\src\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\dev\python\hebban\datasets\src\datasets\commands\test.py", line 144, in run
builder.as_dataset()
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 899, in as_dataset
datasets = map_nested(
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 393, in map_nested
mapped = [
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 930, in _build_single_dataset
ds = self._as_dataset(
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 1006, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "c:\dev\python\hebban\datasets\src\datasets\arrow_dataset.py", line 661, in __init__
info = info.copy() if info is not None else DatasetInfo()
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 286, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 176, in __post_init__
self.task_templates = [
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 177, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "c:\dev\python\hebban\datasets\src\datasets\tasks\text_classification.py", line 22, in align_with_features
raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
ValueError: Column review_sentiment is not a ClassLabel.
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | 29 | DatasetInfo issue when testing multiple configs: mixed task_templates
## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data from unfiltered.json.gz (I'd want this without splits, just one chunk of data, but that does not seem possible?)
- filtered_sentiment: `review_sentiment` as ClassLabel, TextClassification task with `review_sentiment` as label. Gets train/test split from respective json.gz files
- filtered_rating: `review_rating0` as ClassLabel, TextClassification task with `review_rating0` as label. Gets train/test split from respective json.gz files
This might be a bit tedious to reproduce, so I am sorry, but these are the steps:
- Clone datasets -> `datasets/` and install it
- Clone `https://huggingface.co/datasets/BramVanroy/hebban-reviews` into `datasets/datasets` so that you have a new folder `datasets/datasets/hebban-reviews/`.
- Replace the HebbanReviews class with this new one:
```python
class HebbanReviews(datasets.GeneratorBasedBuilder):
"""The Hebban book reviews dataset."""
BUILDER_CONFIGS = [
HebbanReviewsConfig(
name="unfiltered",
description=_HEBBAN_REVIEWS_UNFILTERED_DESCRIPTION,
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_sentiment",
description=f"This config has the negative, neutral, and positive sentiment scores as ClassLabel in the 'review_sentiment' column.\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_rating",
description=f"This config has the 5-class ratings as ClassLabel in the 'review_rating0' column (which is a variant of 'review_rating' that starts counting from 0 instead of 1).\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
)
]
DEFAULT_CONFIG_NAME = "filtered_sentiment"
_URLS = {
"train": "train.jsonl.gz",
"test": "test.jsonl.gz",
"unfiltered": "unfiltered.jsonl.gz",
}
def _info(self):
features = {
"review_title": datasets.Value("string"),
"review_text": datasets.Value("string"),
"review_text_without_quotes": datasets.Value("string"),
"review_n_quotes": datasets.Value("int32"),
"review_n_tokens": datasets.Value("int32"),
"review_rating": datasets.Value("int32"),
"review_rating0": datasets.Value("int32"),
"review_author_url": datasets.Value("string"),
"review_author_type": datasets.Value("string"),
"review_n_likes": datasets.Value("int32"),
"review_n_comments": datasets.Value("int32"),
"review_url": datasets.Value("string"),
"review_published_date": datasets.Value("string"),
"review_crawl_date": datasets.Value("string"),
"lid": datasets.Value("string"),
"lid_probability": datasets.Value("float32"),
"review_sentiment": datasets.features.ClassLabel(names=["negative", "neutral", "positive"]),
"review_sentiment_label": datasets.Value("string"),
"book_id": datasets.Value("int32"),
}
if self.config.name == "filtered_sentiment":
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_sentiment")]
elif self.config.name == "filtered_rating":
# For CrossEntropy, our classes need to start at index 0 -- not 1
features["review_rating0"] = datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"])
features["review_sentiment"] = datasets.Value("int32")
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_rating0")]
elif self.config.name == "unfiltered": # no ClassLabels in unfiltered
features["review_sentiment"] = datasets.Value("int32")
task_templates = None
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
print("AT INFO", self.config.name, task_templates)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage="https://huggingface.co/datasets/BramVanroy/hebban-reviews",
citation=_HEBBAN_REVIEWS_CITATION,
task_templates=task_templates,
license="cc-by-4.0"
)
def _split_generators(self, dl_manager):
if self.config.name.startswith("filtered"):
files = dl_manager.download_and_extract({"train": "train.jsonl.gz",
"test": "test.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": files["test"]
},
),
]
elif self.config.name == "unfiltered":
files = dl_manager.download_and_extract({"train": "unfiltered.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
]
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
def _generate_examples(self, data_file):
lines = Path(data_file).open(encoding="utf-8").readlines()
for line_idx, line in enumerate(lines):
row = json.loads(line)
yield line_idx, row
```
- finally, run `datasets-cli test ./datasets/hebban-reviews/ --save_infos --all_configs` from within the topmost `datasets` directory
## Expected results
Succeeding tests for three different configs.
## Actual results
I printed out the values that are given to `DatasetInfo` for config name and task_templates, as you can see. There, as expected, I get `unfiltered None`. I also modified datasets/info.py and added this line [at L.170](https://github.com/huggingface/datasets/blob/f5847a304aa1b38b3a3c54a8318b4df60f1299bc/src/datasets/info.py#L170):
```python
print("INTERNALLY AT INFO.PY", self.config_name, self.task_templates)
```
to my surprise, here I get `unfiltered [TextClassification(task='text-classification', text_column='review_text_without_quotes', label_column='review_sentiment')]`. So one way or another, here I suddenly see that `unfiltered` now does have a task_template -- even though that is not what is written in the data loading script, as the first print statement correctly shows.
I do not quite understand how, but it seems that the config name and task_templates get mixed.
This ultimately leads to the following error, but this trace may not be very useful in itself:
```
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\hebban-U6poXNQd\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "c:\dev\python\hebban\datasets\src\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\dev\python\hebban\datasets\src\datasets\commands\test.py", line 144, in run
builder.as_dataset()
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 899, in as_dataset
datasets = map_nested(
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 393, in map_nested
mapped = [
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 930, in _build_single_dataset
ds = self._as_dataset(
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 1006, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "c:\dev\python\hebban\datasets\src\datasets\arrow_dataset.py", line 661, in __init__
info = info.copy() if info is not None else DatasetInfo()
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 286, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 176, in __post_init__
self.task_templates = [
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 177, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "c:\dev\python\hebban\datasets\src\datasets\tasks\text_classification.py", line 22, in align_with_features
raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
ValueError: Column review_sentiment is not a ClassLabel.
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
I've narrowed down the issue to the `dataset_module_factory` which already creates a `dataset_infos.json` file down in the `.cache/modules/dataset_modules/..` folder. That JSON file already contains the wrong task_templates for `unfiltered`. | [
-0.30294740200042725,
-0.02238108217716217,
-0.007900869473814964,
0.27841848134994507,
0.04747239127755165,
0.0910637304186821,
0.4126910865306854,
0.2915855348110199,
-0.2092212736606598,
-0.03551861643791199,
-0.2731928825378418,
0.08178993314504623,
-0.011083702556788921,
0.13023871183395386,
-0.16048872470855713,
0.07670483738183975,
0.1474226862192154,
-0.08985315263271332,
-0.09656168520450592,
0.20267952978610992,
-0.017200278118252754,
0.21734634041786194,
-0.12220072001218796,
-0.03424946963787079,
-0.2773225009441376,
-0.37570691108703613,
-0.2987944185733795,
0.2699897289276123,
0.024026833474636078,
-0.0822402760386467,
0.4878433346748352,
0.37967467308044434,
-0.3503599464893341,
0.6244824528694153,
-0.00011843802349176258,
0.15155130624771118,
0.0972118079662323,
0.009496333077549934,
-0.2661677896976471,
0.09607415646314621,
-0.31376296281814575,
-0.0023348331451416016,
-0.2756499946117401,
-0.1757371574640274,
-0.06886974722146988,
0.12302394956350327,
-0.41851964592933655,
-0.22040289640426636,
0.1484028548002243,
0.24308347702026367,
0.10377523303031921,
0.00045577622950077057,
-0.49932119250297546,
-0.0640961304306984,
0.010494440793991089,
0.25357291102409363,
-0.22226499021053314,
0.11761537194252014,
0.49024760723114014,
-0.07115299999713898,
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0.1593988537788391,
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0.33772292733192444,
-0.41770222783088684,
0.14139634370803833,
0.2331722378730774,
0.48351964354515076,
0.2029017210006714,
-0.047514885663986206,
-0.3638101816177368,
-0.2828819453716278,
-0.3068445920944214,
-0.09993434697389603,
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0.30564215779304504,
-0.2672106921672821,
0.17745333909988403,
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0.11268223822116852,
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0.1301737129688263,
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0.13949641585350037,
0.15021991729736328,
0.19984689354896545,
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0.28425273299217224,
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0.40658846497535706,
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0.016935281455516815,
0.13802041113376617,
0.0448787659406662,
0.005428901873528957,
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0.4385403096675873,
-0.035499170422554016,
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0.2760704755783081,
0.13236401975154877,
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0.06996025145053864,
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0.06623825430870056,
-0.3423503339290619,
0.2695068120956421,
0.11304938793182373
] |
https://github.com/huggingface/datasets/issues/4752 | DatasetInfo issue when testing multiple configs: mixed task_templates | Ugh. Found the issue: apparently `datasets` was reusing the already existing `dataset_infos.json` that is inside `datasets/datasets/hebban-reviews`! Is this desired behavior?
Perhaps when `--save_infos` and `--all_configs` are given, an existing `dataset_infos.json` file should first be deleted before continuing with the test? Because that would assume that the user wants to create a new infos file for all configs anyway. | ## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data from unfiltered.json.gz (I'd want this without splits, just one chunk of data, but that does not seem possible?)
- filtered_sentiment: `review_sentiment` as ClassLabel, TextClassification task with `review_sentiment` as label. Gets train/test split from respective json.gz files
- filtered_rating: `review_rating0` as ClassLabel, TextClassification task with `review_rating0` as label. Gets train/test split from respective json.gz files
This might be a bit tedious to reproduce, so I am sorry, but these are the steps:
- Clone datasets -> `datasets/` and install it
- Clone `https://huggingface.co/datasets/BramVanroy/hebban-reviews` into `datasets/datasets` so that you have a new folder `datasets/datasets/hebban-reviews/`.
- Replace the HebbanReviews class with this new one:
```python
class HebbanReviews(datasets.GeneratorBasedBuilder):
"""The Hebban book reviews dataset."""
BUILDER_CONFIGS = [
HebbanReviewsConfig(
name="unfiltered",
description=_HEBBAN_REVIEWS_UNFILTERED_DESCRIPTION,
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_sentiment",
description=f"This config has the negative, neutral, and positive sentiment scores as ClassLabel in the 'review_sentiment' column.\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_rating",
description=f"This config has the 5-class ratings as ClassLabel in the 'review_rating0' column (which is a variant of 'review_rating' that starts counting from 0 instead of 1).\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
)
]
DEFAULT_CONFIG_NAME = "filtered_sentiment"
_URLS = {
"train": "train.jsonl.gz",
"test": "test.jsonl.gz",
"unfiltered": "unfiltered.jsonl.gz",
}
def _info(self):
features = {
"review_title": datasets.Value("string"),
"review_text": datasets.Value("string"),
"review_text_without_quotes": datasets.Value("string"),
"review_n_quotes": datasets.Value("int32"),
"review_n_tokens": datasets.Value("int32"),
"review_rating": datasets.Value("int32"),
"review_rating0": datasets.Value("int32"),
"review_author_url": datasets.Value("string"),
"review_author_type": datasets.Value("string"),
"review_n_likes": datasets.Value("int32"),
"review_n_comments": datasets.Value("int32"),
"review_url": datasets.Value("string"),
"review_published_date": datasets.Value("string"),
"review_crawl_date": datasets.Value("string"),
"lid": datasets.Value("string"),
"lid_probability": datasets.Value("float32"),
"review_sentiment": datasets.features.ClassLabel(names=["negative", "neutral", "positive"]),
"review_sentiment_label": datasets.Value("string"),
"book_id": datasets.Value("int32"),
}
if self.config.name == "filtered_sentiment":
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_sentiment")]
elif self.config.name == "filtered_rating":
# For CrossEntropy, our classes need to start at index 0 -- not 1
features["review_rating0"] = datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"])
features["review_sentiment"] = datasets.Value("int32")
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_rating0")]
elif self.config.name == "unfiltered": # no ClassLabels in unfiltered
features["review_sentiment"] = datasets.Value("int32")
task_templates = None
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
print("AT INFO", self.config.name, task_templates)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage="https://huggingface.co/datasets/BramVanroy/hebban-reviews",
citation=_HEBBAN_REVIEWS_CITATION,
task_templates=task_templates,
license="cc-by-4.0"
)
def _split_generators(self, dl_manager):
if self.config.name.startswith("filtered"):
files = dl_manager.download_and_extract({"train": "train.jsonl.gz",
"test": "test.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": files["test"]
},
),
]
elif self.config.name == "unfiltered":
files = dl_manager.download_and_extract({"train": "unfiltered.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
]
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
def _generate_examples(self, data_file):
lines = Path(data_file).open(encoding="utf-8").readlines()
for line_idx, line in enumerate(lines):
row = json.loads(line)
yield line_idx, row
```
- finally, run `datasets-cli test ./datasets/hebban-reviews/ --save_infos --all_configs` from within the topmost `datasets` directory
## Expected results
Succeeding tests for three different configs.
## Actual results
I printed out the values that are given to `DatasetInfo` for config name and task_templates, as you can see. There, as expected, I get `unfiltered None`. I also modified datasets/info.py and added this line [at L.170](https://github.com/huggingface/datasets/blob/f5847a304aa1b38b3a3c54a8318b4df60f1299bc/src/datasets/info.py#L170):
```python
print("INTERNALLY AT INFO.PY", self.config_name, self.task_templates)
```
to my surprise, here I get `unfiltered [TextClassification(task='text-classification', text_column='review_text_without_quotes', label_column='review_sentiment')]`. So one way or another, here I suddenly see that `unfiltered` now does have a task_template -- even though that is not what is written in the data loading script, as the first print statement correctly shows.
I do not quite understand how, but it seems that the config name and task_templates get mixed.
This ultimately leads to the following error, but this trace may not be very useful in itself:
```
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\hebban-U6poXNQd\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "c:\dev\python\hebban\datasets\src\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\dev\python\hebban\datasets\src\datasets\commands\test.py", line 144, in run
builder.as_dataset()
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 899, in as_dataset
datasets = map_nested(
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 393, in map_nested
mapped = [
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 930, in _build_single_dataset
ds = self._as_dataset(
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 1006, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "c:\dev\python\hebban\datasets\src\datasets\arrow_dataset.py", line 661, in __init__
info = info.copy() if info is not None else DatasetInfo()
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 286, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 176, in __post_init__
self.task_templates = [
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 177, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "c:\dev\python\hebban\datasets\src\datasets\tasks\text_classification.py", line 22, in align_with_features
raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
ValueError: Column review_sentiment is not a ClassLabel.
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | 58 | DatasetInfo issue when testing multiple configs: mixed task_templates
## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data from unfiltered.json.gz (I'd want this without splits, just one chunk of data, but that does not seem possible?)
- filtered_sentiment: `review_sentiment` as ClassLabel, TextClassification task with `review_sentiment` as label. Gets train/test split from respective json.gz files
- filtered_rating: `review_rating0` as ClassLabel, TextClassification task with `review_rating0` as label. Gets train/test split from respective json.gz files
This might be a bit tedious to reproduce, so I am sorry, but these are the steps:
- Clone datasets -> `datasets/` and install it
- Clone `https://huggingface.co/datasets/BramVanroy/hebban-reviews` into `datasets/datasets` so that you have a new folder `datasets/datasets/hebban-reviews/`.
- Replace the HebbanReviews class with this new one:
```python
class HebbanReviews(datasets.GeneratorBasedBuilder):
"""The Hebban book reviews dataset."""
BUILDER_CONFIGS = [
HebbanReviewsConfig(
name="unfiltered",
description=_HEBBAN_REVIEWS_UNFILTERED_DESCRIPTION,
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_sentiment",
description=f"This config has the negative, neutral, and positive sentiment scores as ClassLabel in the 'review_sentiment' column.\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_rating",
description=f"This config has the 5-class ratings as ClassLabel in the 'review_rating0' column (which is a variant of 'review_rating' that starts counting from 0 instead of 1).\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
)
]
DEFAULT_CONFIG_NAME = "filtered_sentiment"
_URLS = {
"train": "train.jsonl.gz",
"test": "test.jsonl.gz",
"unfiltered": "unfiltered.jsonl.gz",
}
def _info(self):
features = {
"review_title": datasets.Value("string"),
"review_text": datasets.Value("string"),
"review_text_without_quotes": datasets.Value("string"),
"review_n_quotes": datasets.Value("int32"),
"review_n_tokens": datasets.Value("int32"),
"review_rating": datasets.Value("int32"),
"review_rating0": datasets.Value("int32"),
"review_author_url": datasets.Value("string"),
"review_author_type": datasets.Value("string"),
"review_n_likes": datasets.Value("int32"),
"review_n_comments": datasets.Value("int32"),
"review_url": datasets.Value("string"),
"review_published_date": datasets.Value("string"),
"review_crawl_date": datasets.Value("string"),
"lid": datasets.Value("string"),
"lid_probability": datasets.Value("float32"),
"review_sentiment": datasets.features.ClassLabel(names=["negative", "neutral", "positive"]),
"review_sentiment_label": datasets.Value("string"),
"book_id": datasets.Value("int32"),
}
if self.config.name == "filtered_sentiment":
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_sentiment")]
elif self.config.name == "filtered_rating":
# For CrossEntropy, our classes need to start at index 0 -- not 1
features["review_rating0"] = datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"])
features["review_sentiment"] = datasets.Value("int32")
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_rating0")]
elif self.config.name == "unfiltered": # no ClassLabels in unfiltered
features["review_sentiment"] = datasets.Value("int32")
task_templates = None
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
print("AT INFO", self.config.name, task_templates)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage="https://huggingface.co/datasets/BramVanroy/hebban-reviews",
citation=_HEBBAN_REVIEWS_CITATION,
task_templates=task_templates,
license="cc-by-4.0"
)
def _split_generators(self, dl_manager):
if self.config.name.startswith("filtered"):
files = dl_manager.download_and_extract({"train": "train.jsonl.gz",
"test": "test.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": files["test"]
},
),
]
elif self.config.name == "unfiltered":
files = dl_manager.download_and_extract({"train": "unfiltered.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
]
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
def _generate_examples(self, data_file):
lines = Path(data_file).open(encoding="utf-8").readlines()
for line_idx, line in enumerate(lines):
row = json.loads(line)
yield line_idx, row
```
- finally, run `datasets-cli test ./datasets/hebban-reviews/ --save_infos --all_configs` from within the topmost `datasets` directory
## Expected results
Succeeding tests for three different configs.
## Actual results
I printed out the values that are given to `DatasetInfo` for config name and task_templates, as you can see. There, as expected, I get `unfiltered None`. I also modified datasets/info.py and added this line [at L.170](https://github.com/huggingface/datasets/blob/f5847a304aa1b38b3a3c54a8318b4df60f1299bc/src/datasets/info.py#L170):
```python
print("INTERNALLY AT INFO.PY", self.config_name, self.task_templates)
```
to my surprise, here I get `unfiltered [TextClassification(task='text-classification', text_column='review_text_without_quotes', label_column='review_sentiment')]`. So one way or another, here I suddenly see that `unfiltered` now does have a task_template -- even though that is not what is written in the data loading script, as the first print statement correctly shows.
I do not quite understand how, but it seems that the config name and task_templates get mixed.
This ultimately leads to the following error, but this trace may not be very useful in itself:
```
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\hebban-U6poXNQd\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "c:\dev\python\hebban\datasets\src\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\dev\python\hebban\datasets\src\datasets\commands\test.py", line 144, in run
builder.as_dataset()
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 899, in as_dataset
datasets = map_nested(
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 393, in map_nested
mapped = [
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 930, in _build_single_dataset
ds = self._as_dataset(
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 1006, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "c:\dev\python\hebban\datasets\src\datasets\arrow_dataset.py", line 661, in __init__
info = info.copy() if info is not None else DatasetInfo()
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 286, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 176, in __post_init__
self.task_templates = [
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 177, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "c:\dev\python\hebban\datasets\src\datasets\tasks\text_classification.py", line 22, in align_with_features
raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
ValueError: Column review_sentiment is not a ClassLabel.
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Ugh. Found the issue: apparently `datasets` was reusing the already existing `dataset_infos.json` that is inside `datasets/datasets/hebban-reviews`! Is this desired behavior?
Perhaps when `--save_infos` and `--all_configs` are given, an existing `dataset_infos.json` file should first be deleted before continuing with the test? Because that would assume that the user wants to create a new infos file for all configs anyway. | [
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] |
https://github.com/huggingface/datasets/issues/4752 | DatasetInfo issue when testing multiple configs: mixed task_templates | Hi! I think this is a reasonable solution. Would you be interested in submitting a PR? | ## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data from unfiltered.json.gz (I'd want this without splits, just one chunk of data, but that does not seem possible?)
- filtered_sentiment: `review_sentiment` as ClassLabel, TextClassification task with `review_sentiment` as label. Gets train/test split from respective json.gz files
- filtered_rating: `review_rating0` as ClassLabel, TextClassification task with `review_rating0` as label. Gets train/test split from respective json.gz files
This might be a bit tedious to reproduce, so I am sorry, but these are the steps:
- Clone datasets -> `datasets/` and install it
- Clone `https://huggingface.co/datasets/BramVanroy/hebban-reviews` into `datasets/datasets` so that you have a new folder `datasets/datasets/hebban-reviews/`.
- Replace the HebbanReviews class with this new one:
```python
class HebbanReviews(datasets.GeneratorBasedBuilder):
"""The Hebban book reviews dataset."""
BUILDER_CONFIGS = [
HebbanReviewsConfig(
name="unfiltered",
description=_HEBBAN_REVIEWS_UNFILTERED_DESCRIPTION,
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_sentiment",
description=f"This config has the negative, neutral, and positive sentiment scores as ClassLabel in the 'review_sentiment' column.\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_rating",
description=f"This config has the 5-class ratings as ClassLabel in the 'review_rating0' column (which is a variant of 'review_rating' that starts counting from 0 instead of 1).\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
)
]
DEFAULT_CONFIG_NAME = "filtered_sentiment"
_URLS = {
"train": "train.jsonl.gz",
"test": "test.jsonl.gz",
"unfiltered": "unfiltered.jsonl.gz",
}
def _info(self):
features = {
"review_title": datasets.Value("string"),
"review_text": datasets.Value("string"),
"review_text_without_quotes": datasets.Value("string"),
"review_n_quotes": datasets.Value("int32"),
"review_n_tokens": datasets.Value("int32"),
"review_rating": datasets.Value("int32"),
"review_rating0": datasets.Value("int32"),
"review_author_url": datasets.Value("string"),
"review_author_type": datasets.Value("string"),
"review_n_likes": datasets.Value("int32"),
"review_n_comments": datasets.Value("int32"),
"review_url": datasets.Value("string"),
"review_published_date": datasets.Value("string"),
"review_crawl_date": datasets.Value("string"),
"lid": datasets.Value("string"),
"lid_probability": datasets.Value("float32"),
"review_sentiment": datasets.features.ClassLabel(names=["negative", "neutral", "positive"]),
"review_sentiment_label": datasets.Value("string"),
"book_id": datasets.Value("int32"),
}
if self.config.name == "filtered_sentiment":
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_sentiment")]
elif self.config.name == "filtered_rating":
# For CrossEntropy, our classes need to start at index 0 -- not 1
features["review_rating0"] = datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"])
features["review_sentiment"] = datasets.Value("int32")
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_rating0")]
elif self.config.name == "unfiltered": # no ClassLabels in unfiltered
features["review_sentiment"] = datasets.Value("int32")
task_templates = None
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
print("AT INFO", self.config.name, task_templates)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage="https://huggingface.co/datasets/BramVanroy/hebban-reviews",
citation=_HEBBAN_REVIEWS_CITATION,
task_templates=task_templates,
license="cc-by-4.0"
)
def _split_generators(self, dl_manager):
if self.config.name.startswith("filtered"):
files = dl_manager.download_and_extract({"train": "train.jsonl.gz",
"test": "test.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": files["test"]
},
),
]
elif self.config.name == "unfiltered":
files = dl_manager.download_and_extract({"train": "unfiltered.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
]
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
def _generate_examples(self, data_file):
lines = Path(data_file).open(encoding="utf-8").readlines()
for line_idx, line in enumerate(lines):
row = json.loads(line)
yield line_idx, row
```
- finally, run `datasets-cli test ./datasets/hebban-reviews/ --save_infos --all_configs` from within the topmost `datasets` directory
## Expected results
Succeeding tests for three different configs.
## Actual results
I printed out the values that are given to `DatasetInfo` for config name and task_templates, as you can see. There, as expected, I get `unfiltered None`. I also modified datasets/info.py and added this line [at L.170](https://github.com/huggingface/datasets/blob/f5847a304aa1b38b3a3c54a8318b4df60f1299bc/src/datasets/info.py#L170):
```python
print("INTERNALLY AT INFO.PY", self.config_name, self.task_templates)
```
to my surprise, here I get `unfiltered [TextClassification(task='text-classification', text_column='review_text_without_quotes', label_column='review_sentiment')]`. So one way or another, here I suddenly see that `unfiltered` now does have a task_template -- even though that is not what is written in the data loading script, as the first print statement correctly shows.
I do not quite understand how, but it seems that the config name and task_templates get mixed.
This ultimately leads to the following error, but this trace may not be very useful in itself:
```
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\hebban-U6poXNQd\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "c:\dev\python\hebban\datasets\src\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\dev\python\hebban\datasets\src\datasets\commands\test.py", line 144, in run
builder.as_dataset()
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 899, in as_dataset
datasets = map_nested(
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 393, in map_nested
mapped = [
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 930, in _build_single_dataset
ds = self._as_dataset(
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 1006, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "c:\dev\python\hebban\datasets\src\datasets\arrow_dataset.py", line 661, in __init__
info = info.copy() if info is not None else DatasetInfo()
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 286, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 176, in __post_init__
self.task_templates = [
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 177, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "c:\dev\python\hebban\datasets\src\datasets\tasks\text_classification.py", line 22, in align_with_features
raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
ValueError: Column review_sentiment is not a ClassLabel.
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | 16 | DatasetInfo issue when testing multiple configs: mixed task_templates
## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data from unfiltered.json.gz (I'd want this without splits, just one chunk of data, but that does not seem possible?)
- filtered_sentiment: `review_sentiment` as ClassLabel, TextClassification task with `review_sentiment` as label. Gets train/test split from respective json.gz files
- filtered_rating: `review_rating0` as ClassLabel, TextClassification task with `review_rating0` as label. Gets train/test split from respective json.gz files
This might be a bit tedious to reproduce, so I am sorry, but these are the steps:
- Clone datasets -> `datasets/` and install it
- Clone `https://huggingface.co/datasets/BramVanroy/hebban-reviews` into `datasets/datasets` so that you have a new folder `datasets/datasets/hebban-reviews/`.
- Replace the HebbanReviews class with this new one:
```python
class HebbanReviews(datasets.GeneratorBasedBuilder):
"""The Hebban book reviews dataset."""
BUILDER_CONFIGS = [
HebbanReviewsConfig(
name="unfiltered",
description=_HEBBAN_REVIEWS_UNFILTERED_DESCRIPTION,
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_sentiment",
description=f"This config has the negative, neutral, and positive sentiment scores as ClassLabel in the 'review_sentiment' column.\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_rating",
description=f"This config has the 5-class ratings as ClassLabel in the 'review_rating0' column (which is a variant of 'review_rating' that starts counting from 0 instead of 1).\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
)
]
DEFAULT_CONFIG_NAME = "filtered_sentiment"
_URLS = {
"train": "train.jsonl.gz",
"test": "test.jsonl.gz",
"unfiltered": "unfiltered.jsonl.gz",
}
def _info(self):
features = {
"review_title": datasets.Value("string"),
"review_text": datasets.Value("string"),
"review_text_without_quotes": datasets.Value("string"),
"review_n_quotes": datasets.Value("int32"),
"review_n_tokens": datasets.Value("int32"),
"review_rating": datasets.Value("int32"),
"review_rating0": datasets.Value("int32"),
"review_author_url": datasets.Value("string"),
"review_author_type": datasets.Value("string"),
"review_n_likes": datasets.Value("int32"),
"review_n_comments": datasets.Value("int32"),
"review_url": datasets.Value("string"),
"review_published_date": datasets.Value("string"),
"review_crawl_date": datasets.Value("string"),
"lid": datasets.Value("string"),
"lid_probability": datasets.Value("float32"),
"review_sentiment": datasets.features.ClassLabel(names=["negative", "neutral", "positive"]),
"review_sentiment_label": datasets.Value("string"),
"book_id": datasets.Value("int32"),
}
if self.config.name == "filtered_sentiment":
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_sentiment")]
elif self.config.name == "filtered_rating":
# For CrossEntropy, our classes need to start at index 0 -- not 1
features["review_rating0"] = datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"])
features["review_sentiment"] = datasets.Value("int32")
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_rating0")]
elif self.config.name == "unfiltered": # no ClassLabels in unfiltered
features["review_sentiment"] = datasets.Value("int32")
task_templates = None
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
print("AT INFO", self.config.name, task_templates)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage="https://huggingface.co/datasets/BramVanroy/hebban-reviews",
citation=_HEBBAN_REVIEWS_CITATION,
task_templates=task_templates,
license="cc-by-4.0"
)
def _split_generators(self, dl_manager):
if self.config.name.startswith("filtered"):
files = dl_manager.download_and_extract({"train": "train.jsonl.gz",
"test": "test.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": files["test"]
},
),
]
elif self.config.name == "unfiltered":
files = dl_manager.download_and_extract({"train": "unfiltered.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
]
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
def _generate_examples(self, data_file):
lines = Path(data_file).open(encoding="utf-8").readlines()
for line_idx, line in enumerate(lines):
row = json.loads(line)
yield line_idx, row
```
- finally, run `datasets-cli test ./datasets/hebban-reviews/ --save_infos --all_configs` from within the topmost `datasets` directory
## Expected results
Succeeding tests for three different configs.
## Actual results
I printed out the values that are given to `DatasetInfo` for config name and task_templates, as you can see. There, as expected, I get `unfiltered None`. I also modified datasets/info.py and added this line [at L.170](https://github.com/huggingface/datasets/blob/f5847a304aa1b38b3a3c54a8318b4df60f1299bc/src/datasets/info.py#L170):
```python
print("INTERNALLY AT INFO.PY", self.config_name, self.task_templates)
```
to my surprise, here I get `unfiltered [TextClassification(task='text-classification', text_column='review_text_without_quotes', label_column='review_sentiment')]`. So one way or another, here I suddenly see that `unfiltered` now does have a task_template -- even though that is not what is written in the data loading script, as the first print statement correctly shows.
I do not quite understand how, but it seems that the config name and task_templates get mixed.
This ultimately leads to the following error, but this trace may not be very useful in itself:
```
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\hebban-U6poXNQd\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "c:\dev\python\hebban\datasets\src\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\dev\python\hebban\datasets\src\datasets\commands\test.py", line 144, in run
builder.as_dataset()
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 899, in as_dataset
datasets = map_nested(
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 393, in map_nested
mapped = [
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 930, in _build_single_dataset
ds = self._as_dataset(
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 1006, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "c:\dev\python\hebban\datasets\src\datasets\arrow_dataset.py", line 661, in __init__
info = info.copy() if info is not None else DatasetInfo()
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 286, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 176, in __post_init__
self.task_templates = [
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 177, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "c:\dev\python\hebban\datasets\src\datasets\tasks\text_classification.py", line 22, in align_with_features
raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
ValueError: Column review_sentiment is not a ClassLabel.
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Hi! I think this is a reasonable solution. Would you be interested in submitting a PR? | [
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https://github.com/huggingface/datasets/issues/4750 | Easily create loading script for benchmark comprising multiple huggingface datasets | Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467 | Hi,
I would like to create a loading script for a benchmark comprising multiple huggingface datasets.
The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a single interface to all the underlying datasets.
I thought about downloading the files with the load_dataset function and then providing the link to the cached file. But this seems a bit inelegant to me. What approach would you propose to do this?
Please let me know if you have any questions.
Cheers,
Joel | 29 | Easily create loading script for benchmark comprising multiple huggingface datasets
Hi,
I would like to create a loading script for a benchmark comprising multiple huggingface datasets.
The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a single interface to all the underlying datasets.
I thought about downloading the files with the load_dataset function and then providing the link to the cached file. But this seems a bit inelegant to me. What approach would you propose to do this?
Please let me know if you have any questions.
Cheers,
Joel
Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467 | [
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https://github.com/huggingface/datasets/issues/4746 | Dataset Viewer issue for yanekyuk/wikikey | The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main
Maybe the viewer can display a better message for empty datasets | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | 30 | Dataset Viewer issue for yanekyuk/wikikey
### Link
_No response_
### Description
_No response_
### Owner
_No response_
The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main
Maybe the viewer can display a better message for empty datasets | [
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] |
https://github.com/huggingface/datasets/issues/4746 | Dataset Viewer issue for yanekyuk/wikikey | OK. Closing as it's not an error. We will work on making the error message a lot clearer. | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | 18 | Dataset Viewer issue for yanekyuk/wikikey
### Link
_No response_
### Description
_No response_
### Owner
_No response_
OK. Closing as it's not an error. We will work on making the error message a lot clearer. | [
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https://github.com/huggingface/datasets/issues/4745 | Allow `list_datasets` to include private datasets | Thanks for opening this issue :)
If it can help, I think you can already use `huggingface_hub` to achieve this:
```python
>>> from huggingface_hub import HfApi
>>> [ds_info.id for ds_info in HfApi().list_datasets(use_auth_token=token) if ds_info.private]
['bigscience/xxxx', 'bigscience-catalogue-data/xxxxxxx', ... ]
```
---------
Though the latest versions of `huggingface_hub` that contain this feature are not available on python 3.6, so maybe we should first drop support for python 3.6 (see #4460) to update `list_datasets` in `datasets` as well (or we would have to copy/paste some `huggingface_hub` code) | I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually. | 84 | Allow `list_datasets` to include private datasets
I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually.
Thanks for opening this issue :)
If it can help, I think you can already use `huggingface_hub` to achieve this:
```python
>>> from huggingface_hub import HfApi
>>> [ds_info.id for ds_info in HfApi().list_datasets(use_auth_token=token) if ds_info.private]
['bigscience/xxxx', 'bigscience-catalogue-data/xxxxxxx', ... ]
```
---------
Though the latest versions of `huggingface_hub` that contain this feature are not available on python 3.6, so maybe we should first drop support for python 3.6 (see #4460) to update `list_datasets` in `datasets` as well (or we would have to copy/paste some `huggingface_hub` code) | [
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https://github.com/huggingface/datasets/issues/4745 | Allow `list_datasets` to include private datasets | Great, thanks @lhoestq the workaround works! I think it would be intuitive to have the support directly in `datasets` but it makes sense to wait given that the workaround exists :) | I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually. | 31 | Allow `list_datasets` to include private datasets
I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually.
Great, thanks @lhoestq the workaround works! I think it would be intuitive to have the support directly in `datasets` but it makes sense to wait given that the workaround exists :) | [
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https://github.com/huggingface/datasets/issues/4745 | Allow `list_datasets` to include private datasets | i also think that going forward we should replace more and more implementations inside datasets with the corresponding ones from `huggingface_hub` (same as we're doing in `transformers`) | I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually. | 27 | Allow `list_datasets` to include private datasets
I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually.
i also think that going forward we should replace more and more implementations inside datasets with the corresponding ones from `huggingface_hub` (same as we're doing in `transformers`) | [
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https://github.com/huggingface/datasets/issues/4745 | Allow `list_datasets` to include private datasets | `datasets.list_datasets` is now deprecated in favor of `huggingface_hub.list_datasets` (returns private datasets when `token` is present), so I'm closing this issue. | I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually. | 20 | Allow `list_datasets` to include private datasets
I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually.
`datasets.list_datasets` is now deprecated in favor of `huggingface_hub.list_datasets` (returns private datasets when `token` is present), so I'm closing this issue. | [
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] |
https://github.com/huggingface/datasets/issues/4744 | Remove instructions to generate dummy data from our docs | Note that for me personally, conceptually all the dummy data (even for "canonical" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones) | In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI test requiring dummy data
- there are no instructions on how they can test their dataset locally using the dummy data
- the generation of the dummy data assumes our GitHub directory structure:
- the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue:
- #4742
CC: @stevhliu | 31 | Remove instructions to generate dummy data from our docs
In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI test requiring dummy data
- there are no instructions on how they can test their dataset locally using the dummy data
- the generation of the dummy data assumes our GitHub directory structure:
- the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue:
- #4742
CC: @stevhliu
Note that for me personally, conceptually all the dummy data (even for "canonical" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones) | [
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https://github.com/huggingface/datasets/issues/4744 | Remove instructions to generate dummy data from our docs | I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.
But for now, we could at least start by removing the indication to generate dummy data from our docs. | In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI test requiring dummy data
- there are no instructions on how they can test their dataset locally using the dummy data
- the generation of the dummy data assumes our GitHub directory structure:
- the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue:
- #4742
CC: @stevhliu | 43 | Remove instructions to generate dummy data from our docs
In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI test requiring dummy data
- there are no instructions on how they can test their dataset locally using the dummy data
- the generation of the dummy data assumes our GitHub directory structure:
- the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue:
- #4742
CC: @stevhliu
I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.
But for now, we could at least start by removing the indication to generate dummy data from our docs. | [
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https://github.com/huggingface/datasets/issues/4742 | Dummy data nowhere to be found | Hi @BramVanroy, thanks for reporting.
First of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for datasets on the Hub.
- We should definitely update our docs.
Second, the dummy data is generated locally:
- in your case, the dummy data will be generated inside the directory: `./datasets/hebban-reviews/dummy`
- please note the preceding `./datasets` directory: the reason for this is that the command to generate the dummy data was specifically created for our `datasets` library, and therefore assumes our directory structure: commands are run from the root directory of our GitHub repo, and datasets scripts are under `./datasets`
| ## Describe the bug
To finalize my dataset, I wanted to create dummy data as per the guide and I ran
```shell
datasets-cli dummy_data datasets/hebban-reviews --auto_generate
```
where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs and shows a message at the end that it succeeded, I cannot find the dummy data anywhere. Where is it?
## Expected results
To see the dummy data in the datasets' folder or in the folder where I ran the command.
## Actual results
I see the following message but I cannot find the dummy data anywhere.
```
Dummy data generation done and dummy data test succeeded for config 'filtered''.
Automatic dummy data generation succeeded for all configs of '.\datasets\hebban-reviews\'
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| 139 | Dummy data nowhere to be found
## Describe the bug
To finalize my dataset, I wanted to create dummy data as per the guide and I ran
```shell
datasets-cli dummy_data datasets/hebban-reviews --auto_generate
```
where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs and shows a message at the end that it succeeded, I cannot find the dummy data anywhere. Where is it?
## Expected results
To see the dummy data in the datasets' folder or in the folder where I ran the command.
## Actual results
I see the following message but I cannot find the dummy data anywhere.
```
Dummy data generation done and dummy data test succeeded for config 'filtered''.
Automatic dummy data generation succeeded for all configs of '.\datasets\hebban-reviews\'
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Hi @BramVanroy, thanks for reporting.
First of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for datasets on the Hub.
- We should definitely update our docs.
Second, the dummy data is generated locally:
- in your case, the dummy data will be generated inside the directory: `./datasets/hebban-reviews/dummy`
- please note the preceding `./datasets` directory: the reason for this is that the command to generate the dummy data was specifically created for our `datasets` library, and therefore assumes our directory structure: commands are run from the root directory of our GitHub repo, and datasets scripts are under `./datasets`
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