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2022-03-16 15:53:59+00:00
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NaT
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NONE
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**Is your feature request related to a problem? Please describe.**
The current [Text](https://github.com/huggingface/datasets/blob/207be676bffe9d164740a41a883af6125edef135/src/datasets/packaged_modules/text/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted.
**Describe the solution you'd like**
```python
if self.config.sample_by == "line":
batch_idx = 0
while True:
batch = f.read(self.config.chunksize)
if not batch:
break
batch += f.readline() # finish current line
if self.config.custom_newline is None:
batch = batch.splitlines(keepends=self.config.keep_linebreaks)
else:
batch = batch.split(self.config.custom_newline)[:-1]
pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema)
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), pa_table
batch_idx += 1
```
**A clear and concise description of what you want to happen.**
Creating the dataset rows with a subset of the `splitlines()` line boundaries.
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Remove deprecated methods/params (preparation for v2.0)
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This PR removes the following deprecated methos/params:
* `Dataset.cast_`/`DatasetDict.cast_`
* `Dataset.dictionary_encode_column_`/`DatasetDict.dictionary_encode_column_`
* `Dataset.remove_columns_`/`DatasetDict.remove_columns_`
* `Dataset.rename_columns_`/`DatasetDict.rename_columns_`
* `prepare_module`
* param `script_version` in `load_dataset`/`load_metric`
* param `version` in `hf_github_url`
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PR_kwDODunzps4z0biM
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Release of FairLex dataset
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CONTRIBUTOR
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**FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing**
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
*Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Letizia, Sebastian Felix Schwemer, Anders Søgaard. FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing. 2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*
Note: Please review this initial commit, and I'll update the publication link, once I'll have the ArXived version. Thanks!
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[Breaking] Align `map` when streaming: update instead of overwrite + add missing parameters
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Currently the datasets in streaming mode and in non-streaming mode have two distinct API for `map` processing.
In this PR I'm aligning the two by changing `map` in streamign mode. This includes a **major breaking change** and will require a major release of the library: **Datasets 2.0**
In particular, `Dataset.map` adds new columns (with dict.update) BUT `IterableDataset.map` used to discard previous columns (it overwrites the dict). In this PR I'm chaning the `IterableDataset.map` to behave the same way as `Dataset.map`: it will update the examples instead of overwriting them.
I'm also adding those missing parameters to streaming `map`: with_indices, input_columns, remove_columns
### TODO
- [x] tests
- [x] docs
Related to https://github.com/huggingface/datasets/issues/3444
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Added computer vision tasks
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Previous PR was in my fork so thought it'd be easier if I do it from a branch. Added computer vision task datasets according to HF tasks.
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PR_kwDODunzps4zus9R
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Xtreme-S Metrics
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2022-03-01 13:42:28+00:00
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**Added datasets (TODO)**:
- [x] MLS
- [x] Covost2
- [x] Minds-14
- [x] Voxpopuli
- [x] FLoRes (need data)
**Metrics**: Done
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PR_kwDODunzps4zrl5Y
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|
Fix error message in CSV loader for newer Pandas versions
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Fix the error message in the CSV loader for `Pandas >= 1.4`. To fix this, I directly print the current file name in the for-loop. An alternative would be to use a check similar to this:
```python
csv_file_reader.handle.handle if datasets.config.PANDAS_VERSION >= version.parse("1.4") else csv_file_reader.f
```
CC: @SBrandeis
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Reddit dataset card contribution
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2022-02-28 17:53:18+00:00
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2023-03-09 22:08:58+00:00
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2022-03-01 12:58:57+00:00
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CONTRIBUTOR
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Description tags for webis-tldr-17 added.
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PR_kwDODunzps4zrOQ4
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Skip checksum computation if `ignore_verifications` is `True`
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This will speed up the loading of the datasets where the number of data files is large (can easily happen with `imagefoler`, for instance)
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can not flatten natural_questions dataset
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2022-02-27 13:57:40+00:00
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2022-03-21 14:36:12+00:00
|
2022-03-21 14:36:12+00:00
|
NONE
| null | null | null | null |
## Describe the bug
after downloading the natural_questions dataset, can not flatten the dataset considering there are `long answer` and `short answer` in `annotations`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('natural_questions',cache_dir = 'data/dataset_cache_dir')
dataset['train'].flatten()
```
## Expected results
a dataset with `long_answer` as features
## Actual results
Traceback (most recent call last):
File "temp.py", line 5, in <module>
dataset['train'].flatten()
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/fingerprint.py", line 413, in wrapper
out = func(self, *args, **kwargs)
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1296, in flatten
dataset._data = update_metadata_with_features(dataset._data, dataset.features)
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 536, in update_metadata_with_features
features = Features({col_name: features[col_name] for col_name in table.column_names})
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 536, in <dictcomp>
features = Features({col_name: features[col_name] for col_name in table.column_names})
KeyError: 'annotations.long_answer'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.13
- Platform: MBP
- Python version: 3.8
- PyArrow version: 6.0.1
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| 1,153,185,343
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PR_kwDODunzps4zniT4
| 3,794
|
Add Mahalanobis distance metric
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2022-02-27 10:56:31+00:00
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2022-03-02 14:46:15+00:00
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2022-03-02 14:46:15+00:00
|
CONTRIBUTOR
| null | null | 0
|
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Mahalanobis distance is a very useful metric to measure the distance from one datapoint X to a distribution P.
In this PR I implement the metric in a simple way with the help of numpy only.
Similar to the [MAUVE implementation](https://github.com/huggingface/datasets/blob/master/metrics/mauve/mauve.py), we can make this metric accept texts as input and encode them with a featurize model, if that is desirable.
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| 1,150,974,950
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PR_kwDODunzps4zfdL0
| 3,793
|
Docs new UI actions no self hosted
|
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2022-02-25 23:48:55+00:00
|
2022-03-01 15:55:29+00:00
|
2022-03-01 15:55:28+00:00
|
MEMBER
| null | null | 0
|
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Removes the need to have a self-hosted runner for the dev documentation
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| 1,150,812,404
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I_kwDODunzps5EmAD0
| 3,792
|
Checksums didn't match for dataset source
|
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"default": false,
"description": "Related to the dataset viewer on huggingface.co",
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2022-02-25 19:55:09+00:00
|
2024-03-13 12:25:08+00:00
|
2022-02-28 08:44:18+00:00
|
NONE
| null | null | null | null |
## Dataset viewer issue for 'wiki_lingua*'
**Link:** *link to the dataset viewer page*
`data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]")
`
*short description of the issue*
```
[NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()
```
Am I the one who added this dataset ? No
|
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PR_kwDODunzps4zevU2
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|
Add `data_dir` to `data_files` resolution and misc improvements to HfFileSystem
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2022-02-25 18:26:35+00:00
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2022-03-01 13:10:43+00:00
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2022-03-01 13:10:42+00:00
|
COLLABORATOR
| null | null | 0
|
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As discussed in https://github.com/huggingface/datasets/pull/2830#issuecomment-1048989764, this PR adds a QOL improvement to easily reference the files inside a directory in `load_dataset` using the `data_dir` param (very handy for ImageFolder because it avoids globbing, but also useful for the other loaders). Additionally, it fixes the issue with `HfFileSystem.isdir`, which would previously always return `False`, and aligns the path-handling logic in `HfFileSystem` with `fsspec.GitHubFileSystem`.
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| 1,150,646,899
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PR_kwDODunzps4zedMa
| 3,790
|
Add doc builder scripts
|
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2022-02-25 16:38:47+00:00
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2022-03-01 15:55:42+00:00
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2022-03-01 15:55:41+00:00
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I added the three scripts:
- build_dev_documentation.yml
- build_documentation.yml
- delete_dev_documentation.yml
I got them from `transformers` and did a few changes:
- I removed the `transformers`-specific dependencies
- I changed all the paths to be "datasets" instead of "transformers"
- I passed the `--library_name datasets` arg to the `doc-builder build` command (according to https://github.com/huggingface/doc-builder/pull/94/files#diff-bcc33cf7c223511e498776684a9a433810b527a0a38f483b1487e8a42b6575d3R26)
cc @LysandreJik @mishig25
|
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| 1,150,587,404
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PR_kwDODunzps4zeQpx
| 3,789
|
Add URL and ID fields to Wikipedia dataset
|
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2022-02-25 15:34:37+00:00
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2022-03-04 08:24:24+00:00
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2022-03-04 08:24:23+00:00
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This PR adds the URL field, so that we conform to proper attribution, required by their license: provide credit to the authors by including a hyperlink (where possible) or URL to the page or pages you are re-using.
About the conversion from title to URL, I found that apart from replacing blanks with underscores, some other special character must also be percent-encoded (e.g. `"` to `%22`): https://meta.wikimedia.org/wiki/Help:URL
Therefore, I have finally used `urllib.parse.quote` function. This additionally percent-encodes non-ASCII characters, but Wikimedia docs say these are equivalent:
> For the other characters either the code or the character can be used in internal and external links, they are equivalent. The system does a conversion when needed.
> [[%C3%80_propos_de_M%C3%A9ta]]
> is rendered as [À_propos_de_Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), almost like [À propos de Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), which leads to this page on Meta with in the address bar the URL
> [http://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta)
> while [http://meta.wikipedia.org/wiki/À_propos_de_Méta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta) leads to the same.
Fix #3398.
CC: @geohci
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| 1,150,375,720
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I_kwDODunzps5EkVco
| 3,788
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Only-data dataset loaded unexpectedly as validation split
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2022-02-25 12:11:39+00:00
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2022-02-28 11:22:22+00:00
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NaT
|
MEMBER
| null | null | null | null |
## Describe the bug
As reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`.
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PR_kwDODunzps4zdE7b
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Fix Google Drive URL to avoid Virus scan warning
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2022-02-25 09:35:12+00:00
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2022-03-04 20:43:32+00:00
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This PR fixes, in the datasets library instead of in every specific dataset, the issue of downloading the Virus scan warning page instead of the actual data file for Google Drive URLs.
Fix #3786, fix #3784.
|
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Bug downloading Virus scan warning page from Google Drive URLs
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2022-02-25 09:32:23+00:00
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| null | null | null | null |
## Describe the bug
Recently, some issues were reported with URLs from Google Drive, where we were downloading the Virus scan warning page instead of the data file itself.
See:
- #3758
- #3773
- #3784
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PR_kwDODunzps4zciES
| 3,785
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Fix: Bypass Virus Checks in Google Drive Links (CNN-DM dataset)
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2022-02-25 05:48:57+00:00
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2022-03-03 16:43:47+00:00
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2022-03-03 14:03:37+00:00
|
NONE
| null | null | 0
|
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This commit fixes the issue described in #3784. By adding an extra parameter to the end of Google Drive links, we are able to bypass the virus check and download the datasets.
So, if the original link looked like https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ
The new link now looks like https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ&confirm=t
Fixes #3784
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Unable to Download CNN-Dailymail Dataset
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2022-02-25 05:24:47+00:00
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2022-03-03 14:05:17+00:00
|
2022-03-03 14:05:17+00:00
|
NONE
| null | null | null | null |
## Describe the bug
I am unable to download the CNN-Dailymail dataset. Upon closer investigation, I realised why this was happening:
- The dataset sits in Google Drive, and both the CNN and DM datasets are large.
- Google is unable to scan the folder for viruses, **so the link which would originally download the dataset, now downloads the source code of this web page:**
- **This leads to the following error**:
```python
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
## Steps to reproduce the bug
```python
import datasets
dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train")
```
## Expected results
That the dataset is downloaded and processed just like other datasets.
## Actual results
Hit with this error:
```python
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 6.0.1
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PR_kwDODunzps4zZ1jR
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Support passing str to iter_files
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2022-02-24 12:58:15+00:00
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MEMBER
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PR_kwDODunzps4zY-Xb
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Error of writing with different schema, due to nonpreservation of nullability
|
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2022-02-24 08:23:07+00:00
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CONTRIBUTOR
| null | null | 0
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## 1. Case
```
dataset.map(
batched=True,
disable_nullable=True,
)
```
will get the following error at here https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/arrow_writer.py#L516
`pyarrow.lib.ArrowInvalid: Tried to write record batch with different schema`
## 2. Debugging
### 2.1 tracing
During `_map_single`, the following are called
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/arrow_dataset.py#L2523
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/arrow_writer.py#L511
### 2.2. Observation
The problem is, even after `table_cast`, `pa_table.schema != self._schema`
`pa_table.schema` (before/after `table_cast`)
```
input_ids: list<item: int32>
child 0, item: int32
```
`self._schema`
```
input_ids: list<item: int32> not null
child 0, item: int32
```
### 2.3. Reason
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/table.py#L1121
Here we lose nullability stored in `schema` because it seems that `Features` is always nullable and don't store nullability.
https://github.com/huggingface/datasets/blob/c9967f55626931f8059dc416526c791444cdfdf7/src/datasets/table.py#L1103
So, casting to schema from such `Features` loses nullability, and eventually causes error of writing with different schema
## 3. Solution
1. Let `Features` stores nullability.
2. Directly cast table with original schema but not schema from converted `Features`. (this PR)
3. Don't `cast_table` when `write_table`
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Reddit dataset card additions
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2022-02-23 21:29:16+00:00
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2022-02-28 18:00:40+00:00
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2022-02-28 11:21:14+00:00
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CONTRIBUTOR
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The changes proposed are based on the "TL;DR: Mining Reddit to Learn Automatic Summarization" paper & https://zenodo.org/record/1043504#.YhaKHpbQC38
It is a Reddit dataset indeed, but the name given to the dataset by the authors is Webis-TLDR-17 (corpus), so perhaps it should be modified as well.
The task at which the dataset is aimed is abstractive summarization.
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PR_kwDODunzps4zWVSM
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Add ElkarHizketak v1.0 dataset
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2022-02-23 14:44:17+00:00
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CONTRIBUTOR
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PR_kwDODunzps4zV4qr
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Update manual download URL in newsroom dataset
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2022-02-23 12:49:07+00:00
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2022-02-23 13:26:41+00:00
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MEMBER
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Fix #3778.
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I_kwDODunzps5Ea4xC
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Not be able to download dataset - "Newsroom"
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2022-02-23 17:05:04+00:00
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NONE
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Hello,
I tried to download the **newsroom** dataset but it didn't work out for me. it said me to **download it manually**!
For manually, Link is also didn't work! It is sawing some ad or something!
If anybody has solved this issue please help me out or if somebody has this dataset please share your google drive link, it would be a great help!
Thanks
Darshan Tank
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Start removing canonical datasets logic
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2022-02-22 18:23:30+00:00
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2022-02-24 15:04:37+00:00
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2022-02-24 15:04:36+00:00
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MEMBER
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I updated the source code and the documentation to start removing the "canonical datasets" logic.
Indeed this makes the documentation confusing and we don't want this distinction anymore in the future. Ideally users should share their datasets on the Hub directly.
### Changes
- the documentation about dataset loading mentions the datasets on the Hub (no difference between canonical and community, since they all have their own repository now)
- the documentation about adding a dataset doesn't explain the technical differences between canonical and community anymore, and only presents how to add a community dataset. There is still a small section at the bottom that mentions the datasets that are still on GitHub and redirects to the `ADD_NEW_DATASET.md` guide on GitHub about how to contribute a dataset to the `datasets` library
- the code source doesn't mention "canonical" anymore anywhere. There is still a `GitHubDatasetModuleFactory` class that is left, but I updated the docstring to say that it will be eventually removed in favor of the `HubDatasetModuleFactory` classes that already exist
Would love to have your feedbacks on this !
cc @julien-c @thomwolf @SBrandeis
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| 1,146,932,871
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I_kwDODunzps5EXM6H
| 3,776
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Allow download only some files from the Wikipedia dataset
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2022-02-22 13:46:41+00:00
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2022-02-22 14:50:02+00:00
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NaT
|
NONE
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
The Wikipedia dataset can be really big. This is a problem if you want to use it locally in a laptop with the Apache Beam `DirectRunner`. Even if your laptop have a considerable amount of memory (e.g. 32gb).
**Describe the solution you'd like**
I would like to use the `data_files` argument in the `load_dataset` function to define which file in the wikipedia dataset I would like to download. Thus, I can work with the dataset in a smaller machine using the Apache Beam `DirectRunner`.
**Describe alternatives you've considered**
I've tried to use the `simple` Wikipedia dataset. But it's in English and I would like to use Portuguese texts in my model.
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PR_kwDODunzps4zSEd4
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Update gigaword card and info
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2022-02-22 12:27:16+00:00
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2022-02-28 11:35:24+00:00
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2022-02-28 11:35:24+00:00
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COLLABORATOR
| null | null | 0
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Reported on the forum: https://discuss.huggingface.co/t/error-loading-dataset/14999
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PR_kwDODunzps4zSDHC
| 3,774
|
Fix reddit_tifu data URL
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2022-02-22 12:21:15+00:00
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Fix #3773.
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| 1,146,758,335
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I_kwDODunzps5EWiS_
| 3,773
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Checksum mismatch for the reddit_tifu dataset
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2022-02-22 10:57:07+00:00
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2022-02-25 19:27:49+00:00
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2022-02-22 12:38:44+00:00
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
A checksum occurs when downloading the reddit_tifu data (both long & short).
## Steps to reproduce the bug
reddit_tifu_dataset = load_dataset('reddit_tifu', 'long')
## Expected results
The expected result is for the dataset to be downloaded and cached locally.
## Actual results
File "/.../lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 7.0.0
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Fix: dataset name is stored in keys
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2022-02-22 10:20:37+00:00
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2022-02-22 11:08:34+00:00
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2022-02-22 11:08:33+00:00
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CONTRIBUTOR
| null | null | 0
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PR_kwDODunzps4zRHsd
| 3,771
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Fix DuplicatedKeysError on msr_sqa dataset
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MEMBER
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Fix #3770.
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I_kwDODunzps5EU7Wb
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DuplicatedKeysError on msr_sqa dataset
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2022-02-22 00:43:33+00:00
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2022-02-22 08:12:39+00:00
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2022-02-22 08:12:39+00:00
|
NONE
| null | null | null | null |
### Describe the bug
Failure to generate dataset msr_sqa because of duplicate keys.
### Steps to reproduce the bug
```
from datasets import load_dataset
load_dataset("msr_sqa")
```
### Expected results
The examples keys should be unique.
**Actual results**
```
>>> load_dataset("msr_sqa")
Downloading:
6.72k/? [00:00<00:00, 148kB/s]
Downloading:
2.93k/? [00:00<00:00, 53.8kB/s]
Using custom data configuration default
Downloading and preparing dataset msr_sqa/default (download: 4.57 MiB, generated: 26.25 MiB, post-processed: Unknown size, total: 30.83 MiB) to /root/.cache/huggingface/datasets/msr_sqa/default/0.0.0/70b2a497bd3cc8fc960a3557d2bad1eac5edde824505e15c9c8ebe4c260fd4d1...
Downloading: 100%
4.80M/4.80M [00:00<00:00, 7.49MB/s]
---------------------------------------------------------------------------
DuplicatedKeysError Traceback (most recent call last)
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator)
1080 example = self.info.features.encode_example(record)
-> 1081 writer.write(example, key)
1082 finally:
8 frames
DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: nt-639
Keys should be unique and deterministic in nature
During handling of the above exception, another exception occurred:
DuplicatedKeysError Traceback (most recent call last)
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in check_duplicate_keys(self)
449 for hash, key in self.hkey_record:
450 if hash in tmp_record:
--> 451 raise DuplicatedKeysError(key)
452 else:
453 tmp_record.add(hash)
DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: nt-639
Keys should be unique and deterministic in nature
```
### Environment info
datasets version: 1.18.3
Platform: Google colab notebook
Python version: 3.7
PyArrow version: 6.0.1
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I_kwDODunzps5EUoJn
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`dataset = dataset.map()` causes faiss index lost
|
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2022-02-21 21:59:23+00:00
|
2022-06-27 14:56:29+00:00
|
NaT
|
NONE
| null | null | null | null |
## Describe the bug
assigning the resulted dataset to original dataset causes lost of the faiss index
## Steps to reproduce the bug
`my_dataset` is a regular loaded dataset. It's a part of a customed dataset structure
```python
self.dataset.add_faiss_index('embeddings')
self.dataset.list_indexes()
# ['embeddings']
dataset2 = my_dataset.map(
lambda x: self._get_nearest_examples_batch(x['text']), batch=True
)
# the unexpected result:
dataset2.list_indexes()
# []
self.dataset.list_indexes()
# ['embeddings']
```
in case something wrong with my `_get_nearest_examples_batch()`, it's like this
```python
def _get_nearest_examples_batch(self, examples, k=5):
queries = embed(examples)
scores_batch, retrievals_batch = self.dataset.get_nearest_examples_batch(self.faiss_column, queries, k)
return {
'neighbors': [batch['text'] for batch in retrievals_batch],
'scores': scores_batch
}
```
## Expected results
`map` shouldn't drop the indexes, in another word, indexes should be carried to the generated dataset
## Actual results
map drops the indexes
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.8.12
- PyArrow version: 7.0.0
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PR_kwDODunzps4zPobl
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Fix HfFileSystem docstring
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PR_kwDODunzps4zPahh
| 3,767
|
Expose method and fix param
|
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2022-02-21 16:57:47+00:00
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2022-02-22 08:35:03+00:00
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2022-02-22 08:35:02+00:00
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COLLABORATOR
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A fix + expose a new method, following https://github.com/huggingface/datasets/pull/3670
|
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PR_kwDODunzps4zOujH
| 3,766
|
Fix head_qa data URL
|
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2022-02-21 13:52:50+00:00
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2022-02-21 14:39:20+00:00
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2022-02-21 14:39:19+00:00
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MEMBER
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Fix #3758.
|
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| 1,145,126,881
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PR_kwDODunzps4zMdIL
| 3,765
|
Update URL for tagging app
|
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2022-02-20 20:34:31+00:00
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2022-02-20 20:36:10+00:00
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2022-02-20 20:36:06+00:00
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MEMBER
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This PR updates the URL for the tagging app to be the one on Spaces.
|
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I_kwDODunzps5EQPJq
| 3,764
|
!
|
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2022-02-20 19:05:43+00:00
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2022-02-21 08:55:58+00:00
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2022-02-21 08:55:58+00:00
|
NONE
| null | null | null | null |
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
|
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I_kwDODunzps5EQNZm
| 3,763
|
It's not possible download `20200501.pt` dataset
|
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2022-02-20 18:34:58+00:00
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2022-02-21 12:06:12+00:00
|
2022-02-21 09:25:06+00:00
|
NONE
| null | null | null | null |
## Describe the bug
The dataset `20200501.pt` is broken.
The available datasets: https://dumps.wikimedia.org/ptwiki/
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("wikipedia", "20200501.pt", beam_runner='DirectRunner')
```
## Expected results
I expect to download the dataset locally.
## Actual results
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("wikipedia", "20200501.pt", beam_runner='DirectRunner')
Downloading and preparing dataset wikipedia/20200501.pt to /home/jvanz/.cache/huggingface/datasets/wikipedia/20200501.pt/1.0.0/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475...
/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/apache_beam/__init__.py:79: UserWarning: This version of Apache Beam has not been sufficiently tested on Python 3.9. You may encounter bugs or missing features.
warnings.warn(
0%| | 0/1 [00:00<?, ?it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset
builder_instance.download_and_prepare(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 1245, in _download_and_prepare
super()._download_and_prepare(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/jvanz/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475/wikipedia.py", line 420, in _split_generators
downloaded_files = dl_manager.download_and_extract({"info": info_url})
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 307, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 195, in download
downloaded_path_or_paths = map_nested(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 260, in map_nested
mapped = [
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 261, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 196, in _single_map_nested
return function(data_struct)
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 216, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 612, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/ptwiki/20200501/dumpstatus.json
```
## Environment info
```
- `datasets` version: 1.18.3
- Platform: Linux-5.3.18-150300.59.49-default-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 6.0.1
```
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I_kwDODunzps5EPQSV
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|
`Dataset.class_encode` should support custom class names
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2022-02-21 12:16:35+00:00
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2022-02-21 12:16:35+00:00
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CONTRIBUTOR
| null | null | null | null |
I can make a PR, just wanted approval before starting.
**Is your feature request related to a problem? Please describe.**
It is often the case that classes are not ordered in alphabetical order. Current `class_encode_column` sort the classes before indexing.
https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L1235
**Describe the solution you'd like**
I would like to add a **optional** parameter `class_names` to `class_encode_column` that would be used for the mapping instead of sorting the unique values.
**Describe alternatives you've considered**
One can use map instead. I find it harder to read.
```python
CLASS_NAMES = ['apple', 'orange', 'potato']
ds = ds.map(lambda item: CLASS_NAMES.index(item[label_column]))
# Proposition
ds = ds.class_encode_column(label_column, CLASS_NAMES)
```
**Additional context**
I can make the PR if this feature is accepted.
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|
Know your data for HF hub
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2022-02-21 14:15:23+00:00
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2022-02-21 14:15:23+00:00
|
NONE
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
Would be great to see be able to understand datasets with the goal of improving data quality, and helping mitigate fairness and bias issues.
**Describe the solution you'd like**
Something like https://knowyourdata.withgoogle.com/ for HF hub
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I_kwDODunzps5EPFTO
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|
Unable to view the Gradio flagged call back dataset
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2022-02-19 17:45:08+00:00
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2022-03-22 07:12:11+00:00
|
2022-03-22 07:12:11+00:00
|
NONE
| null | null | null | null |
## Dataset viewer issue for '*savtadepth-flags*'
**Link:** *[savtadepth-flags](https://huggingface.co/datasets/kingabzpro/savtadepth-flags)*
*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https://huggingface.co/spaces/kingabzpro/savtadepth.*
Am I the one who added this dataset ? Yes
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PR_kwDODunzps4zGhQu
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|
Rename GenerateMode to DownloadMode
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2022-02-18 16:53:53+00:00
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2022-02-22 13:57:24+00:00
|
2022-02-22 12:22:52+00:00
|
MEMBER
| null | null | 0
|
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This PR:
- Renames `GenerateMode` to `DownloadMode`
- Implements `DeprecatedEnum`
- Deprecates `GenerateMode`
Close #769.
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I_kwDODunzps5EJmL5
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head_qa file missing
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2022-02-18 16:32:43+00:00
|
2022-02-28 14:29:18+00:00
|
2022-02-21 14:39:19+00:00
|
COLLABORATOR
| null | null | null | null |
## Describe the bug
A file for the `head_qa` dataset is missing (https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t/HEAD_EN/train_HEAD_EN.json)
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> load_dataset("head_qa", name="en")
```
## Expected results
The dataset should be loaded
## Actual results
```
Downloading and preparing dataset head_qa/en (download: 75.69 MiB, generated: 2.69 MiB, post-processed: Unknown size, total: 78.38 MiB) to /home/slesage/.cache/huggingface/datasets/head_qa/en/1.1.0/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9...
Downloading data: 2.21kB [00:00, 2.05MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py", line 1729, in load_dataset
builder_instance.download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 665, in _download_and_prepare
verify_checksums(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t']
```
## Environment info
- `datasets` version: 1.18.4.dev0
- Platform: Linux-5.11.0-1028-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 6.0.1
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| 1,143,300,880
|
PR_kwDODunzps4zGK7p
| 3,757
|
Add perplexity to metrics
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2022-02-18 15:52:23+00:00
|
2022-02-25 17:13:34+00:00
|
2022-02-25 17:13:34+00:00
|
CONTRIBUTOR
| null | null | 0
|
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Adding perplexity metric
This code differs from the code in [this](https://huggingface.co/docs/transformers/perplexity) HF blog post because the blogpost code fails in at least the following circumstances:
- returns nans whenever the stride = 1
- hits a runtime error when the stride is significantly larger than the max model length (e.g. if max_model_length = 512 and stride = 1024)
Note that:
- As it is, it only works for causal models. Pseudoperplexity can be added later as another metric to work with masked language models.
- It takes in a list of strings so that it can be dataset independent. This does mean that it doesn't currently batch inputs, and is therefore relatively slow.
- It overwrites the metrics compute() function for a specific perplexity compute() function. This is because the current general metrics compute() function requires model-generated predictions, which doesn't make sense in the context of perplexity
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| 1,143,273,825
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I_kwDODunzps5EJPlh
| 3,756
|
Images get decoded when using `map()` with `input_columns` argument on a dataset
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2022-02-18 15:35:38+00:00
|
2022-12-13 16:59:06+00:00
|
2022-12-13 16:59:06+00:00
|
NONE
| null | null | null | null |
## Describe the bug
The `datasets.features.Image` feature class decodes image data by default. Expectedly, when indexing a dataset or using the `map()` method, images are returned as PIL Image instances.
However, when calling `map()` and setting a specific data column with the `input_columns` argument, the image data is passed as raw byte representation to the mapping function.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from torchvision import transforms
from PIL.Image import Image
dataset = load_dataset('mnist', split='train')
def transform_all_columns(example):
# example['image'] is encoded as PIL Image
assert isinstance(example['image'], Image)
return example
def transform_image_column(image):
# image is decoded here and represented as raw bytes
assert isinstance(image, Image)
return image
# single-sample dataset for debugging purposes
dev = dataset.select([0])
dev.map(transform_all_columns)
dev.map(transform_image_column, input_columns='image')
```
## Expected results
Image data should be passed in decoded form, i.e. as PIL Image objects to the mapping function unless the `decode` attribute on the image feature is set to `False`.
## Actual results
The mapping function receives images as raw byte data.
## Environment info
- `datasets` version: 1.18.3
- Platform: Linux-5.11.0-49-generic-x86_64-with-glibc2.32
- Python version: 3.8.0b4
- PyArrow version: 7.0.0
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I_kwDODunzps5EIUyB
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Cannot preview dataset
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2022-02-18 13:06:45+00:00
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2022-02-19 14:30:28+00:00
|
2022-02-18 15:41:33+00:00
|
NONE
| null | null | null | null |
## Dataset viewer issue for '*rubrix/news*'
**Link:https://huggingface.co/datasets/rubrix/news** *link to the dataset viewer page*
Cannot see the dataset preview:
```
Status code: 400
Exception: Status400Error
Message: Not found. Cache is waiting to be refreshed.
```
Am I the one who added this dataset ? No
|
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| 1,142,886,536
|
I_kwDODunzps5EHxCI
| 3,754
|
Overflowing indices in `select`
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2022-02-18 11:30:52+00:00
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2022-02-18 11:38:23+00:00
|
2022-02-18 11:38:23+00:00
|
MEMBER
| null | null | null | null |
## Describe the bug
The `Dataset.select` function seems to accept indices that are larger than the dataset size and seems to effectively use `index %len(ds)`.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"test": [1,2,3]})
ds = ds.select(range(5))
print(ds)
print()
print(ds["test"])
```
Result:
```python
Dataset({
features: ['test'],
num_rows: 5
})
[1, 2, 3, 1, 2]
```
This behaviour is not documented and can lead to unexpected behaviour when for example taking a sample larger than the dataset and thus creating a lot of duplicates.
## Expected results
It think this should throw an error or at least a very big warning:
```python
IndexError: Invalid key: 5 is out of bounds for size 3
```
## Environment info
- `datasets` version: 1.18.3
- Platform: macOS-12.0.1-x86_64-i386-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0
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| 1,142,821,144
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I_kwDODunzps5EHhEY
| 3,753
|
Expanding streaming capabilities
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2022-02-18 10:45:41+00:00
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2025-03-19 14:50:14+00:00
|
NaT
|
MEMBER
| null | null | null | null |
Some ideas for a few features that could be useful when working with large datasets in streaming mode.
## `filter` for `IterableDataset`
Adding filtering to streaming datasets would be useful in several scenarios:
- filter a dataset with many languages for a subset of languages
- filter a dataset for specific licenses
- other custom logic to get a subset
The only way to achieve this at the moment is I think through writing a custom loading script and implementing filters there.
## `IterableDataset` to `Dataset` conversion
In combination with the above filter a functionality to "play" the whole stream would be useful. The motivation is that often one might filter the dataset to get a manageable size for experimentation. In that case streaming mode is no longer necessary as the filtered dataset is small enough and it would be useful to be able to play through the whole stream to create a normal `Dataset` with all its benefits.
```python
ds = load_dataset("some_large_dataset", streaming=True)
ds_filter = ds.filter(lambda x: x["lang"]="fr")
ds_filter = ds_filter.stream() # here the `IterableDataset` is converted to a `Dataset`
```
Naturally, this could be expanded with `stream(n=1000)` which creates a `Dataset` with the first `n` elements similar to `take`.
## Stream to the Hub
While streaming allows to use a dataset as is without saving the whole dataset on the local machine it is currently not possible to process a dataset and add it to the hub. The only way to do this is by downloading the full dataset and saving the processed dataset again before pushing them to the hub. The API could looks something like:
```python
ds = load_dataset("some_large_dataset", streaming=True)
ds_filter = ds.filter(some_filter_func)
ds_processed = ds_filter.map(some_processing_func)
ds_processed.push_to_hub("new_better_dataset", batch_size=100_000)
```
Under the hood this could be done by processing and aggregating `batch_size` elements and then pushing that batch as a single file to the hub. With this functionality one could process and create TB scale datasets while only requiring size of `batch_size` local disk space.
cc @lhoestq @albertvillanova
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PR_kwDODunzps4zD1D9
| 3,752
|
Update metadata JSON for cats_vs_dogs dataset
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2022-02-18 08:32:53+00:00
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2022-02-18 14:56:12+00:00
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2022-02-18 14:56:11+00:00
|
MEMBER
| null | null | 0
|
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Note that the number of examples in the train split was already fixed in the dataset card.
Fix #3750.
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PR_kwDODunzps4zDw9_
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|
Fix typo in train split name
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2022-02-18 08:18:04+00:00
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2022-02-18 14:28:52+00:00
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2022-02-18 14:28:52+00:00
|
MEMBER
| null | null | 0
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In the README guide (and consequently in many datasets) there was a typo in the train split name:
```
| Tain | Valid | Test |
```
This PR:
- fixes the typo in the train split name
- fixes the column alignment of the split tables
in the README guide and in all datasets.
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`NonMatchingSplitsSizesError` for cats_vs_dogs dataset
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2022-02-18 14:56:11+00:00
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2022-02-18 14:56:11+00:00
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
Cannot download cats_vs_dogs dataset due to `NonMatchingSplitsSizesError`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("cats_vs_dogs")
```
## Expected results
Loading is successful.
## Actual results
```
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=7503250, num_examples=23422, dataset_name='cats_vs_dogs'), 'recorded': SplitInfo(name='train', num_bytes=7262410, num_examples=23410, dataset_name='cats_vs_dogs')}]
```
## Environment info
Reproduced on a fresh [Colab notebook](https://colab.research.google.com/drive/13GTvrSJbBGvL2ybDdXCBZwATd6FOkMub?usp=sharing).
## Additional Context
Originally reported in https://github.com/huggingface/transformers/issues/15698.
cc @mariosasko
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PR_kwDODunzps4zCKqg
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Add tqdm arguments
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2022-02-18 01:34:46+00:00
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NONE
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In this PR, tqdm arguments can be passed to the map() function and such, in order to be more flexible.
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PR_kwDODunzps4zCEyM
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Add tqdm arguments
|
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2022-02-18 00:47:55+00:00
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In this PR, there are two changes.
1. It is able to show the progress bar by adding the length of the iterator.
2. Pass in tqdm_kwargs so that can enable more feasibility for the control of tqdm library.
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I_kwDODunzps5EDMoW
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Passing invalid subset should throw an error
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2022-02-17 18:16:11+00:00
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2022-02-17 18:16:11+00:00
|
NaT
|
CONTRIBUTOR
| null | null | null | null |
## Describe the bug
Only some datasets have a subset (as in `load_dataset(name, subset)`). If you pass an invalid subset, an error should be thrown.
## Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('rotten_tomatoes', 'asdfasdfa')
```
## Expected results
This should break, since `'asdfasdfa'` isn't a subset of the `rotten_tomatoes` dataset.
## Actual results
This API call silently succeeds.
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PR_kwDODunzps4zAS-C
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Use the same seed to shuffle shards and metadata in streaming mode
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|
MEMBER
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When shuffling in streaming mode, those two entangled lists are shuffled independently. In this PR I changed this to shuffle the lists of same length with the exact same seed, in order for the files and metadata to still be aligned.
```python
gen_kwargs = {
"files": [os.path.join(data_dir, filename) for filename in all_files],
"metadata_files": [all_metadata[filename] for filename in all_files],
}
```
IMO this is important to avoid big but silent issues.
Fix https://github.com/huggingface/datasets/issues/3744
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Add mIoU metric
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This PR adds the mean Intersection-over-Union metric to the library, useful for tasks like semantic segmentation.
It is entirely based on mmseg's [implementation](https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/core/evaluation/metrics.py).
I've removed any PyTorch dependency, and rely on Numpy only.
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I_kwDODunzps5ECVCt
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|
Better shards shuffling in streaming mode
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2022-02-23 15:00:58+00:00
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2022-02-23 15:00:58+00:00
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MEMBER
| null | null | null | null |
Sometimes a dataset script has a `_split_generators` that returns several files as well as the corresponding metadata of each file. It often happens that they end up in two separate lists in the `gen_kwargs`:
```python
gen_kwargs = {
"files": [os.path.join(data_dir, filename) for filename in all_files],
"metadata_files": [all_metadata[filename] for filename in all_files],
}
```
It happened for Multilingual Spoken Words for example in #3666
However currently **the two lists are shuffled independently** when shuffling the shards in streaming mode. This leads to `_generate_examples` not having the right metadata for each file.
To prevent this issue I suggest that we always shuffle lists of the same length the exact same way to avoid such a big but silent issue.
cc @polinaeterna
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initial monash time series forecasting repository
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PR_kwDODunzps4y-1v5
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Fix ValueError message formatting in int2str
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| null | null | 0
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Hi!
I bumped into this particular `ValueError` during my work (because an instance of `np.int64` was passed instead of regular Python `int`), and so I had to `print(type(values))` myself. Apparently, it's just the missing `f` to make message an f-string.
It ain't much for a contribution, but it's honest work. Hope it spares someone else a few seconds in the future 😃
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Rm sphinx doc
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NONE
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Checklist
- [x] Update circle ci yaml
- [x] Delete sphinx static & python files in docs dir
- [x] Update readme in docs dir
- [ ] Update docs config in setup.py
|
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Support streaming for pubmed
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2022-02-17 00:18:22+00:00
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2022-02-18 14:42:13+00:00
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2022-02-18 14:42:13+00:00
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CONTRIBUTOR
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This PR makes some minor changes to the `pubmed` dataset to allow for `streaming=True`. Fixes #3739.
Basically, I followed the C4 dataset which works in streaming mode as an example, and made the following changes:
* Change URL prefix from `ftp://` to `https://`
* Explicilty `open` the filename and pass the XML contents to `etree.fromstring(xml_str)`
The Github diff tool makes it look like the changes are larger than they are, sorry about that.
I tested locally and the `pubmed` dataset now works in both normal and streaming modes. There is some overhead at the start of each shard in streaming mode as building the XML tree online is quite slow (each pubmed .xml.gz file is ~20MB), but the overhead gets amortized over all the samples in the shard. On my laptop with a single CPU worker I am able to stream at about ~600 samples/s.
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I_kwDODunzps5D-Arl
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Pubmed dataset does not work in streaming mode
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CONTRIBUTOR
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## Describe the bug
Trying to use the `pubmed` dataset with `streaming=True` fails.
## Steps to reproduce the bug
```python
import datasets
pubmed_train = datasets.load_dataset('pubmed', split='train', streaming=True)
print (next(iter(pubmed_train)))
```
## Expected results
I would expect to see the first training sample from the pubmed dataset.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/abhinav/Documents/mosaicml/mosaicml_venv/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 367, in __iter__
for key, example in self._iter():
File "/Users/abhinav/Documents/mosaicml/mosaicml_venv/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 364, in _iter
yield from ex_iterable
File "/Users/abhinav/Documents/mosaicml/mosaicml_venv/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 79, in __iter__
for key, example in self.generate_examples_fn(**self.kwargs):
File "/Users/abhinav/.cache/huggingface/modules/datasets_modules/datasets/pubmed/9715addf10c42a7877a2149ae0c5f2fddabefc775cd1bd9b03ac3f012b86ce46/pubmed.py", line 373, in _generate_examples
tree = etree.parse(filename)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/xml/etree/ElementTree.py", line 1202, in parse
tree.parse(source, parser)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/xml/etree/ElementTree.py", line 584, in parse
source = open(source, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 'gzip://pubmed21n0001.xml::ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0001.xml.gz'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.2
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.2
- PyArrow version: 6.0.0
## Comments
The error looks like an issue with `open` vs. `xopen` inside the `xml` package. It looks like it's trying to open the remote source URL, which has been edited with prefix `gzip://...`.
Maybe there can be an explicit `xopen` before passing the raw data to `etree`, something like:
```python
# Before
tree = etree.parse(filename)
root = tree.getroot()
# After
with xopen(filename) as f:
data_str = f.read()
root = etree.fromstring(data_str)
```
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I_kwDODunzps5D9Yad
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For data-only datasets, streaming and non-streaming don't behave the same
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2022-02-16 15:20:57+00:00
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2022-02-21 14:24:55+00:00
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NaT
|
COLLABORATOR
| null | null | null | null |
See https://huggingface.co/datasets/huggingface/transformers-metadata: it only contains two JSON files.
In streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:
```python
import datasets as ds
iterable_dataset = ds.load_dataset("huggingface/transformers-metadata", split="train", streaming=True);
rows = list(iterable_dataset.take(100))
rows[0]
# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}
rows[99]
# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}
```
In normal mode, an exception is thrown:
```python
import datasets as ds
dataset = ds.load_dataset("huggingface/transformers-metadata", split="train");
```
```
ValueError: Couldn't cast
model_class: string
pipeline_tag: string
auto_class: string
to
{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}
because column names don't match
```
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Make RedCaps streamable
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Make RedCaps streamable.
@lhoestq Using `data/redcaps_v1.0_annotations.zip` as a download URL gives an error locally when running `datasets-cli test` (will investigate this another time)
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Local paths in common voice
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Continuation of https://github.com/huggingface/datasets/pull/3664:
- pass the `streaming` parameter to _split_generator
- update @anton-l's code to use this parameter for `common_voice`
- add a comment to explain why we use `download_and_extract` in non-streaming and `iter_archive` in streaming
Now the `common_voice` dataset has a local path back in `ds["path"]`, and this field is `None` in streaming mode.
cc @patrickvonplaten @anton-l @albertvillanova
Fix #3663.
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Performance of `datasets` at scale
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2022-02-16 14:23:32+00:00
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2024-06-27 01:17:48+00:00
|
NaT
|
MEMBER
| null | null | null | null |
# Performance of `datasets` at 1TB scale
## What is this?
During the processing of a large dataset I monitored the performance of the `datasets` library to see if there are any bottlenecks. The insights of this analysis could guide the decision making to improve the performance of the library.
## Dataset
The dataset is a 1.1TB extract from GitHub with 120M code files and is stored as 5000 `.json.gz` files. The goal of the preprocessing is to remove duplicates and filter files based on their stats. While the calculating of the hashes for deduplication and stats for filtering can be parallelized the filtering itself is run with a single process. After processing the files are pushed to the hub.
## Machine
The experiment was run on a `m1` machine on GCP with 96 CPU cores and 1.3TB RAM.
## Performance breakdown
- Loading the data **3.5h** (_30sec_ from cache)
- **1h57min** single core loading (not sure what is going on here, corresponds to second progress bar)
- **1h10min** multi core json reading
- **20min** remaining time before and after the two main processes mentioned above
- Process the data **2h** (_20min_ from cache)
- **20min** Getting reading for processing
- **40min** Hashing and files stats (96 workers)
- **58min** Deduplication filtering (single worker)
- Save parquet files **5h**
- Saving 1000 parquet files (16 workers)
- Push to hub **37min**
- **34min** git add
- **3min** git push (several hours with `Repository.git_push()`)
## Conclusion
It appears that loading and saving the data is the main bottleneck at that scale (**8.5h**) whereas processing (**2h**) and pushing the data to the hub (**0.5h**) is relatively fast. To optimize the performance at this scale it would make sense to consider such an end-to-end example and target the bottlenecks which seem to be loading from and saving to disk. The processing itself seems to run relatively fast.
## Notes
- map operation on a 1TB dataset with 96 workers requires >1TB RAM
- map operation does not maintain 100% CPU utilization with 96 workers
- sometimes when the script crashes all the data files have a corresponding `*.lock` file in the data folder (or multiple e.g. `*.lock.lock` when it happened a several times). This causes the cache **not** to be triggered (which is significant at that scale) - i guess because there are new data files
- parallelizing `to_parquet` decreased the saving time from 17h to 5h, however adding more workers at this point had almost no effect. not sure if this is:
a) a bug in my parallelization logic,
b) i/o limit to load data form disk to memory or
c) i/o limit to write from memory to disk.
- Using `Repository.git_push()` was much slower than using command line `git-lfs` - 10-20MB/s vs. 300MB/s! The `Dataset.push_to_hub()` function is even slower as it only uploads one file at a time with only a few MB/s, whereas `Repository.git_push()` pushes files in parallel (each at a similar speed).
cc @lhoestq @julien-c @LysandreJik @SBrandeis
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PR_kwDODunzps4y7ZU2
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Fix bugs in NewsQA dataset
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2022-02-16 13:51:28+00:00
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2022-02-17 07:54:26+00:00
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Fix #3733.
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Bugs in NewsQA dataset
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2022-02-16 13:17:37+00:00
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2022-02-17 07:54:25+00:00
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2022-02-17 07:54:25+00:00
|
MEMBER
| null | null | null | null |
## Describe the bug
NewsQA dataset has the following bugs:
- the field `validated_answers` is an exact copy of the field `answers` but with the addition of `'count': [0]` to each dict
- the field `badQuestion` does not appear in `answers` nor `validated_answers`
## Steps to reproduce the bug
By inspecting the dataset script we can see that:
- the parsing of `validated_answers` is a copy-paste of the one for `answers`
- the `badQuestion` field is ignored in the parsing of both `answers` and `validated_answers`
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Support streaming in size estimation function in `push_to_hub`
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This PR adds the streamable version of `os.path.getsize` (`fsspec` can return `None`, so we fall back to `fs.open` to make it more robust) to account for possible streamable paths in the nested `extra_nbytes_visitor` function inside `push_to_hub`.
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Fix Multi-News dataset metadata and card
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Fix #3730.
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I_kwDODunzps5D7BYN
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Checksum Error when loading multi-news dataset
|
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2022-02-16 05:11:08+00:00
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2022-02-16 20:05:06+00:00
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2022-02-16 08:48:46+00:00
|
NONE
| null | null | null | null |
## Describe the bug
When using the load_dataset function from datasets module to load the Multi-News dataset, does not load the dataset but throws Checksum Error instead.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("multi_news")
```
## Expected results
Should download and load Multi-News dataset.
## Actual results
Throws the following error and cannot load data successfully:
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C']
```
Could this issue please be looked at? Thanks!
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Wrong number of examples when loading a text dataset
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2022-02-16 01:13:31+00:00
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2022-03-15 16:16:09+00:00
|
2022-03-15 16:16:09+00:00
|
NONE
| null | null | null | null |
## Describe the bug
when I use load_dataset to read a txt file I find that the number of the samples is incorrect
## Steps to reproduce the bug
```
fr = open('train.txt','r',encoding='utf-8').readlines()
print(len(fr)) # 1199637
datasets = load_dataset('text', data_files={'train': ['train.txt']}, streaming=False)
print(len(datasets['train'])) # 1199649
```
I also use command line operation to verify it
```
$ wc -l train.txt
1199637 train.txt
```
## Expected results
please fix that issue
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.3
- Platform:windows&linux
- Python version:3.7
- PyArrow version:6.0.1
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2022-02-15 23:04:55+00:00
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2022-02-16 18:49:12+00:00
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2022-02-16 18:49:12+00:00
|
CONTRIBUTOR
| null | null | null | null |
## Adding a Dataset
- **Name:** VoxPopuli
- **Description:** A Large-Scale Multilingual Speech Corpus
- **Paper:** https://arxiv.org/pdf/2101.00390.pdf
- **Data:** https://github.com/facebookresearch/voxpopuli
- **Motivation:** one of the largest (if not the largest) multilingual speech corpus: 400K hours of multilingual unlabeled speech + 17k hours of labeled speech
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
👀 @kahne @Molugan
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Patch all module attributes in its namespace
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|
MEMBER
| null | null | 0
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When patching module attributes, only those defined in its `__all__` variable were considered by default (only falling back to `__dict__` if `__all__` was None).
However those are only a subset of all the module attributes in its namespace (`__dict__` variable).
This PR fixes the problem of modules that have non-None `__all__` variable, but try to access an attribute present in `__dict__` (and not in `__all__`).
For example, `pandas` has attribute `__version__` only present in `__dict__`.
- Before version 1.4, pandas `__all__` was None, thus all attributes in `__dict__` were patched
- From version 1.4, pandas `__all__` is not None, thus attributes in `__dict__` not present in `__all__` are ignored
Fix #3724.
CC: @severo @lvwerra
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Use config pandas version in CSV dataset builder
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Fix #3724.
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Pin pandas to avoid bug in streaming mode
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Temporarily pin pandas version to avoid bug in streaming mode (patching no longer works).
Related to #3724.
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Bug while streaming CSV dataset with pandas 1.4
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## Describe the bug
If we upgrade to pandas `1.4`, the patching of the pandas module is no longer working
```
AttributeError: '_PatchedModuleObj' object has no attribute '__version__'
```
## Steps to reproduce the bug
```
pip install pandas==1.4
```
```python
from datasets import load_dataset
ds = load_dataset("lvwerra/red-wine", split="train", streaming=True)
item = next(iter(ds))
item
```
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Fix flatten of complex feature types
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Fix `flatten` for the following feature types: Image/Audio, Translation, and TranslationVariableLanguages.
Inspired by `cast`/`table_cast`, I've introduced a `table_flatten` function to handle the Image/Audio types.
CC: @SBrandeis
Fix #3686.
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added electricity load diagram dataset
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Initial Electricity Load Diagram time series dataset.
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PR_kwDODunzps4yzXCd
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Multi-GPU support for `FaissIndex`
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Per #3716 , current implementation does not take into consideration that `faiss` can run on multiple GPUs.
In this commit, I provided multi-GPU support for `FaissIndex` by modifying the device management in `IndexableMixin.add_faiss_index` and `FaissIndex.load`.
Now users are able to pass in
1. a positive integer (as usual) to use 1 GPU
2. a negative integer `-1` to use all GPUs
3. a list of integers e.g. `[0, 1]` to run only on those GPUs
4. Of course, passing in nothing still runs on CPU.
This closes: #3716
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I_kwDODunzps5DzXA4
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Builder Configuration Update Required on Common Voice Dataset
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2022-02-14 16:21:41+00:00
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2024-04-28 18:03:08+00:00
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2024-04-28 18:03:08+00:00
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NONE
| null | null | null | null |
Missing language in Common Voice dataset
**Link:** https://huggingface.co/datasets/common_voice
I tried to call the Urdu dataset using `load_dataset("common_voice", "ur", split="train+validation")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:
https://github.com/huggingface/datasets/blob/master/datasets/common_voice/common_voice.py
and Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.
Am I the one who added this dataset? No
|
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PR_kwDODunzps4yyFv7
| 3,719
|
Check if indices values in `Dataset.select` are within bounds
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2022-02-14 12:31:41+00:00
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2022-02-14 19:19:22+00:00
|
2022-02-14 19:19:22+00:00
|
COLLABORATOR
| null | null | 0
|
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Fix #3707
Instead of reusing `_check_valid_index_key` from `datasets.formatting`, I defined a new function to provide a more meaningful error message.
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PR_kwDODunzps4yx8r2
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Fix Evidence Infer Treatment dataset
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2022-02-14 11:58:07+00:00
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2022-02-14 13:21:45+00:00
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2022-02-14 13:21:44+00:00
|
MEMBER
| null | null | 0
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This PR:
- fixes a bug in the script, by removing an unnamed column with the row index: fix KeyError
- fix the metadata JSON, by adding both configurations (1.1 and 2.0): fix ExpectedMoreDownloadedFiles
- updates the dataset card
Fix #3515.
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I_kwDODunzps5DyAkn
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|
wrong condition in `Features ClassLabel encode_example`
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2022-02-14 11:44:35+00:00
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2022-02-14 15:09:36+00:00
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2022-02-14 15:07:43+00:00
|
NONE
| null | null | null | null |
## Describe the bug
The `encode_example` function in *features.py* seems to have a wrong condition.
```python
if not -1 <= example_data < self.num_classes:
raise ValueError(f"Class label {example_data:d} greater than configured num_classes {self.num_classes}")
```
## Expected results
The `not - 1` condition change the result of the condition. For instance, if `example_data` equals 4 and ` self.num_classes` equals 4 too, `example_data < self.num_classes` will give `False` as expected . But if i add the `not - 1` condition, `not -1 <= example_data < self.num_classes` will give `True` and raise an exception.
## Environment info
- `datasets` version: 1.18.3
- Python version: 3.8.10
- PyArrow version: 7.00
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| 1,136,831,092
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I_kwDODunzps5Dwqp0
| 3,716
|
`FaissIndex` to support multiple GPU and `custom_index`
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2022-03-07 16:28:56+00:00
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2022-03-07 16:28:56+00:00
|
CONTRIBUTOR
| null | null | null | null |
**Is your feature request related to a problem? Please describe.**
Currently, because `device` is of the type `int | None`, to leverage `faiss-gpu`'s multi-gpu support, you need to create a `custom_index`. However, if using a `custom_index` created by e.g. `faiss.index_cpu_to_all_gpus`, then `FaissIndex.save` does not work properly because it checks the device id (which is an int, so no multiple GPUs).
**Describe the solution you'd like**
I would like `FaissIndex` to support multiple GPUs, by passing in a list to `add_faiss_index`.
**Describe alternatives you've considered**
Alternatively, I would like it to at least provide a warning cause it wasn't the behavior that I expected.
**Additional context**
Relavent source code here:
https://github.com/huggingface/datasets/blob/6ed6ac9448311930557810383d2cfd4fe6aae269/src/datasets/search.py#L340-L349
Device management needs changing to support multiple GPUs, probably by `isinstance` calls.
I can provide a PR if you like :)
Thanks for reading!
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Fix bugs in msr_sqa dataset
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| null | null | 0
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The last version has many problems,
1) Errors in table load-in. Split by a single comma instead of using pandas is wrong.
2) id reduplicated in _generate_examples function.
3) Missing information of history questions which make it hard to use.
I fix it refer to https://github.com/HKUNLP/UnifiedSKG. And we test it to perform normally.
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I_kwDODunzps5Dt5g6
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tatoeba_mt: File not found error and key error
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NONE
| null | null | null | null |
## Dataset viewer issue for 'tatoeba_mt'
**Link:** https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt
My data loader script does not seem to work.
The files are part of the local repository but cannot be found. An example where it should work is the subset for "afr-eng".
Another problem is that I do not have validation data for all subsets and I don't know how to properly check whether validation exists in the configuration before I try to download it. An example is the subset for "afr-deu".
Am I the one who added this dataset ? Yes
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Rm sphinx doc
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NONE
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Checklist
- [x] Update circle ci yaml
- [x] Delete sphinx static & python files in docs dir
- [x] Update readme in docs dir
- [ ] Update docs config in setup.py
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PR_kwDODunzps4ynVYy
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Fix the error of msr_sqa dataset
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2022-02-13 11:21:05+00:00
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Fix the error of _load_table_data function in msr_sqa dataset, it is wrong to use comma to split each row.
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PR_kwDODunzps4ymmlK
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Fix the error of _load_table_data function in msr_sqa dataset
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2022-02-12 13:20:53+00:00
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The _load_table_data function from the last version is wrong, it is wrong to use comma to split each row.
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Fix CI code quality issue
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Fix CI code quality issue introduced by #3695.
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PR_kwDODunzps4yi0J4
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Set base path to hub url for canonical datasets
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MEMBER
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This should allow canonical datasets to use relative paths to download data files from the Hub
cc @polinaeterna this will be useful if we have audio datasets that are canonical and for which you'd like to host data files
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I_kwDODunzps5Dh7nS
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Loading JSON gets stuck with many workers/threads
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NaT
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MEMBER
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## Describe the bug
Loading a JSON dataset with `load_dataset` can get stuck when running on a machine with many CPUs. This is especially an issue when loading a large dataset on a large machine.
## Steps to reproduce the bug
I originally created the following script to reproduce the issue:
```python
from datasets import load_dataset
from multiprocessing import Process
from tqdm import tqdm
import datasets
from transformers import set_seed
def run_tasks_in_parallel(tasks, ds_list):
for _ in tqdm(range(1000)):
print('new batch')
running_tasks = [Process(target=task, args=(ds, i)) for i, (task, ds) in enumerate(zip(tasks, ds_list))]
for running_task in running_tasks:
running_task.start()
for running_task in running_tasks:
running_task.join()
def get_dataset():
dataset_name = 'transformersbook/codeparrot'
ds = load_dataset(dataset_name+'-train', split="train", streaming=True)
ds = ds.shuffle(buffer_size=1000, seed=1)
return iter(ds)
def get_next_element(ds, process_id, N=10000):
for _ in range(N):
_ = next(ds)['content']
print(f'process {process_id} done')
return
set_seed(1)
datasets.utils.logging.set_verbosity_debug()
n_processes = 8
tasks = [get_next_element for _ in range(n_processes)]
args = [get_dataset() for _ in range(n_processes)]
run_tasks_in_parallel(tasks, args)
```
Today I noticed that it can happen when running it on a single process on a machine with many cores without streaming. So just `load_dataset("transformersbook/codeparrot-train")` alone might cause the issue after waiting long enough or trying many times. It's a slightly random process which makes it especially hard to track down. When I encountered it today it had already processed 17GB of data (the size of the cache folder when it got stuck) before getting stuck.
Here's my current understanding of the error. As far as I can tell it happens in the following block: https://github.com/huggingface/datasets/blob/be701e9e89ab38022612c7263edc015bc7feaff9/src/datasets/packaged_modules/json/json.py#L119-L139
When the try on line 121 fails and the `block_size` is increased it can happen that it can't read the JSON again and gets stuck indefinitely. A hint that points in that direction is that increasing the `chunksize` argument decreases the chance of getting stuck and vice versa. Maybe it is an issue with a lock on the file that is not properly released.
## Expected results
Read a JSON before the end of the universe.
## Actual results
Read a JSON not before the end of the universe.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.10
- PyArrow version: 7.0.0
@lhoestq we dicsussed this a while ago. @albertvillanova we discussed this today :)
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I_kwDODunzps5DhEUP
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`.select`: unexpected behavior with `indices`
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|
2022-02-11 15:20:01+00:00
|
2022-02-14 19:19:21+00:00
|
2022-02-14 19:19:21+00:00
|
NONE
| null | null | null | null |
## Describe the bug
The `.select` method will not throw when sending `indices` bigger than the dataset length; `indices` will be wrapped instead. This behavior is not documented anywhere, and is not intuitive.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"text": ["d", "e", "f"], "label": [4, 5, 6]})
res1 = ds.select([1, 2, 3])['text']
res2 = ds.select([1000])['text']
```
## Expected results
Both results should throw an `Error`.
## Actual results
`res1` will give `['e', 'f', 'd']`
`res2` will give `['e']`
## Environment info
Bug found from this environment:
- `datasets` version: 1.16.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.7
- PyArrow version: 6.0.1
It was also replicated on `master`.
|
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| 1,132,218,874
|
I_kwDODunzps5DfEn6
| 3,706
|
Unable to load dataset 'big_patent'
|
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[] | null | 5
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2022-02-11 09:48:34+00:00
|
2022-02-14 15:26:03+00:00
|
2022-02-14 15:26:03+00:00
|
NONE
| null | null | null | null |
## Describe the bug
Unable to load the "big_patent" dataset
## Steps to reproduce the bug
```python
load_dataset('big_patent', 'd', 'validation')
```
## Expected results
Download big_patents' validation split from the 'd' subset
## Getting an error saying:
{FileNotFoundError}Local file ..\huggingface\datasets\downloads\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\bigPatentData\train.tar.gz doesn't exist
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:1.18.3
- Platform: Windows
- Python version:3.8
- PyArrow version:7.0.0
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| 1,132,053,226
|
PR_kwDODunzps4yfhyj
| 3,705
|
Raise informative error when loading a save_to_disk dataset
|
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[] | null | 0
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2022-02-11 08:21:03+00:00
|
2022-02-11 22:56:40+00:00
|
2022-02-11 22:56:39+00:00
|
MEMBER
| null | null | 0
|
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People recurrently report error when trying to load a dataset (using `load_dataset`) that was previously saved using `save_to_disk`.
This PR raises an informative error message telling them they should use `load_from_disk` instead.
Close #3700.
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