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https://api.github.com/repos/huggingface/datasets/issues/2054 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2054/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2054/comments | https://api.github.com/repos/huggingface/datasets/issues/2054/events | https://github.com/huggingface/datasets/issues/2054 | 831,597,665 | MDU6SXNzdWU4MzE1OTc2NjU= | 2,054 | Could not find file for ZEST dataset | {
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"name": "dataset bug",
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library"
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] | closed | false | null | [] | null | [
"The zest dataset url was changed (allenai/zest#3) and #2057 should resolve this.",
"This has been fixed in #2057 by @matt-peters (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)",
"Thanks @lhoestq and @matt-peters ",
"I am closing this issue since its ... | 1,615,799,518,000 | 1,620,034,224,000 | 1,620,034,224,000 | CONTRIBUTOR | null | null | null | I am trying to use zest dataset from Allen AI using below code in colab,
```
!pip install -q datasets
from datasets import load_dataset
dataset = load_dataset("zest")
```
I am getting the following error,
```
Using custom data configuration default
Downloading and preparing dataset zest/default (download: 5.53 MiB, generated: 19.96 MiB, post-processed: Unknown size, total: 25.48 MiB) to /root/.cache/huggingface/datasets/zest/default/0.0.0/1f7a230fbfc964d979bbca0f0130fbab3259fce547ee758ad8aa4f9c9bec6cca...
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-6-18dbbc1a4b8a> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("zest")
9 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
612 )
613 elif response is not None and response.status_code == 404:
--> 614 raise FileNotFoundError("Couldn't find file at {}".format(url))
615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
616 raise ConnectionError("Couldn't reach {}".format(url))
FileNotFoundError: Couldn't find file at https://ai2-datasets.s3-us-west-2.amazonaws.com/zest/zest.zip
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2053/comments | https://api.github.com/repos/huggingface/datasets/issues/2053/events | https://github.com/huggingface/datasets/pull/2053 | 831,151,728 | MDExOlB1bGxSZXF1ZXN0NTkyNTM4ODY2 | 2,053 | Add bAbI QA tasks | {
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"Hi @lhoestq,\r\n\r\nShould I remove the 160 configurations? Is it too much?\r\n\r\nEDIT:\r\nCan you also check the task category? I'm not sure if there is an appropriate tag for the same.",
"Thanks for the changes !\r\n\r\n> Should I remove the 160 configurations? Is it too much?\r\n\r\nYea 160 configuration is ... | 1,615,727,079,000 | 1,617,021,708,000 | 1,617,021,708,000 | CONTRIBUTOR | null | false | {
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} | - **Name:** *The (20) QA bAbI tasks*
- **Description:** *The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. The aim is to classify these tasks into skill sets,so that researchers can identify (and then rectify) the failings of their systems.*
- **Paper:** [arXiv](https://arxiv.org/pdf/1502.05698.pdf)
- **Data:** [Facebook Research Page](https://research.fb.com/downloads/babi/)
- **Motivation:** This is a unique dataset with story-based Question Answering. It is a part of the `bAbI` project by Facebook Research.
**Note**: I have currently added all the 160 configs. If this seems impractical, I can keep only a few. While each `dummy_data.zip` weighs a few KBs, overall it is around 1.3MB for all configurations. This is problematic. Let me know what is to be done.
Thanks :)
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
| {
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https://api.github.com/repos/huggingface/datasets/issues/2052 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2052/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2052/comments | https://api.github.com/repos/huggingface/datasets/issues/2052/events | https://github.com/huggingface/datasets/issues/2052 | 831,135,704 | MDU6SXNzdWU4MzExMzU3MDQ= | 2,052 | Timit_asr dataset repeats examples | {
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"Hi,\r\n\r\nthis was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: \r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n```",
"Ty!"
] | 1,615,722,223,000 | 1,615,804,636,000 | 1,615,804,636,000 | NONE | null | null | null | Summary
When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same
Steps to reproduce
As an example, on this code there is the text from the training part:
Code snippet:
```
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
timit['train']['text']
#['Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
```
The same behavior happens for other columns
Expected behavior:
Different info on the actual timit_asr dataset
Actual behavior:
When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same. I've checked datasets 1.3 and the rows are different
Debug info
Streamlit version: (get it with $ streamlit version)
Python version: Python 3.6.12
Using Conda? PipEnv? PyEnv? Pex? Using pip
OS version: Centos-release-7-9.2009.1.el7.centos.x86_64
Additional information
You can check the same behavior on https://huggingface.co/datasets/viewer/?dataset=timit_asr | {
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"Hi @lhoestq,\r\n\r\nI have added changes from review.",
"Thanks for approving :)"
] | 1,615,680,065,000 | 1,616,152,544,000 | 1,616,149,919,000 | CONTRIBUTOR | null | false | {
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} | - **Name:** *MDD Dataset*
- **Description:** The Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion), from various movie reviews sources such as MovieLens and OMDb.
- **Paper:** [arXiv](https://arxiv.org/pdf/1511.06931.pdf)
- **Data:** https://research.fb.com/downloads/babi/
- **Motivation:** This is one of the popular dialog datasets, a part of Facebook Research's "bAbI project".
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
**Note**: I haven't included the following from the data files: `entities` (the file containing list of all entities in the first three subtasks), `dictionary`(the dictionary of words they use in their models), `movie_kb`(contains the knowledge base of information about the movies, actors and other entities that are mentioned in the dialogs). Please let me know if those are needed, and if yes, should I make separate configurations for them? | {
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https://api.github.com/repos/huggingface/datasets/issues/2050 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2050/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2050/comments | https://api.github.com/repos/huggingface/datasets/issues/2050/events | https://github.com/huggingface/datasets/issues/2050 | 831,006,551 | MDU6SXNzdWU4MzEwMDY1NTE= | 2,050 | Build custom dataset to fine-tune Wav2Vec2 | {
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"@lhoestq - We could simply use the \"general\" json dataset for this no? ",
"Sure you can use the json loader\r\n```python\r\ndata_files = {\"train\": \"path/to/your/train_data.json\", \"test\": \"path/to/your/test_data.json\"}\r\ntrain_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\n... | 1,615,672,870,000 | 1,615,800,448,000 | 1,615,800,448,000 | NONE | null | null | null | Thank you for your recent tutorial on how to finetune Wav2Vec2 on a custom dataset. The example you gave here (https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) was on the CommonVoice dataset. However, what if I want to load my own dataset? I have a manifest (transcript and their audio files) in a JSON file.
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https://api.github.com/repos/huggingface/datasets/issues/2049 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2049/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2049/comments | https://api.github.com/repos/huggingface/datasets/issues/2049/events | https://github.com/huggingface/datasets/pull/2049 | 830,978,687 | MDExOlB1bGxSZXF1ZXN0NTkyNDE2MzQ0 | 2,049 | Fix text-classification tags | {
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"LGTM, thanks for fixing."
] | 1,615,665,102,000 | 1,615,909,666,000 | 1,615,909,666,000 | CONTRIBUTOR | null | false | {
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} | There are different tags for text classification right now: `text-classification` and `text_classification`:
.
This PR fixes it.
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https://api.github.com/repos/huggingface/datasets/issues/2048 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2048/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2048/comments | https://api.github.com/repos/huggingface/datasets/issues/2048/events | https://github.com/huggingface/datasets/issues/2048 | 830,953,431 | MDU6SXNzdWU4MzA5NTM0MzE= | 2,048 | github is not always available - probably need a back up | {
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```
:/tmp$ wget https://raw.githubusercontent.com/huggingface/datasets/1.4.1/metrics/sacrebleu/sacrebleu.py--2021-03-12 18:35:59-- https://raw.githubusercontent.com/huggingface/datasets/1.4.1/metrics/sacrebleu/sacrebleu.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 500 Internal Server Error
2021-03-12 18:36:11 ERROR 500: Internal Server Error.
```
Suggestion: have a failover system and replicate the data on another system and reach there if gh isn't reachable? perhaps gh can be a master and the replicate a slave - so there is only one true source. | {
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https://api.github.com/repos/huggingface/datasets/issues/2047 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2047/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2047/comments | https://api.github.com/repos/huggingface/datasets/issues/2047/events | https://github.com/huggingface/datasets/pull/2047 | 830,626,430 | MDExOlB1bGxSZXF1ZXN0NTkyMTI2NzQ3 | 2,047 | Multilingual dIalogAct benchMark (miam) | {
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"Hello. All aforementioned changes have been made. I've also re-run black on miam.py. :-)",
"I will run isort again. Hopefully it resolves the current check_code_quality test failure.",
"Once the review period is over, feel free to open a PR to add all the missing information ;)",
"Hi! I will follow up right ... | 1,615,590,175,000 | 1,616,495,794,000 | 1,616,150,833,000 | CONTRIBUTOR | null | false | {
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} | My collaborators (@EmileChapuis, @PierreColombo) and I within the Affective Computing team at Telecom Paris would like to anonymously publish the miam dataset. It is assocated with a publication currently under review. We will update the dataset with full citations once the review period is over. | {
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https://api.github.com/repos/huggingface/datasets/issues/2046 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2046/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2046/comments | https://api.github.com/repos/huggingface/datasets/issues/2046/events | https://github.com/huggingface/datasets/issues/2046 | 830,423,033 | MDU6SXNzdWU4MzA0MjMwMzM= | 2,046 | add_faisis_index gets very slow when doing it interatively | {
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"I think faiss automatically sets the number of threads to use to build the index.\r\nCan you check how many CPU cores are being used when you build the index in `use_own_knowleldge_dataset` as compared to this script ? Are there other programs running (maybe for rank>0) ?",
"Hi,\r\n I am running the add_faiss_in... | 1,615,580,838,000 | 1,616,624,951,000 | 1,616,624,951,000 | NONE | null | null | null | As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster?
@lhoestq
```
def training_step(self, batch, batch_idx) -> Dict:
if (not batch_idx==0) and (batch_idx%5==0):
print("******************************************************")
ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder
model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU
model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff
list_of_gpus = ['cuda:2','cuda:3']
c_dir='/custom/cache/dir'
kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir)
print(kb_dataset)
n=len(list_of_gpus) #nunber of dedicated GPUs
kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]
#kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir')
print(self.trainer.global_rank)
dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])
output = [None for _ in list_of_gpus]
#self.trainer.accelerator_connector.accelerator.barrier("embedding_process")
dist.all_gather_object(output, dataset_shards)
#This creation and re-initlaization of the new index
if (self.trainer.global_rank==0): #saving will be done in the main process
combined_dataset = concatenate_datasets(output)
passages_path =self.config.passages_path
logger.info("saving the dataset with ")
#combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage')
combined_dataset.save_to_disk(passages_path)
logger.info("Add faiss index to the dataset that consist of embeddings")
embedding_dataset=combined_dataset
index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)
embedding_dataset.add_faiss_index("embeddings", custom_index=index)
embedding_dataset.get_index("embeddings").save(self.config.index_path)
| {
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https://api.github.com/repos/huggingface/datasets/issues/2045 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2045/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2045/comments | https://api.github.com/repos/huggingface/datasets/issues/2045/events | https://github.com/huggingface/datasets/pull/2045 | 830,351,527 | MDExOlB1bGxSZXF1ZXN0NTkxODc2Mjcz | 2,045 | Preserve column ordering in Dataset.rename_column | {
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"Not sure why CI isn't triggered.\r\n\r\n@lhoestq Can you please help me with this? ",
"I don't know how to trigger it manually, but an empty commit should do the job"
] | 1,615,573,607,000 | 1,615,906,085,000 | 1,615,905,305,000 | CONTRIBUTOR | null | false | {
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} | Currently `Dataset.rename_column` doesn't necessarily preserve the order of the columns:
```python
>>> from datasets import Dataset
>>> d = Dataset.from_dict({'sentences': ["s1", "s2"], 'label': [0, 1]})
>>> d
Dataset({
features: ['sentences', 'label'],
num_rows: 2
})
>>> d.rename_column('sentences', 'text')
Dataset({
features: ['label', 'text'],
num_rows: 2
})
```
This PR fixes this. | {
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https://api.github.com/repos/huggingface/datasets/issues/2044 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2044/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2044/comments | https://api.github.com/repos/huggingface/datasets/issues/2044/events | https://github.com/huggingface/datasets/pull/2044 | 830,339,905 | MDExOlB1bGxSZXF1ZXN0NTkxODY2NzM1 | 2,044 | Add CBT dataset | {
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"Hi @lhoestq,\r\n\r\nI have added changes from the review.",
"Thanks for approving @lhoestq "
] | 1,615,572,259,000 | 1,616,152,213,000 | 1,616,149,755,000 | CONTRIBUTOR | null | false | {
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} | This PR adds the [CBT Dataset](https://arxiv.org/abs/1511.02301).
Note that I have also added the `raw` dataset as a separate configuration. I couldn't find a suitable "task" for it in YAML tags.
The dummy files have one example each, as the examples are slightly big. For `raw` dataset, I just used top few lines, because they are entire books and would take up a lot of space.
Let me know in case of any issues. | {
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https://api.github.com/repos/huggingface/datasets/issues/2043 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2043/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2043/comments | https://api.github.com/repos/huggingface/datasets/issues/2043/events | https://github.com/huggingface/datasets/pull/2043 | 830,279,098 | MDExOlB1bGxSZXF1ZXN0NTkxODE1ODAz | 2,043 | Support pickle protocol for dataset splits defined as ReadInstruction | {
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"@lhoestq But we don't perform conversion to a `NamedSplit` if `_split` is not a string which means it **will** be a `ReadInstruction` after reloading.",
"Yes right ! I read it wrong.\r\nPerfect then"
] | 1,615,566,911,000 | 1,615,904,738,000 | 1,615,903,505,000 | CONTRIBUTOR | null | false | {
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} | Fixes #2022 (+ some style fixes) | {
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https://api.github.com/repos/huggingface/datasets/issues/2042 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2042/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2042/comments | https://api.github.com/repos/huggingface/datasets/issues/2042/events | https://github.com/huggingface/datasets/pull/2042 | 830,190,276 | MDExOlB1bGxSZXF1ZXN0NTkxNzQwNzQ3 | 2,042 | Fix arrow memory checks issue in tests | {
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} | The tests currently fail on `master` because the arrow memory verification doesn't return the expected memory evolution when loading an arrow table in memory.
From my experiments, the tests fail only when the full test suite is ran.
This made me think that maybe some arrow objects from other tests were not freeing their memory until they do and cause the memory verifications to fail in other tests.
Collecting the garbage collector before checking the arrow memory usage seems to fix this issue.
I added a context manager `assert_arrow_memory_increases` that we can use in tests and that deals with the gc. | {
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https://api.github.com/repos/huggingface/datasets/issues/2041 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2041/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2041/comments | https://api.github.com/repos/huggingface/datasets/issues/2041/events | https://github.com/huggingface/datasets/pull/2041 | 830,180,803 | MDExOlB1bGxSZXF1ZXN0NTkxNzMyNzMw | 2,041 | Doc2dial update data_infos and data_loaders | {
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https://api.github.com/repos/huggingface/datasets/issues/2040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2040/comments | https://api.github.com/repos/huggingface/datasets/issues/2040/events | https://github.com/huggingface/datasets/issues/2040 | 830,169,387 | MDU6SXNzdWU4MzAxNjkzODc= | 2,040 | ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk | {
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"Hi ! To help me understand the situation, can you print the values of `load_from_disk(PATH_DATA_CLS_A)['train']._indices_data_files` and `load_from_disk(PATH_DATA_CLS_B)['train']._indices_data_files` ?\r\nThey should both have a path to an arrow file\r\n\r\nAlso note that from #2025 concatenating datasets will no... | 1,615,559,220,000 | 1,628,100,043,000 | 1,628,100,043,000 | NONE | null | null | null | Hi there,
I am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):
```python
concatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])
```
Yielding the following error:
```python
ValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.
However datasets' indices [1] come from memory and datasets' indices [0] come from disk.
```
Been trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...
`load_from_disk(PATH_DATA_CLS_A)['train']` yields:
```python
Dataset({
features: ['labels', 'text'],
num_rows: 785
})
```
`load_from_disk(PATH_DATA_CLS_B)['train']` yields:
```python
Dataset({
features: ['labels', 'text'],
num_rows: 3341
})
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2039 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2039/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2039/comments | https://api.github.com/repos/huggingface/datasets/issues/2039/events | https://github.com/huggingface/datasets/pull/2039 | 830,047,652 | MDExOlB1bGxSZXF1ZXN0NTkxNjE3ODY3 | 2,039 | Doc2dial rc | {
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} | Added fix to handle the last turn that is a user turn. | {
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https://api.github.com/repos/huggingface/datasets/issues/2038 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2038/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2038/comments | https://api.github.com/repos/huggingface/datasets/issues/2038/events | https://github.com/huggingface/datasets/issues/2038 | 830,036,875 | MDU6SXNzdWU4MzAwMzY4NzU= | 2,038 | outdated dataset_infos.json might fail verifications | {
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"Hi ! Thanks for reporting.\r\n\r\nTo update the dataset_infos.json you can run:\r\n```\r\ndatasets-cli test ./datasets/doc2dial --all_configs --save_infos --ignore_verifications\r\n```",
"Fixed by #2041, thanks again @songfeng !"
] | 1,615,549,314,000 | 1,615,912,060,000 | 1,615,912,060,000 | CONTRIBUTOR | null | null | null | The [doc2dial/dataset_infos.json](https://github.com/huggingface/datasets/blob/master/datasets/doc2dial/dataset_infos.json) is outdated. It would fail data_loader when verifying download checksum etc..
Could you please update this file or point me how to update this file?
Thank you. | {
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https://api.github.com/repos/huggingface/datasets/issues/2037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2037/comments | https://api.github.com/repos/huggingface/datasets/issues/2037/events | https://github.com/huggingface/datasets/pull/2037 | 829,919,685 | MDExOlB1bGxSZXF1ZXN0NTkxNTA4MTQz | 2,037 | Fix: Wikipedia - save memory by replacing root.clear with elem.clear | {
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"The error you got is minor and appeared in the last version of pyarrow, we'll fix the CI to take this into account. You can ignore it"
] | 1,615,540,920,000 | 1,616,479,696,000 | 1,615,892,482,000 | CONTRIBUTOR | null | false | {
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} | see: https://github.com/huggingface/datasets/issues/2031
What I did:
- replace root.clear with elem.clear
- remove lines to get root element
- $ make style
- $ make test
- some tests required some pip packages, I installed them.
test results on origin/master and my branch are same. I think it's not related on my modification, isn't it?
```
==================================================================================== short test summary info ====================================================================================
FAILED tests/test_arrow_writer.py::TypedSequenceTest::test_catch_overflow - AssertionError: OverflowError not raised
============================================================= 1 failed, 2332 passed, 5138 skipped, 70 warnings in 91.75s (0:01:31) ==============================================================
make: *** [Makefile:19: test] Error 1
```
Is there anything else I should do? | {
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https://api.github.com/repos/huggingface/datasets/issues/2036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2036/comments | https://api.github.com/repos/huggingface/datasets/issues/2036/events | https://github.com/huggingface/datasets/issues/2036 | 829,909,258 | MDU6SXNzdWU4Mjk5MDkyNTg= | 2,036 | Cannot load wikitext | {
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"Solved!"
] | 1,615,540,179,000 | 1,615,797,902,000 | 1,615,797,884,000 | NONE | null | null | null | when I execute these codes
```
>>> from datasets import load_dataset
>>> test_dataset = load_dataset("wikitext")
```
I got an error,any help?
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/wikitext/wikitext.py
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2035/comments | https://api.github.com/repos/huggingface/datasets/issues/2035/events | https://github.com/huggingface/datasets/issues/2035 | 829,475,544 | MDU6SXNzdWU4Mjk0NzU1NDQ= | 2,035 | wiki40b/wikipedia for almost all languages cannot be downloaded | {
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"Dear @lhoestq for wikipedia dataset I also get the same error, I greatly appreciate if you could have a look into this dataset as well. Below please find the command to reproduce the error:\r\n\r\n```\r\ndataset = load_dataset(\"wikipedia\", \"20200501.bg\")\r\nprint(dataset)\r\n```\r\n\r\nYour library is my only ... | 1,615,492,494,000 | 1,615,906,417,000 | null | NONE | null | null | null | Hi
I am trying to download the data as below:
```
from datasets import load_dataset
dataset = load_dataset("wiki40b", "cs")
print(dataset)
```
I am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.
I really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources.
thank you very much.
```
(fast) dara@vgne046:/user/dara/dev/codes/seq2seq$ python test_data.py
Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp/dara/cache_home_2/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...
Traceback (most recent call last):
File "test_data.py", line 3, in <module>
dataset = load_dataset("wiki40b", "cs")
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/load.py", line 746, in load_dataset
use_auth_token=use_auth_token,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/builder.py", line 579, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/builder.py", line 1105, in _download_and_prepare
import apache_beam as beam
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/__init__.py", line 96, in <module>
from apache_beam import io
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/io/__init__.py", line 23, in <module>
from apache_beam.io.avroio import *
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/io/avroio.py", line 55, in <module>
import avro
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 668, in _load_unlocked
File "<frozen importlib._bootstrap>", line 638, in _load_backward_compatible
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/__init__.py", line 34, in <module>
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/__init__.py", line 30, in LoadResource
NotADirectoryError: [Errno 20] Not a directory: '/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/VERSION.txt'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2034/comments | https://api.github.com/repos/huggingface/datasets/issues/2034/events | https://github.com/huggingface/datasets/pull/2034 | 829,381,388 | MDExOlB1bGxSZXF1ZXN0NTkxMDU2MTEw | 2,034 | Fix typo | {
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} | Change `ENV_XDG_CACHE_HOME ` to `XDG_CACHE_HOME ` | {
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https://api.github.com/repos/huggingface/datasets/issues/2033 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2033/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2033/comments | https://api.github.com/repos/huggingface/datasets/issues/2033/events | https://github.com/huggingface/datasets/pull/2033 | 829,295,339 | MDExOlB1bGxSZXF1ZXN0NTkwOTgzMDAy | 2,033 | Raise an error for outdated sacrebleu versions | {
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} | The `sacrebleu` metric seem to only work for sacrecleu>=1.4.12
For example using sacrebleu==1.2.10, an error is raised (from metric/sacrebleu/sacrebleu.py):
```python
def _compute(
self,
predictions,
references,
smooth_method="exp",
smooth_value=None,
force=False,
lowercase=False,
tokenize=scb.DEFAULT_TOKENIZER,
use_effective_order=False,
):
references_per_prediction = len(references[0])
if any(len(refs) != references_per_prediction for refs in references):
raise ValueError("Sacrebleu requires the same number of references for each prediction")
transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
> output = scb.corpus_bleu(
sys_stream=predictions,
ref_streams=transformed_references,
smooth_method=smooth_method,
smooth_value=smooth_value,
force=force,
lowercase=lowercase,
tokenize=tokenize,
use_effective_order=use_effective_order,
)
E TypeError: corpus_bleu() got an unexpected keyword argument 'smooth_method'
/mnt/cache/modules/datasets_modules/metrics/sacrebleu/b390045b3d1dd4abf6a95c4a2a11ee3bcc2b7620b076204d0ddc353fa649fd86/sacrebleu.py:114: TypeError
```
I improved the error message when users have an outdated version of sacrebleu.
The new error message tells the user to update sacrebleu.
cc @LysandreJik | {
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https://api.github.com/repos/huggingface/datasets/issues/2032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2032/comments | https://api.github.com/repos/huggingface/datasets/issues/2032/events | https://github.com/huggingface/datasets/issues/2032 | 829,250,912 | MDU6SXNzdWU4MjkyNTA5MTI= | 2,032 | Use Arrow filtering instead of writing a new arrow file for Dataset.filter | {
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"followers_url": "https://api.github... | null | [] | 1,615,475,930,000 | 1,615,483,257,000 | null | MEMBER | null | null | null | Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.
Using a mask directly on the arrow table doesn't do any read or write operation therefore it's significantly quicker.
I think there are two cases:
- if the dataset doesn't have an indices mapping, then one can simply use the arrow filtering on the main arrow table `dataset._data.filter(...)`
- if the dataset an indices mapping, then the mask should be applied on the indices mapping table `dataset._indices.filter(...)`
The indices mapping is used to map between the idx at `dataset[idx]` in `__getitem__` and the idx in the actual arrow table.
The new filter method should therefore be faster, and allow users to pass either a filtering function (that returns a boolean given an example), or directly a mask.
Feel free to discuss this idea in this thread :)
One additional note: the refactor at #2025 would make all the pickle-related stuff work directly with the arrow filtering, so that we only need to change the Dataset.filter method without having to deal with pickle.
cc @theo-m @gchhablani
related issues: #1796 #1949 | {
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https://api.github.com/repos/huggingface/datasets/issues/2031 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2031/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2031/comments | https://api.github.com/repos/huggingface/datasets/issues/2031/events | https://github.com/huggingface/datasets/issues/2031 | 829,122,778 | MDU6SXNzdWU4MjkxMjI3Nzg= | 2,031 | wikipedia.py generator that extracts XML doesn't release memory | {
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"Hi @miyamonz \r\nThanks for investigating this issue, good job !\r\nIt would be awesome to integrate your fix in the library, could you open a pull request ?",
"OK! I'll send it later."
] | 1,615,467,084,000 | 1,616,402,032,000 | 1,616,402,032,000 | CONTRIBUTOR | null | null | null | I tried downloading Japanese wikipedia, but it always failed because of out of memory maybe.
I found that the generator function that extracts XML data in wikipedia.py doesn't release memory in the loop.
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L464-L502
`root.clear()` intend to clear memory, but it doesn't.
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L490
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L494
I replaced them with `elem.clear()`, then it seems to work correctly.
here is the notebook to reproduce it.
https://gist.github.com/miyamonz/dc06117302b6e85fa51cbf46dde6bb51#file-xtract_content-ipynb | {
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https://api.github.com/repos/huggingface/datasets/issues/2030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2030/comments | https://api.github.com/repos/huggingface/datasets/issues/2030/events | https://github.com/huggingface/datasets/pull/2030 | 829,110,803 | MDExOlB1bGxSZXF1ZXN0NTkwODI4NzQ4 | 2,030 | Implement Dataset from text | {
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"I am wondering why only one test of \"keep_in_memory=True\" fails, when there are many other tests that test the same and it happens only in pyarrow_1..."
] | 1,615,466,090,000 | 1,616,074,169,000 | 1,616,074,169,000 | MEMBER | null | false | {
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} | Implement `Dataset.from_text`.
Analogue to #1943, #1946. | {
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https://api.github.com/repos/huggingface/datasets/issues/2029 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2029/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2029/comments | https://api.github.com/repos/huggingface/datasets/issues/2029/events | https://github.com/huggingface/datasets/issues/2029 | 829,097,290 | MDU6SXNzdWU4MjkwOTcyOTA= | 2,029 | Loading a faiss index KeyError | {
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"In your code `dataset2` doesn't contain the \"embeddings\" column, since it is created from the pandas DataFrame with columns \"text\" and \"label\".\r\n\r\nTherefore when you call `dataset2[embeddings_name]`, you get a `KeyError`.\r\n\r\nIf you want the \"embeddings\" column back, you can create `dataset2` with\r... | 1,615,464,973,000 | 1,615,508,469,000 | 1,615,508,469,000 | NONE | null | null | null | I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.
The basic steps are:
1. Create a dataset (dataset1)
2. Create an embeddings column using DPR
3. Add a faiss index to the dataset
4. Save faiss index to a file
5. Create a new dataset (dataset2) with the same text and label information as dataset1
6. Try to load the faiss index from file to dataset2
7. Get `KeyError: "Column embeddings not in the dataset"`
I've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.
https://colab.research.google.com/drive/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing
Ubuntu Version
VERSION="18.04.5 LTS (Bionic Beaver)"
datasets==1.4.1
faiss==1.5.3
faiss-gpu==1.7.0
torch==1.8.0+cu101
transformers==4.3.3
NVIDIA-SMI 460.56
Driver Version: 460.32.03
CUDA Version: 11.2
Tesla K80
I was basically following the steps here: https://huggingface.co/docs/datasets/faiss_and_ea.html#adding-a-faiss-index
I included the exact code from the documentation at the end of the notebook to show that they don't work either.
| {
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https://api.github.com/repos/huggingface/datasets/issues/2028 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2028/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2028/comments | https://api.github.com/repos/huggingface/datasets/issues/2028/events | https://github.com/huggingface/datasets/pull/2028 | 828,721,393 | MDExOlB1bGxSZXF1ZXN0NTkwNDk1NzEx | 2,028 | Adding PersiNLU reading-comprehension | {
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"@lhoestq I think I have addressed all your comments. ",
"Thanks! @lhoestq Let me know if you want me to address anything to get this merged. ",
"It's all good thanks ;)\r\nmerging"
] | 1,615,437,673,000 | 1,615,801,197,000 | 1,615,801,197,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/2027 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2027/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2027/comments | https://api.github.com/repos/huggingface/datasets/issues/2027/events | https://github.com/huggingface/datasets/pull/2027 | 828,490,444 | MDExOlB1bGxSZXF1ZXN0NTkwMjkzNDA1 | 2,027 | Update format columns in Dataset.rename_columns | {
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} | Fixes #2026 | {
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https://api.github.com/repos/huggingface/datasets/issues/2026 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2026/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2026/comments | https://api.github.com/repos/huggingface/datasets/issues/2026/events | https://github.com/huggingface/datasets/issues/2026 | 828,194,467 | MDU6SXNzdWU4MjgxOTQ0Njc= | 2,026 | KeyError on using map after renaming a column | {
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"Hi,\r\n\r\nActually, the error occurs due to these two lines:\r\n```python\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n```\r\n`Dataset.rename_column` doesn't update the `_format_columns` attribute, previously defined by `Dataset.set_format... | 1,615,402,457,000 | 1,615,473,574,000 | 1,615,473,520,000 | CONTRIBUTOR | null | null | null | Hi,
I'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function.
Here is what I try:
```python
transform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])])
def prepare_features(examples):
images = []
labels = []
print(examples)
for example_idx, example in enumerate(examples["image"]):
if transform is not None:
images.append(transform(examples["image"][example_idx].permute(2,0,1)))
else:
images.append(examples["image"][example_idx].permute(2,0,1))
labels.append(examples["label"][example_idx])
output = {"label":labels, "image":images}
return output
raw_dataset = load_dataset('cifar10')
raw_dataset.set_format('torch',columns=['img','label'])
raw_dataset = raw_dataset.rename_column('img','image')
features = datasets.Features({
"image": datasets.Array3D(shape=(3,32,32),dtype="float32"),
"label": datasets.features.ClassLabel(names=[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
]),
})
train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)
```
The error:
```python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-54-bf29672c53ee> in <module>()
14 ]),
15 })
---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)
2 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1287 test_inputs = self[:2] if batched else self[0]
1288 test_indices = [0, 1] if batched else 0
-> 1289 update_data = does_function_return_dict(test_inputs, test_indices)
1290 logger.info("Testing finished, running the mapping function on the dataset")
1291
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in does_function_return_dict(inputs, indices)
1258 fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]
1259 processed_inputs = (
-> 1260 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1261 )
1262 does_return_dict = isinstance(processed_inputs, Mapping)
<ipython-input-52-b4dccbafb70d> in prepare_features(examples)
3 labels = []
4 print(examples)
----> 5 for example_idx, example in enumerate(examples["image"]):
6 if transform is not None:
7 images.append(transform(examples["image"][example_idx].permute(2,0,1)))
KeyError: 'image'
```
The print statement inside returns this:
```python
{'label': tensor([6, 9])}
```
Apparently, both `img` and `image` do not exist after renaming.
Note that this code works fine with `img` everywhere.
Notebook: https://colab.research.google.com/drive/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing
| {
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https://api.github.com/repos/huggingface/datasets/issues/2025 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2025/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2025/comments | https://api.github.com/repos/huggingface/datasets/issues/2025/events | https://github.com/huggingface/datasets/pull/2025 | 828,047,476 | MDExOlB1bGxSZXF1ZXN0NTg5ODk2NjMz | 2,025 | [Refactor] Use in-memory/memory-mapped/concatenation tables in Dataset | {
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"There is one more thing I would love to see. Let's say we iteratively keep updating a data source that loaded from **load_dataset** or **load_from_disk**. Now we need to save it to the same location by overriding the previous file inorder to save the disk space. At the moment **save_to_disk** can not assign a name... | 1,615,395,647,000 | 1,617,115,613,000 | 1,616,777,519,000 | MEMBER | null | false | {
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} | ## Intro
Currently there is one assumption that we need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk with memory mapping (using the dataset._data_files).
This assumption is used for pickling for example:
- in-memory dataset can just be pickled/unpickled in-memory
- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling
## Issues
Because of this assumption, we can't easily implement methods like `Dataset.add_item` to append more rows to a dataset, or `dataset.add_column` to add a column, since we can't mix data from memory and data from the disk.
Moreover, `concatenate_datasets` doesn't work if the datasets to concatenate are not all from memory, or all form the disk.
## Solution provided in this PR
I changed this by allowing several types of Table to be used in the Dataset object.
More specifically I added three pyarrow Table wrappers: InMemoryTable, MemoryMappedTable and ConcatenationTable.
The in-memory and memory-mapped tables implement the pickling behavior described above.
The ConcatenationTable can be made from several tables (either in-memory or memory mapped) called "blocks". Pickling a ConcatenationTable simply pickles the underlying blocks.
## Implementation details
The three tables classes mentioned above all inherit from a `Table` class defined in `table.py`, which is a wrapper of a pyarrow table. The `Table` wrapper implements all the attributes and methods of the underlying pyarrow table.
Regarding the MemoryMappedTable:
Reloading a pyarrow table from the disk makes you lose all the changes you may have applied (slice, rename_columns, drop, cast etc.). Therefore the MemoryMappedTable implements a "replay" mechanism to re-apply the changes when reloading the pyarrow table from the disk.
## Checklist
- [x] add InMemoryTable
- [x] add MemoryMappedTable
- [x] add ConcatenationTable
- [x] Update the ArrowReader to use these new tables depending on the `in_memory` parameter
- [x] Update Dataset.from_xxx methods
- [x] Update load_from_disk and save_to_disk
- [x] Backward compatibility of load_from_disk
- [x] Add tests for the new tables
- [x] Update current tests
- [ ] Documentation
----------
I would be happy to discuss the design of this PR :)
Close #1877 | {
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https://api.github.com/repos/huggingface/datasets/issues/2024 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2024/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2024/comments | https://api.github.com/repos/huggingface/datasets/issues/2024/events | https://github.com/huggingface/datasets/pull/2024 | 827,842,962 | MDExOlB1bGxSZXF1ZXN0NTg5NzEzNDAy | 2,024 | Remove print statement from mnist.py | {
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"Thanks for noticing !\r\n#2020 fixed this earlier today though ^^'\r\n\r\nClosing this one"
] | 1,615,387,198,000 | 1,615,485,832,000 | 1,615,485,831,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/2023 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2023/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2023/comments | https://api.github.com/repos/huggingface/datasets/issues/2023/events | https://github.com/huggingface/datasets/pull/2023 | 827,819,608 | MDExOlB1bGxSZXF1ZXN0NTg5NjkyNDU2 | 2,023 | Add Romanian to XQuAD | {
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"Hi ! Thanks for updating XQUAD :)\r\n\r\nThe slow test is failing though since there's no dummy data nor metadata in dataset_infos.json for the romanian configuration.\r\n\r\nCould you please generate the dummy data with\r\n```\r\ndatasets-cli dummy_data ./datasets/xquad --auto_generate --json_field data\r\n```\r\... | 1,615,386,272,000 | 1,615,802,897,000 | 1,615,802,897,000 | CONTRIBUTOR | null | false | {
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"merged_at": 1615802897000
} | On Jan 18, XQuAD was updated with a new Romanian validation file ([xquad commit link](https://github.com/deepmind/xquad/commit/60cac411649156efb6aab9dd4c9cde787a2c0345))
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https://api.github.com/repos/huggingface/datasets/issues/2022 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2022/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2022/comments | https://api.github.com/repos/huggingface/datasets/issues/2022/events | https://github.com/huggingface/datasets/issues/2022 | 827,435,033 | MDU6SXNzdWU4Mjc0MzUwMzM= | 2,022 | ValueError when rename_column on splitted dataset | {
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"Hi,\r\n\r\nThis is a bug so thanks for reporting it. `Dataset.__setstate__` is the problem, which is called when `Dataset.rename_column` tries to copy the dataset with `copy.deepcopy(self)`. This only happens if the `split` arg in `load_dataset` was defined as `ReadInstruction`.\r\n\r\nTo overcome this issue, use... | 1,615,369,238,000 | 1,615,903,568,000 | 1,615,903,505,000 | NONE | null | null | null | Hi there,
I am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so:
```python
split = {
'train': ReadInstruction('train', to=90, unit='%'),
'test': ReadInstruction('train', from_=-10, unit='%')
}
dataset = load_dataset(
path='csv', # use 'text' loading script to load from local txt-files
delimiter='\t', # xxx
data_files=text_files, # list of paths to local text files
split=split, # xxx
)
dataset
```
Part of output:
```python
DatasetDict({
train: Dataset({
features: ['sentence', 'sentiment'],
num_rows: 900
})
test: Dataset({
features: ['sentence', 'sentiment'],
num_rows: 100
})
})
```
Afterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however:
```python
dataset['train'].rename_column('sentence', 'text')
```
```python
/usr/local/lib/python3.7/dist-packages/datasets/splits.py in __init__(self, name)
353 for split_name in split_names_from_instruction:
354 if not re.match(_split_re, split_name):
--> 355 raise ValueError(f"Split name should match '{_split_re}'' but got '{split_name}'.")
356
357 def __str__(self):
ValueError: Split name should match '^\w+(\.\w+)*$'' but got 'ReadInstruction('.
```
In particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split.
Thanks in advance! :) | {
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https://api.github.com/repos/huggingface/datasets/issues/2021 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2021/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2021/comments | https://api.github.com/repos/huggingface/datasets/issues/2021/events | https://github.com/huggingface/datasets/issues/2021 | 826,988,016 | MDU6SXNzdWU4MjY5ODgwMTY= | 2,021 | Interactively doing save_to_disk and load_from_disk corrupts the datasets object? | {
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"Hi,\r\n\r\nCan you give us a minimal reproducible example? This [part](https://huggingface.co/docs/datasets/master/processing.html#controling-the-cache-behavior) of the docs explains how to control caching."
] | 1,615,344,514,000 | 1,615,630,061,000 | 1,615,630,061,000 | NONE | null | null | null | dataset_info.json file saved after using save_to_disk gets corrupted as follows.
Is there a way to disable the cache that will save to /tmp/huggiface/datastes ?
I have a feeling there is a serious issue with cashing. | {
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https://api.github.com/repos/huggingface/datasets/issues/2020 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2020/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2020/comments | https://api.github.com/repos/huggingface/datasets/issues/2020/events | https://github.com/huggingface/datasets/pull/2020 | 826,961,126 | MDExOlB1bGxSZXF1ZXN0NTg4OTE3MjYx | 2,020 | Remove unnecessary docstart check in conll-like datasets | {
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} | Related to this PR: #1998
Additionally, this PR adds the docstart note to the conll2002 dataset card ([link](https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/ned.train) to the raw data with `DOCSTART` lines).
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https://api.github.com/repos/huggingface/datasets/issues/2019 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2019/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2019/comments | https://api.github.com/repos/huggingface/datasets/issues/2019/events | https://github.com/huggingface/datasets/pull/2019 | 826,625,706 | MDExOlB1bGxSZXF1ZXN0NTg4NjEyODgy | 2,019 | Replace print with logging in dataset scripts | {
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"@lhoestq Maybe a script or even a test in `test_dataset_common.py` that verifies that a dataset script meets some set of quality standards (print calls and todos from the dataset script template are not present, etc.) could be added?",
"Yes definitely !"
] | 1,615,323,574,000 | 1,615,543,741,000 | 1,615,479,259,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/2018 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2018/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2018/comments | https://api.github.com/repos/huggingface/datasets/issues/2018/events | https://github.com/huggingface/datasets/pull/2018 | 826,473,764 | MDExOlB1bGxSZXF1ZXN0NTg4NDc0NTQz | 2,018 | Md gender card update | {
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"Link to the card: https://github.com/mcmillanmajora/datasets/blob/md-gender-card/datasets/md_gender_bias/README.md",
"dataset card* @sgugger :p ",
"Ahah that's what I wanted to say @lhoestq, thanks for fixing. Not used to review the Datasets side ;-)"
] | 1,615,316,240,000 | 1,615,570,260,000 | 1,615,570,260,000 | CONTRIBUTOR | null | false | {
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} | I updated the descriptions of the datasets as they appear in the HF repo and the descriptions of the source datasets according to what I could find from the paper and the references. I'm still a little unclear about some of the fields of the different configs, and there was little info on the word list and name list. I'll contact the authors to see if they have any additional information or suggested changes. | {
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https://api.github.com/repos/huggingface/datasets/issues/2017 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2017/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2017/comments | https://api.github.com/repos/huggingface/datasets/issues/2017/events | https://github.com/huggingface/datasets/pull/2017 | 826,428,578 | MDExOlB1bGxSZXF1ZXN0NTg4NDMyNDc2 | 2,017 | Add TF-based Features to handle different modes of data | {
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I am creating this draft PR to work on add features similar to [TF datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/core/features). I'll be starting with `Tensor` and `FeatureConnector` classes, and build upon them to add other features as well. This is a work in progress. | {
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} | The test at `tests/test_caching.py::RecurseDumpTest::test_dump_ipython_function` was failing in python 3.8 because the ipython function was not properly created.
Fix #2010 | {
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} | re: #2009
not super convinced this is better, and while I usually fight against kwargs here it seems to me that it better conveys the relationship to the `_split_generator` method. | {
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} | cc @aviaefrat who's the original author of the dataset & paper, see https://github.com/aviaefrat/cryptonite | {
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"What's the issue exactly ?\r\n\r\nGiven an `upstream` remote repository with url `https://github.com/huggingface/datasets.git`, you can totally rebase from `upstream/master`.\r\n\r\nIt's mentioned at the beginning how to add the `upstream` remote repository\r\n\r\nhttps://github.com/huggingface/datasets/blob/987df... | 1,615,283,335,000 | 1,615,289,611,000 | 1,615,289,611,000 | CONTRIBUTOR | null | null | null | Feels like the documentation on adding a new dataset is outdated?
https://github.com/huggingface/datasets/blob/987df6b4e9e20fc0c92bc9df48137d170756fd7b/ADD_NEW_DATASET.md#L49-L54
There is no upstream branch on remote. | {
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} | This PR adds a Romanian sentiment analysis dataset. This PR also closes pending PR #1529.
I had to add an `original_id` feature because the dataset files have repeated IDs. I can remove them if needed. I have also added `id` which is unique.
Let me know in case of any issues. | {
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https://api.github.com/repos/huggingface/datasets/issues/2010 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2010/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2010/comments | https://api.github.com/repos/huggingface/datasets/issues/2010/events | https://github.com/huggingface/datasets/issues/2010 | 825,567,635 | MDU6SXNzdWU4MjU1Njc2MzU= | 2,010 | Local testing fails | {
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"I'm not able to reproduce on my side.\r\nCan you provide the full stacktrace please ?\r\nWhat version of `python` and `dill` do you have ? Which OS are you using ?",
"```\r\nco_filename = '<ipython-input-2-e0383a102aae>', returned_obj = [0]\r\n ... | 1,615,280,498,000 | 1,615,298,763,000 | 1,615,298,763,000 | CONTRIBUTOR | null | null | null | I'm following the CI setup as described in
https://github.com/huggingface/datasets/blob/8eee4fa9e133fe873a7993ba746d32ca2b687551/.circleci/config.yml#L16-L19
in a new conda environment, at commit https://github.com/huggingface/datasets/commit/4de6dbf84e93dad97e1000120d6628c88954e5d4
and getting
```
FAILED tests/test_caching.py::RecurseDumpTest::test_dump_ipython_function - TypeError: an integer is required (got type bytes)
1 failed, 2321 passed, 5109 skipped, 10 warnings in 124.32s (0:02:04)
```
Seems like a discrepancy with CI, perhaps a lib version that's not controlled?
Tried with `pyarrow=={1.0.0,0.17.1,2.0.0}` | {
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"Hi @theo-m !\r\n\r\nA few lines above this line, you'll find that the `_split_generators` method returns a list of `SplitGenerator`s objects:\r\n\r\n```python\r\ndatasets.SplitGenerator(\r\n name=datasets.Split.VALIDATION,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n ... | 1,615,279,331,000 | 1,615,561,294,000 | 1,615,561,294,000 | CONTRIBUTOR | null | null | null | https://github.com/huggingface/datasets/blob/2ac9a0d24a091989f869af55f9f6411b37ff5188/templates/new_dataset_script.py#L156-L158
Looking at the template, I find this documentation line to be confusing, the method parameters don't include the `gen_kwargs` so I'm unclear where they're coming from.
Happy to push a PR with a clearer statement when I understand the meaning. | {
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"What do yo think of the documentation btw ?\r\nWhat parts would you like to see improved ?",
"I like how concise and straightforward the docs are.\r\n\r\nFew things that would further improve the docs IMO:\r\n* the usage example of `Dataset.formatted_as` in https://huggingface.co/docs/datasets/master/processing.... | 1,615,253,968,000 | 1,615,833,769,000 | 1,615,285,292,000 | CONTRIBUTOR | null | false | {
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* fixes various typos/grammer I came across while reading the docs
* adds the "Install with conda" installation instructions
Closes #1959 | {
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"So maybe a summary here: \r\nIf I could fit a large model with batch_size = X into memory, is there a way I could train this model for huge datasets with keeping setting the same? thanks ",
"The `datastets` library doesn't load datasets into memory. Therefore you can load a dataset that is terabytes big without ... | 1,615,206,926,000 | 1,628,100,145,000 | 1,628,100,145,000 | NONE | null | null | null | Hi
I am running this example from transformers library version 4.3.3:
(Here is the full documentation https://github.com/huggingface/transformers/issues/8771 but the running command should work out of the box)
USE_TF=0 deepspeed run_seq2seq.py --model_name_or_path google/mt5-base --dataset_name wmt16 --dataset_config_name ro-en --source_prefix "translate English to Romanian: " --task translation_en_to_ro --output_dir /test/test_large --do_train --do_eval --predict_with_generate --max_train_samples 500 --max_val_samples 500 --max_source_length 128 --max_target_length 128 --sortish_sampler --per_device_train_batch_size 8 --val_max_target_length 128 --deepspeed ds_config.json --num_train_epochs 1 --eval_steps 25000 --warmup_steps 500 --overwrite_output_dir
(Here please find the script: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/run_seq2seq.py)
If you do not pass max_train_samples in above command to load the full dataset, then I get memory issue on a gpu with 24 GigBytes of memory.
I need to train large-scale mt5 model on large-scale datasets of wikipedia (multiple of them concatenated or other datasets in multiple languages like OPUS), could you help me how I can avoid loading the full data into memory? to make the scripts not related to data size?
In above example, I was hoping the script could work without relying on dataset size, so I can still train the model without subsampling training set.
thank you so much @lhoestq for your great help in advance
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} | The benchmarks runs are [failing](https://github.com/huggingface/datasets/runs/2055534629?check_suite_focus=true) because of
```
ERROR: 'dvc.lock' is git-ignored.
```
I removed the dvc.lock file from the gitignore to fix that | {
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"Adding to the previous information, I think `torch.utils.data.DataLoader` is doing some conversion. \r\nWhat I tried:\r\n```python\r\ntrain_dataset = load_dataset('mnist')\r\n```\r\nI don't use any `map` or `set_format` or any `transform`. I use this directly, and try to load batches using the `DataLoader` with ba... | 1,615,189,091,000 | 1,615,312,693,000 | 1,615,312,693,000 | CONTRIBUTOR | null | null | null | Hi
I am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.
A snippet of what I am trying to do:
```python
def prepare_features(examples):
images = []
labels = []
for example_idx, example in enumerate(examples["image"]):
if transform is not None:
images.append(transform(
np.array(examples["image"][example_idx], dtype=np.uint8)
))
else:
images.append(torch.tensor(np.array(examples["image"][example_idx], dtype=np.uint8)))
labels.append(torch.tensor(examples["label"][example_idx]))
output = {"label":labels, "image":images}
return output
raw_dataset = load_dataset('mnist')
train_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)
train_dataset.set_format("torch",columns=["image","label"])
```
After this, I check the type of the following:
```python
print(type(train_dataset["train"]["label"]))
print(type(train_dataset["train"]["image"][0]))
```
This leads to the following output:
```python
<class 'torch.Tensor'>
<class 'list'>
```
I use `torch.utils.DataLoader` for batches, the type of `batch["train"]["image"]` is also `<class 'list'>`.
I don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?
Thanks,
Gunjan
EDIT:
I just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).
EDIT 2:
Inside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. | {
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"@lhoestq all the changes requested are implemented. Thank you for your time and feedback :)"
] | 1,615,165,592,000 | 1,615,977,800,000 | 1,615,977,800,000 | CONTRIBUTOR | null | false | {
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"This is expected to show this message to the user via stdout.\r\nThis way the users see it directly and can cancel the downloading if they want to.\r\nCould you elaborate why it would be better to have it in stderr instead of stdout ?",
"@lhoestq, sorry for the late reply\r\n\r\nI completely understand why you d... | 1,615,154,974,000 | 1,615,830,467,000 | null | NONE | null | null | null | In this code segment, we can see some messages are being printed to the `stdout`.
https://github.com/huggingface/datasets/blob/7e60bb509b595e8edc60a87f32b2bacfc065d607/src/datasets/builder.py#L545-L554
According to the comment, it is done intentionally, but I don't really understand why don't we log it with a higher level or print it directly to the `stderr`.
In my opinion, this kind of messages should never printed to the stdout. At least some configuration/flag should make it possible to provide in order to explicitly prevent the package to contaminate the stdout.
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"@lhoestq Thank you for all the feedback. I've added the suggested changes in my last commit."
] | 1,615,134,137,000 | 1,616,147,526,000 | 1,616,147,526,000 | CONTRIBUTOR | null | false | {
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} | [] | closed | false | null | [] | null | [] | 1,615,131,695,000 | 1,615,960,261,000 | 1,615,960,261,000 | NONE | null | null | null | In the original KILT benchmark(https://github.com/facebookresearch/KILT),
all samples has its evidence document (i.e. wikipedia page id) for prediction.
For example, a sample in ELI5 dataset has the format including provenance (=evidence document) like this
`{"id": "1kiwfx", "input": "In Trading Places (1983, Akroyd/Murphy) how does the scheme at the end of the movie work? Why would buying a lot of OJ at a high price ruin the Duke Brothers?", "output": [{"answer": "I feel so old. People have been askinbg what happened at the end of this movie for what must be the last 15 years of my life. It never stops. Every year/month/fortnight, I see someone asking what happened, and someone explaining. Andf it will keep on happening, until I am 90yrs old, in a home, with nothing but the Internet and my bladder to keep me going. And there it will be: \"what happens at the end of Trading Places?\""}, {"provenance": [{"wikipedia_id": "242855", "title": "Futures contract", "section": "Section::::Abstract.", "start_paragraph_id": 1, "start_character": 14, "end_paragraph_id": 1, "end_character": 612, "bleu_score": 0.9232808519770748}]}], "meta": {"partial_evidence": [{"wikipedia_id": "520990", "title": "Trading Places", "section": "Section::::Plot.\n", "start_paragraph_id": 7, "end_paragraph_id": 7, "meta": {"evidence_span": ["On television, they learn that Clarence Beeks is transporting a secret USDA report on orange crop forecasts.", "On television, they learn that Clarence Beeks is transporting a secret USDA report on orange crop forecasts. Winthorpe and Valentine recall large payments made to Beeks by the Dukes and realize that the Dukes plan to obtain the report to corner the market on frozen orange juice.", "Winthorpe and Valentine recall large payments made to Beeks by the Dukes and realize that the Dukes plan to obtain the report to corner the market on frozen orange juice."]}}]}}`
However, KILT ELI5 dataset from huggingface datasets library only contain empty list of provenance.
`{'id': '1oy5tc', 'input': 'in football whats the point of wasting the first two plays with a rush - up the middle - not regular rush plays i get those', 'meta': {'left_context': '', 'mention': '', 'obj_surface': [], 'partial_evidence': [], 'right_context': '', 'sub_surface': [], 'subj_aliases': [], 'template_questions': []}, 'output': [{'answer': 'In most cases the O-Line is supposed to make a hole for the running back to go through. If you run too many plays to the outside/throws the defense will catch on.\n\nAlso, 2 5 yard plays gets you a new set of downs.', 'meta': {'score': 2}, 'provenance': []}, {'answer': "I you don't like those type of plays, watch CFL. We only get 3 downs so you can't afford to waste one. Lots more passing.", 'meta': {'score': 2}, 'provenance': []}]}
`
should i perform other procedure to obtain evidence documents? | {
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https://api.github.com/repos/huggingface/datasets/issues/2000 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2000/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2000/comments | https://api.github.com/repos/huggingface/datasets/issues/2000/events | https://github.com/huggingface/datasets/issues/2000 | 823,899,910 | MDU6SXNzdWU4MjM4OTk5MTA= | 2,000 | Windows Permission Error (most recent version of datasets) | {
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"Hi @itsLuisa !\r\n\r\nCould you give us more information about the error you're getting, please?\r\nA copy-paste of the Traceback would be nice to get a better understanding of what is wrong :) ",
"Hello @SBrandeis , this is it:\r\n```\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\Luisa\\AppData\\... | 1,615,118,128,000 | 1,615,293,777,000 | 1,615,293,777,000 | NONE | null | null | null | Hi everyone,
Can anyone help me with why the dataset loading script below raises a Windows Permission Error? I stuck quite closely to https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py , only I want to load the data from three local three-column tsv-files (id\ttokens\tpos_tags\n). I am using the most recent version of datasets. Thank you in advance!
Luisa
My script:
```
import datasets
import csv
logger = datasets.logging.get_logger(__name__)
class SampleConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(SampleConfig, self).__init__(**kwargs)
class Sample(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
SampleConfig(name="conll2003", version=datasets.Version("1.0.0"), description="Conll2003 dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description="Dataset with words and their POS-Tags",
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"pos_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"''",
",",
"-LRB-",
"-RRB-",
".",
":",
"CC",
"CD",
"DT",
"EX",
"FW",
"HYPH",
"IN",
"JJ",
"JJR",
"JJS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WRB",
"``"
]
)
),
}
),
supervised_keys=None,
homepage="https://catalog.ldc.upenn.edu/LDC2011T03",
citation="Weischedel, Ralph, et al. OntoNotes Release 4.0 LDC2011T03. Web Download. Philadelphia: Linguistic Data Consortium, 2011.",
)
def _split_generators(self, dl_manager):
loaded_files = dl_manager.download_and_extract(self.config.data_files)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": loaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": loaded_files["test"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": loaded_files["val"]})
]
def _generate_examples(self, filepath):
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="cp1252") as f:
data = csv.reader(f, delimiter="\t")
ids = list()
tokens = list()
pos_tags = list()
for id_, line in enumerate(data):
#print(line)
if len(line) == 1:
if tokens:
yield id_, {"id": ids, "tokens": tokens, "pos_tags": pos_tags}
ids = list()
tokens = list()
pos_tags = list()
else:
ids.append(line[0])
tokens.append(line[1])
pos_tags.append(line[2])
# last example
yield id_, {"id": ids, "tokens": tokens, "pos_tags": pos_tags}
def main():
dataset = datasets.load_dataset(
"data_loading.py", data_files={
"train": "train.tsv",
"test": "test.tsv",
"val": "val.tsv"
}
)
#print(dataset)
if __name__=="__main__":
main()
```
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https://api.github.com/repos/huggingface/datasets/issues/1999 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1999/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1999/comments | https://api.github.com/repos/huggingface/datasets/issues/1999/events | https://github.com/huggingface/datasets/pull/1999 | 823,753,591 | MDExOlB1bGxSZXF1ZXN0NTg2MTM5ODMy | 1,999 | Add FashionMNIST dataset | {
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"Hi @lhoestq,\r\n\r\nI have added the changes from the review."
] | 1,615,066,617,000 | 1,615,283,531,000 | 1,615,283,531,000 | CONTRIBUTOR | null | false | {
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"Nice catch! Yes I didn't check the actual data, instead I was just looking for the `if line.startswith(\"-DOCSTART-\")` pattern."
] | 1,615,057,709,000 | 1,615,429,207,000 | 1,615,429,207,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/1997 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1997/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1997/comments | https://api.github.com/repos/huggingface/datasets/issues/1997/events | https://github.com/huggingface/datasets/issues/1997 | 823,679,465 | MDU6SXNzdWU4MjM2Nzk0NjU= | 1,997 | from datasets import MoleculeDataset, GEOMDataset | {
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] | closed | false | null | [] | null | [] | 1,615,045,819,000 | 1,615,047,206,000 | 1,615,047,206,000 | NONE | null | null | null | I met the ImportError: cannot import name 'MoleculeDataset' from 'datasets'. Have anyone met the similar issues? Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/1996 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1996/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1996/comments | https://api.github.com/repos/huggingface/datasets/issues/1996/events | https://github.com/huggingface/datasets/issues/1996 | 823,573,410 | MDU6SXNzdWU4MjM1NzM0MTA= | 1,996 | Error when exploring `arabic_speech_corpus` | {
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"Thanks for reporting! We'll fix that as soon as possible",
"Actually soundfile is not a dependency of this dataset.\r\nThe error comes from a bug that was fixed in this commit: https://github.com/huggingface/datasets/pull/1767/commits/c304e63629f4453367de2fd42883a78768055532\r\nBasically the library used to cons... | 1,615,010,120,000 | 1,615,288,345,000 | null | NONE | null | null | null | Navigate to https://huggingface.co/datasets/viewer/?dataset=arabic_speech_corpus
Error:
```
ImportError: To be able to use this dataset, you need to install the following dependencies['soundfile'] using 'pip install soundfile' for instance'
Traceback:
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/script_runner.py", line 332, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 233, in <module>
configs = get_confs(option)
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py", line 604, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py", line 588, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp-viewer/run.py", line 145, in get_confs
module_path = nlp.load.prepare_module(path, dataset=True
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/datasets/load.py", line 342, in prepare_module
f"To be able to use this {module_type}, you need to install the following dependencies"
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1995 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1995/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1995/comments | https://api.github.com/repos/huggingface/datasets/issues/1995/events | https://github.com/huggingface/datasets/pull/1995 | 822,878,431 | MDExOlB1bGxSZXF1ZXN0NTg1NDI5NTg0 | 1,995 | [Timit_asr] Make sure not only the first sample is used | {
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"cc @lhoestq @vrindaprabhu",
"Failing `run (push)` is unrelated -> merging",
"Thanks for fixing this, it was affecting my runs for https://github.com/huggingface/transformers/pull/10581/",
"I am seeing this very late! Sorry for the blunder everyone! :("
] | 1,614,933,771,000 | 1,625,034,353,000 | 1,614,934,739,000 | MEMBER | null | false | {
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"@lhoestq I really appreciate if you could help me providiing processed datasets, I do not really have access to enough resources to run the apache-beam and need to run the codes on these datasets. Only en/de/fr currently works, but I need all the languages more or less. thanks ",
"Hi @dorost1234, I think I can ... | 1,614,933,108,000 | 1,615,495,581,000 | null | NONE | null | null | null | Hi
I am trying to run a code with wikipedia of config 20200501.es, getting:
Traceback (most recent call last):
File "run_mlm_t5.py", line 608, in <module>
main()
File "run_mlm_t5.py", line 359, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
File "/dara/libs/anaconda3/envs/success432/lib/python3.7/site-packages/datasets-1.2.1-py3.7.egg/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/dara/libs/anaconda3/envs/success432/lib/python3.7/site-packages/datasets-1.2.1-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/dara/libs/anaconda3/envs/success432/lib/python3.7/site-packages/datasets-1.2.1-py3.7.egg/datasets/builder.py", line 1050, in _download_and_prepare
"\n\t`{}`".format(usage_example)
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`
thanks @lhoestq for any suggestion/help | {
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"Hi ! That looks like a bug, can you provide some code so that we can reproduce ?\r\nIt's not supposed to update the original dataset",
"Hi, I experimented with RAG. \r\n\r\nActually, you can run the [use_own_knowldge_dataset.py](https://github.com/shamanez/transformers/blob/rag-end-to-end-retrieval/examples/rese... | 1,614,921,950,000 | 1,616,385,950,000 | 1,616,385,950,000 | NONE | null | null | null | I am using the latest datasets library. In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.
When I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it. How to prevent from this?
| {
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https://api.github.com/repos/huggingface/datasets/issues/1992 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1992/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1992/comments | https://api.github.com/repos/huggingface/datasets/issues/1992/events | https://github.com/huggingface/datasets/issues/1992 | 822,672,238 | MDU6SXNzdWU4MjI2NzIyMzg= | 1,992 | `datasets.map` multi processing much slower than single processing | {
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"Hi @hwijeen, you might want to look at issues #1796 and #1949. I think it could be something related to the I/O operations being performed.",
"I see that many people are experiencing the same issue. Is this problem considered an \"official\" bug that is worth a closer look? @lhoestq",
"Yes this is an official ... | 1,614,910,202,000 | 1,626,689,109,000 | null | NONE | null | null | null | Hi, thank you for the great library.
I've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.
My data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.
I noticed that `map` function with `num_proc=mp.cpu_count() //2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.
What could be the reason? I would be happy to provide information necessary to spot the reason.
p.s. I was experiencing the imbalance issue mentioned in [here](https://github.com/huggingface/datasets/issues/610#issuecomment-705177036) when I was using multi processing.
p.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work.
| {
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https://api.github.com/repos/huggingface/datasets/issues/1991 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1991/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1991/comments | https://api.github.com/repos/huggingface/datasets/issues/1991/events | https://github.com/huggingface/datasets/pull/1991 | 822,554,473 | MDExOlB1bGxSZXF1ZXN0NTg1MTYwNDkx | 1,991 | Adding the conllpp dataset | {
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"Thanks for the reviews! A note that I have addressed the comments, and waiting for a further review."
] | 1,614,896,383,000 | 1,615,977,459,000 | 1,615,977,459,000 | CONTRIBUTOR | null | false | {
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"Do you think this is trying to bring the dataset into memory and if I can avoid it to save on memory so it only brings a batch into memory? @lhoestq thank you",
"It's not trying to bring the dataset into memory.\r\n\r\nActually, it's trying to memory map the dataset file, which is different. It allows to load l... | 1,614,882,118,000 | 1,628,100,265,000 | 1,628,100,265,000 | NONE | null | null | null | Hi,
I am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https://github.com/huggingface/transformers/blob/v4.3.2/examples/language-modeling/run_mlm.py
```
python run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir /dara/test --max_seq_length 128
```
I am using transformer version: 4.3.2
But I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?
Specially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq for your help and suggestions:
```
File "run_mlm.py", line 441, in <module>
main()
File "run_mlm.py", line 233, in main
split=f"train[{data_args.validation_split_percentage}%:]",
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/load.py", line 750, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 740, in as_dataset
map_tuple=True,
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 757, in _build_single_dataset
in_memory=in_memory,
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 829, in _as_dataset
in_memory=in_memory,
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 215, in read
return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 236, in read_files
pa_table = self._read_files(files, in_memory=in_memory)
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 171, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 322, in read_table
stream = stream_from(filename)
File "pyarrow/io.pxi", line 782, in pyarrow.lib.memory_map
File "pyarrow/io.pxi", line 743, in pyarrow.lib.MemoryMappedFile._open
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
OSError: Memory mapping file failed: Cannot allocate memory
```
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https://api.github.com/repos/huggingface/datasets/issues/1989 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1989/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1989/comments | https://api.github.com/repos/huggingface/datasets/issues/1989/events | https://github.com/huggingface/datasets/issues/1989 | 822,328,147 | MDU6SXNzdWU4MjIzMjgxNDc= | 1,989 | Question/problem with dataset labels | {
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"It seems that I get parsing errors for various fields in my data. For example now I get this:\r\n```\r\n File \"../../../models/tr-4.3.2/run_puppets.py\", line 523, in <module>\r\n main()\r\n File \"../../../models/tr-4.3.2/run_puppets.py\", line 249, in main\r\n datasets = load_dataset(\"csv\", data_files... | 1,614,877,613,000 | 1,615,455,855,000 | null | NONE | null | null | null | Hi, I'm using a dataset with two labels "nurse" and "not nurse". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are "nurse" and "surgeon".
This is the trace I get:
```
File "../../../models/tr-4.3.2/run_puppets.py", line 523, in <module>
main()
File "../../../models/tr-4.3.2/run_puppets.py", line 249, in main
datasets = load_dataset("csv", data_files=data_files)
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/load.py", line 740, in load_dataset
builder_instance.download_and_prepare(
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 572, in download_and_prepare
self._download_and_prepare(
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 650, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 1028, in _prepare_split
writer.write_table(table)
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/arrow_writer.py", line 292, in write_table
pa_table = pa_table.cast(self._schema)
File "pyarrow/table.pxi", line 1311, in pyarrow.lib.Table.cast
File "pyarrow/table.pxi", line 265, in pyarrow.lib.ChunkedArray.cast
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/pyarrow/compute.py", line 87, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 298, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 192, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Failed to parse string: not nurse
```
Any ideas how to fix this? For now, I'll probably make them numeric. | {
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https://api.github.com/repos/huggingface/datasets/issues/1988 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1988/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1988/comments | https://api.github.com/repos/huggingface/datasets/issues/1988/events | https://github.com/huggingface/datasets/issues/1988 | 822,324,605 | MDU6SXNzdWU4MjIzMjQ2MDU= | 1,988 | Readme.md is misleading about kinds of datasets? | {
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"Hi ! Yes it's possible to use image data. There are already a few of them available (MNIST, CIFAR..)"
] | 1,614,877,460,000 | 1,628,100,323,000 | 1,628,100,323,000 | NONE | null | null | null | Hi!
At the README.MD, you say: "efficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV/JSON/text. "
But here:
https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py#L82-L117
You mention other kinds of datasets, with images and so on. I'm confused.
Is it possible to use it to store, say, imagenet locally? | {
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https://api.github.com/repos/huggingface/datasets/issues/1987 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1987/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1987/comments | https://api.github.com/repos/huggingface/datasets/issues/1987/events | https://github.com/huggingface/datasets/issues/1987 | 822,308,956 | MDU6SXNzdWU4MjIzMDg5NTY= | 1,987 | wmt15 is broken | {
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} | [] | open | false | null | [] | null | [] | 1,614,876,385,000 | 1,614,876,385,000 | null | CONTRIBUTOR | null | null | null | While testing the hotfix, I tried a random other wmt release and found wmt15 to be broken:
```
python -c 'from datasets import load_dataset; load_dataset("wmt15", "de-en")'
Downloading: 2.91kB [00:00, 818kB/s]
Downloading: 3.02kB [00:00, 897kB/s]
Downloading: 41.1kB [00:00, 19.1MB/s]
Downloading and preparing dataset wmt15/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/stas/.cache/huggingface/datasets/wmt15/de-en/1.0.0/39ad5f9262a0910a8ad7028ad432731ad23fdf91f2cebbbf2ba4776b9859e87f...
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/load.py", line 740, in load_dataset
builder_instance.download_and_prepare(
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/builder.py", line 578, in download_and_prepare
self._download_and_prepare(
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/builder.py", line 634, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt15/39ad5f9262a0910a8ad7028ad432731ad23fdf91f2cebbbf2ba4776b9859e87f/wmt_utils.py", line 757, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 283, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 191, in download
downloaded_path_or_paths = map_nested(
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 203, in map_nested
mapped = [
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 204, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 160, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 160, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested
return function(data_struct)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 214, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 274, in cached_path
output_path = get_from_cache(
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 614, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/wmt/wmt15/resolve/main/training-parallel-nc-v10.tgz
``` | {
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"caching issue, seems to work again.."
] | 1,614,867,535,000 | 1,614,868,267,000 | 1,614,868,267,000 | NONE | null | null | null | ~\.cache\huggingface\modules\datasets_modules\datasets\wmt14\43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\wmt_utils.py in _split_generators(self, dl_manager)
758 # Extract manually downloaded files.
759 manual_files = dl_manager.extract(manual_paths_dict)
--> 760 extraction_map = dict(downloaded_files, **manual_files)
761
762 for language in self.config.language_pair:
TypeError: type object argument after ** must be a mapping, not list | {
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"@lhoestq, are the tests OK? Some other cases I missed? Do you agree with this approach?",
"I just tested this and it works like a charm :) \r\n\r\nHowever tokenizing and then setting the format to \"torch\" to feed the tokens into a model doesn't seem to work anymore, since the pytorch tensors have the int32/int... | 1,614,867,143,000 | 1,616,414,680,000 | 1,615,887,840,000 | MEMBER | null | false | {
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} | Optimize int precision to reduce dataset file size.
Close #1973, close #1825, close #861. | {
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} | [] | open | false | null | [] | null | [] | 1,614,840,402,000 | 1,614,840,402,000 | null | MEMBER | null | null | null | As requested in #1981, we need tests for WMT datasets, using dummy data. | {
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"Hi,\r\n\r\nif you inspect the raw data, you can find there are 946 occurrences of `-DOCSTART- -X- -X- O` in the train split and `14041 + 946 = 14987`, which is exactly the number of sentences the authors report. `-DOCSTART-` is a special line that acts as a boundary between two different documents and is filtered ... | 1,614,832,894,000 | 1,615,220,665,000 | null | NONE | null | null | null | Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.
The statistics of conll-2003 in this repo is :
\#train 14041 \#dev 3250 \#test 3453
While the official statistics is:
\#train 14987 \#dev 3466 \#test 3684
Wish for your reply~ | {
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"I validated that this fixed the problem, thank you, @albertvillanova!\r\n",
"still facing the same issue or similar:\r\nfrom datasets import load_dataset\r\nwtm14_test = load_dataset('wmt14',\"de-en\",cache_dir='./datasets')\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\wmt14\\43e717d978d22... | 1,614,802,611,000 | 1,614,876,364,000 | 1,614,811,716,000 | MEMBER | null | false | {
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"@stas00 Mea culpa... May I fix this tomorrow morning?",
"yes, of course, I reverted to the version before that and it works ;)\r\n\r\nbut since a new release was just made you will probably need to make a hotfix.\r\n\r\nand add the wmt to the tests?",
"Sure, I will implement a regression test!",
"@stas00 it ... | 1,614,799,299,000 | 1,614,867,407,000 | 1,614,811,716,000 | CONTRIBUTOR | null | null | null | on master:
```
python -c 'from datasets import load_dataset; load_dataset("wmt14", "de-en")'
Downloading and preparing dataset wmt14/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/stas/.cache/huggingface/datasets/wmt14/de-en/1.0.0/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e...
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 740, in load_dataset
builder_instance.download_and_prepare(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 578, in download_and_prepare
self._download_and_prepare(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 634, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt14/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e/wmt_utils.py", line 760, in _split_generators
extraction_map = dict(downloaded_files, **manual_files)
```
it worked fine recently. same problem if I try wmt16.
git bisect points to this commit from Feb 25 as the culprit https://github.com/huggingface/datasets/commit/792f1d9bb1c5361908f73e2ef7f0181b2be409fa
@albertvillanova | {
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"Nice thanks for the change !\r\nThis looks all good to me\r\n\r\nBefore we merge can you just update the dataset_infos.json file of drop ? You can do it by running\r\n```\r\ndatasets-cli test ./datasets/drop --all_configs --save_infos --ignore_verifications\r\n```",
"Done!"
] | 1,614,791,587,000 | 1,615,807,646,000 | 1,615,807,646,000 | CONTRIBUTOR | null | false | {
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} | Hello all,
I propose this change to the DROP loading script so that all answers are loaded no matter their type. Currently, only "span" answers are loaded, which excludes a significant amount of answers from drop (i.e. "number" and "date").
I updated the script with the version I use for my work. However, I couldn't find a way to verify that all is working when integrated with the datasets repo, since the `load_dataset` method seems to always download the script from github and not local files.
Note that 9 items from the train set have no answers, as well as 1 from the validation set. The script I propose simply do not load them.
Let me know if there is anything else I can do,
Clément | {
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"Thanks !\r\nNow to fix the CI the only thing left is to add a dummy `test-task-tc-template.out` file inside the `dummy_data.zip` at `./datasets/sem_eval_2020_task_11/dummy/1.1.0`\r\nIt must contain the labels template for each dummy article of the test set included in `dummy_data.zip`\r\n\r\nAfter that we should b... | 1,614,767,672,000 | 1,615,633,180,000 | 1,615,554,650,000 | CONTRIBUTOR | null | false | {
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- `article_id` is needed to create the submission file for the task at https://propaganda.qcri.org/semeval2020-task11/
- The `technique classification` task provides the span indices in a template for the test set that is necessary to complete the task. This PR implements processing of that template for the dataset. | {
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"@lhoestq thank you very much for the quick review and useful comments! \r\n\r\nI have tried to address them all, and a few comments that you left for ro_sts I have applied to the ro_sts_parallel as well (in read-me: fixed source_datasets, links to homepage, repository, leaderboard, thanks to me message, in ro_sts_... | 1,614,766,133,000 | 1,614,938,414,000 | 1,614,936,835,000 | CONTRIBUTOR | null | false | {
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"I sometimes also get this error with other languages of the same dataset:\r\n\r\n File \"/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py\", line 322, in read_table\r\n stream = stream_from(filename)\r\n File \"pyarrow/io.pxi\", line 782, in pyarrow.... | 1,614,712,888,000 | 1,614,766,660,000 | null | NONE | null | null | null | Hi
I am trying to run run_mlm.py code [1] of huggingface with following "wikipedia"/ "20200501.aa" dataset:
`python run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.aa --do_train --do_eval --output_dir /tmp/test-mlm --max_seq_length 256
`
I am getting this error, but as per documentation, huggingface dataset provide processed version of this dataset and users can load it without requiring setup extra settings for apache-beam. could you help me please to load this dataset?
Do you think I can run run_ml.py with this dataset? or anyway I could subsample and train the model? I greatly appreciate providing the processed version of all languages for this dataset, which allow the user to use them without setting up apache-beam,. thanks
I really appreciate your help.
@lhoestq
thanks.
[1] https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py
error I get:
```
>>> import datasets
>>> datasets.load_dataset("wikipedia", "20200501.aa")
Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /dara/temp/cache_home_2/datasets/wikipedia/20200501.aa/1.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/dara/temp/libs/anaconda3/envs/codes/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/load.py", line 746, in load_dataset
use_auth_token=use_auth_token,
File "/dara/temp/libs/anaconda3/envs/codes/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 573, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/dara/temp/libs/anaconda3/envs/codes/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 1099, in _download_and_prepare
import apache_beam as beam
ModuleNotFoundError: No module named 'apache_beam'
``` | {
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cc @stas00 | {
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More info : https://github.com/sphinx-doc/sphinx/pull/2064
You can try here : https://29353-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html | {
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"Echo'ing this observation: I have a few datasets in the neighborhood of 2GB CSVs uncompressed, and when I use something like `Dataset.save_to_disk()` it's ~18GB on disk.\r\n\r\nIf this is unexpected behavior, would be happy to help run debugging as needed.",
"Thanks @ioana-blue for pointing out this problem (and... | 1,614,695,753,000 | 1,617,113,039,000 | 1,615,887,840,000 | NONE | null | null | null | I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you! | {
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"Hi ! `rename_column` has been added recently and will be available in the next release"
] | 1,614,672,109,000 | 1,614,690,483,000 | null | NONE | null | null | null | 'Dataset' object has no attribute 'rename_column' | {
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"Oh nice thanks for adding the context manager ! All the streams and RecordBatchWriter will be properly closed now. Hopefully this gives a better experience on windows on which it's super important to close stuff.\r\n\r\nNot sure about the error, it looks like a process crashed silently.\r\nLet me take a look",
"... | 1,614,669,154,000 | 1,615,394,217,000 | 1,615,394,217,000 | MEMBER | null | false | {
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} | Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method.
Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit. | {
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cc @sebastianGehrmann | {
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"Thanks for the change, merging now !"
] | 1,614,622,919,000 | 1,614,705,900,000 | 1,614,705,900,000 | CONTRIBUTOR | null | false | {
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} | This PR adds the Turkish News Categories Dataset (270K - Lite Version) dataset which is a text classification dataset by me, @basakbuluz and @serdarakyol.
This dataset contains the same news from the current [interpress_news_category_tr dataset](https://huggingface.co/datasets/interpress_news_category_tr) but contains less information, OCR errors are reduced, can be easily separated, and can be divided into 10 classes ("kültürsanat", "ekonomi", "siyaset", "eğitim", "dünya", "spor", "teknoloji", "magazin", "sağlık", "gündem") were rearranged. | {
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"Since the failure was originally intermittent, there is no 100% telling that the problem is gone. \r\nBut if my artificial race condition setup https://github.com/huggingface/datasets/issues/1942#issuecomment-787124529 is to be the litmus test then the problem has been fixed, as with this PR branch that particular... | 1,614,620,718,000 | 1,614,690,345,000 | 1,614,690,344,000 | MEMBER | null | false | {
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} | As noticed in #1942 , there's a issue with locks if you run multiple separate evaluation experiments in a multiprocessed setup.
Indeed there is a time span in Metric._finalize() where the process 0 loses its lock before re-acquiring it. This is bad since the lock of the process 0 tells the other process that the corresponding cache file is available for writing/reading/deleting: we end up having one metric cache that collides with another one. This can raise FileNotFound errors when a metric tries to read the cache file and if the second conflicting metric deleted it.
To fix that I made sure that the lock file of the process 0 stays acquired from the cache file creation to the end of the metric computation. This way the other metrics can simply sample a new hashing name in order to avoid the collision.
Finally I added missing tests for separate experiments in distributed setup. | {
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"Hi !\r\nAs far as I know not all faiss indexes can be computed in parallel and then merged. \r\nFor example [here](https://github.com/facebookresearch/faiss/wiki/Special-operations-on-indexes#splitting-and-merging-indexes) is is mentioned that only IndexIVF indexes can be merged.\r\nMoreover faiss already works us... | 1,614,602,854,000 | 1,614,886,856,000 | 1,614,886,842,000 | NONE | null | null | null | I am thinking of making the **add_faiss_index** process faster. What if we run the add_faiss_index process on separate dataset shards and then combine them before (dataset.concatenate) saving the faiss.index file ?
I feel theoretically this will reduce the accuracy of retrieval since it affects the indexing process.
@lhoestq
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"Hi !\r\n\r\nTo fix 1, an you try to run this code ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"squad\", download_mode=\"force_redownload\")\r\n```\r\nMaybe the file your downloaded was corrupted, in this case redownloading this way should fix your issue 1.\r\n\r\nRegarding your 2nd poi... | 1,614,588,091,000 | 1,614,870,566,000 | null | NONE | null | null | null | ### 1 When I try to train lxmert,and follow the code in README that --dataset name:
```shell
python examples/question-answering/run_qa.py --model_name_or_path unc-nlp/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir /home2/zhenggo1/checkpoint/lxmert_squad
```
the bug is that:
```
Downloading and preparing dataset squad/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to /home2/zhenggo1/.cache/huggingface/datasets/squad/plain_text/1.0.0/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...
Traceback (most recent call last):
File "examples/question-answering/run_qa.py", line 501, in <module>
main()
File "examples/question-answering/run_qa.py", line 217, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
File "/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/load.py", line 746, in load_dataset
use_auth_token=use_auth_token,
File "/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/builder.py", line 573, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/builder.py", line 633, in _download_and_prepare
self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
File "/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 39, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json']
```
And I try to find the [checksum link](https://github.com/huggingface/datasets/blob/master/datasets/squad/dataset_infos.json)
,is the problem plain_text do not have a checksum?
### 2 When I try to train lxmert,and use local dataset:
```
python examples/question-answering/run_qa.py --model_name_or_path unc-nlp/lxmert-base-uncased --train_file $SQUAD_DIR/train-v1.1.json --validation_file $SQUAD_DIR/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir /home2/zhenggo1/checkpoint/lxmert_squad
```
The bug is that
```
['title', 'paragraphs']
Traceback (most recent call last):
File "examples/question-answering/run_qa.py", line 501, in <module>
main()
File "examples/question-answering/run_qa.py", line 273, in main
answer_column_name = "answers" if "answers" in column_names else column_names[2]
IndexError: list index out of range
```
I print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:
```
if training_args.do_train:
column_names = datasets["train"].column_names
else:
column_names = datasets["validation"].column_names
print(datasets["train"].column_names)
question_column_name = "question" if "question" in column_names else column_names[0]
context_column_name = "context" if "context" in column_names else column_names[1]
answer_column_name = "answers" if "answers" in column_names else column_names[2]
```
## Please tell me how to fix the bug,thks a lot! | {
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"Hi ! The labels -1 correspond to the examples without gold labels in the original snli dataset.\r\nFeel free to remove these examples if you don't need them by using\r\n```python\r\ndata = data.filter(lambda x: x[\"label\"] != -1)\r\n```"
] | 1,614,540,980,000 | 1,614,600,089,000 | null | NONE | null | null | null | Hi
There is label of -1 in train set of SNLI dataset, please find the code below:
```
import numpy as np
import datasets
data = datasets.load_dataset("snli")["train"]
labels = []
for d in data:
labels.append(d["label"])
print(np.unique(labels))
```
and results:
`[-1 0 1 2]`
version of datasets used:
`datasets 1.2.1 <pip>
`
thanks for your help. @lhoestq | {
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"@lhoestq Re-added the arg. The ConnectionError in CI seems unrelated to this PR (the same test fails on master as well).",
"Thanks !\r\nI'm re-running the CI, maybe this was an issue with circleCI",
"Looks all good now, merged :)"
] | 1,614,480,427,000 | 1,615,429,097,000 | 1,614,789,470,000 | CONTRIBUTOR | null | false | {
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"@lhoestq Added a test. If you can come up with a better stateful callable, I'm all ears 😄. ",
"Sorry I said earlier that it was good to have it inside the loop, my mistake !",
"@lhoestq Okay, did some refactoring and now the \"cache\" part comes before the for loop. Thanks for the guidance.\r\n\r\nThink this... | 1,614,475,745,000 | 1,616,513,209,000 | 1,616,513,209,000 | CONTRIBUTOR | null | false | {
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} | Removes the "test type" section in Dataset.map which would modify the state of the stateful function. Now, the return type of the map function is inferred after processing the first example.
Fixes #1940
@lhoestq Not very happy with the usage of `nonlocal`. Would like to hear your opinion on this. | {
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"Hi,\r\n\r\ntry `skiprows` instead. This part is not properly documented in the docs it seems.\r\n\r\n@lhoestq I'll fix this as part of a bigger PR that fixes typos in the docs."
] | 1,614,468,774,000 | 1,615,285,292,000 | 1,615,285,292,000 | NONE | null | null | null | Hello everyone,
I'm quite new to Git so sorry in advance if I'm breaking some ground rules of issues posting... :/
I tried to use the load_dataset function, from Huggingface datasets library, on a csv file using the skip_rows argument described on Huggingface page to skip the first row containing column names
`test_dataset = load_dataset('csv', data_files=['test_wLabel.tsv'], delimiter='\t', column_names=["id", "sentence", "label"], skip_rows=1)`
But I got the following error message
`__init__() got an unexpected keyword argument 'skip_rows'`
Have I used the wrong argument ? Am I missing something or is this a bug ?
Thank you very much for your time,
Best regards,
Arthur | {
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"Never mind, I ran it again and it worked this time. Strange."
] | 1,614,462,476,000 | 1,614,462,616,000 | 1,614,462,616,000 | NONE | null | null | null | I did
```
from datasets import load_dataset
dataset = load_dataset("xsum")
```
This returns
`ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz` | {
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metric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)
```
May I suggest that `num_process` is confusing as it's singular yet expects a plural value and either
* be deprecated in favor of `num_processes` which is more intuitive since it's plural as its expected value
* or even better why not mimic the established dist environment convention for that purpose, which uses `world_size`.
Same for `process_id` - why reinvent the naming and needing to explain that this is **NOT** `PID`, when we have `rank` already. That is:
```
metric = load_metric('glue', 'mrpc', world_size=world_size, rank=rank)
```
This then fits like a glove into the pytorch DDP and alike envs. and we just need to call:
* `dist.get_world_size()`
* `dist.get_rank()`
So it'd be as simple as:
```
metric = load_metric('glue', 'mrpc', world_size=dist.get_world_size(), rank=dist.get_rank())
```
From: https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group
* `world_size (int, optional)` – Number of processes participating in the job. Required if store is specified.
* `rank (int, optional)` – Rank of the current process. Required if store is specified.
And may be an example would be useful, so that the user doesn't even need to think about where to get `dist`:
```
import torch.distributed as dist
if dist.is_initialized():
metric = load_metric(metric_name, world_size=dist.get_world_size(), rank=dist.get_rank())
else:
metric = load_metric(metric_name)
```
I'm aware this is pytorch-centric, but it's better than no examples, IMHO.
Thank you. | {
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https://api.github.com/repos/huggingface/datasets/issues/1956 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1956/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1956/comments | https://api.github.com/repos/huggingface/datasets/issues/1956/events | https://github.com/huggingface/datasets/issues/1956 | 818,013,741 | MDU6SXNzdWU4MTgwMTM3NDE= | 1,956 | [distributed env] potentially unsafe parallel execution | {
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"You can pass the same `experiment_id` for all the metrics of the same group, and use another `experiment_id` for the other groups.\r\nMaybe we can add an environment variable that sets the default value for `experiment_id` ? What do you think ?",
"Ah, you're absolutely correct, @lhoestq - it's exactly the equiva... | 1,614,458,325,000 | 1,614,619,482,000 | 1,614,619,482,000 | CONTRIBUTOR | null | null | null | ```
metric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)
```
presumes that there is only one set of parallel processes running - and will intermittently fail if you have multiple sets running as they will surely overwrite each other. Similar to https://github.com/huggingface/datasets/issues/1942 (but for a different reason).
That's why dist environments use some unique to a group identifier so that each group is dealt with separately.
e.g. the env-way of pytorch dist syncing is done with a unique per set `MASTER_ADDRESS+MASTER_PORT`
So ideally this interface should ask for a shared secret to do the right thing.
I'm not reporting an immediate need, but am only flagging that this will hit someone down the road.
This problem can be remedied by adding a new optional `shared_secret` option, which can then be used to differentiate different groups of processes. and this secret should be part of the file lock name and the experiment.
Thank you | {
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https://api.github.com/repos/huggingface/datasets/issues/1955 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1955/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1955/comments | https://api.github.com/repos/huggingface/datasets/issues/1955/events | https://github.com/huggingface/datasets/pull/1955 | 818,010,664 | MDExOlB1bGxSZXF1ZXN0NTgxMzk2OTA5 | 1,955 | typos + grammar | {
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} | This PR proposes a few typo + grammar fixes, and rewrites some sentences in an attempt to improve readability.
N.B. When referring to the library `datasets` in the docs it is typically used as a singular, and it definitely is a singular when written as "`datasets` library", that is "`datasets` library is ..." and not "are ...". | {
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"Hi\r\nnot sure how change the lable after creation, but this is an issue not dataset request. thanks ",
"Hi ! Currently you have to use `map` . You can see an example of how to do it in this comment: https://github.com/huggingface/datasets/issues/853#issuecomment-727872188\r\n\r\nIn the future we'll add support ... | 1,614,363,447,000 | 1,619,707,843,000 | 1,619,707,843,000 | NONE | null | null | null | Hi
I'd need to add a new column to the dataset, I was wondering how this can be done? thanks
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I also fixed some formatting issues in the docstrings | {
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