Datasets:

itsankitkp

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like 0

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2021-03-09T14:06:03Z

MDExOlB1bGxSZXF1ZXN0NTg3OTg4NTQ0

closed

Fix ipython function creation in tests

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2021-03-09T13:36:59Z

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MEMBER

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

2021-03-09T14:06:04Z

https://github.com/huggingface/datasets/pull/2015

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2021-03-10T10:08:36Z

MDExOlB1bGxSZXF1ZXN0NTg3OTY1NDg3

closed

more explicit method parameters

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2021-03-09T13:18:29Z

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CONTRIBUTOR

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.

2021-03-10T10:08:37Z

https://github.com/huggingface/datasets/pull/2014

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2021-03-09T19:27:06Z

MDExOlB1bGxSZXF1ZXN0NTg3NzYzMTgx

closed

Add Cryptonite dataset

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2021-03-09T10:32:11Z

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CONTRIBUTOR

cc @aviaefrat who's the original author of the dataset & paper, see https://github.com/aviaefrat/cryptonite

2021-03-09T19:27:07Z

https://github.com/huggingface/datasets/pull/2013

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2021-03-09T11:33:31Z

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No upstream branch

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2021-03-09T09:48:55Z

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CONTRIBUTOR

Feels like the documentation on adding a new dataset is outdated?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.

2021-03-09T11:33:31Z

https://github.com/huggingface/datasets/issues/2012

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2021-03-11T18:00:52Z

MDExOlB1bGxSZXF1ZXN0NTg3Njk4MTAx

closed

Add RoSent Dataset

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2021-03-09T09:40:08Z

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CONTRIBUTOR

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.

2021-03-11T18:00:52Z

https://github.com/huggingface/datasets/pull/2011

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2021-03-09T14:06:03Z

completed

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Local testing fails

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2021-03-09T09:01:38Z

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CONTRIBUTOR

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}`

2021-03-09T14:06:03Z

https://github.com/huggingface/datasets/issues/2010

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2021-03-12T15:01:34Z

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Ambiguous documentation

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2021-03-09T08:42:11Z

https://api.github.com/repos/huggingface/datasets/issues/2009

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CONTRIBUTOR

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.

2021-03-12T15:01:34Z

https://github.com/huggingface/datasets/issues/2009

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2021-03-09T10:21:32Z

MDExOlB1bGxSZXF1ZXN0NTg3Mjc1Njk4

closed

Fix various typos/grammer in the docs

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2021-03-09T01:39:28Z

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CONTRIBUTOR

This PR: * fixes various typos/grammer I came across while reading the docs * adds the "Install with conda" installation instructions Closes #1959

2021-03-15T18:42:49Z

https://github.com/huggingface/datasets/pull/2008

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2021-08-04T18:02:25Z

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How to not load huggingface datasets into memory

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2021-03-08T12:35:26Z

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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

2021-08-04T18:02:25Z

https://github.com/huggingface/datasets/issues/2007

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2021-03-08T11:28:34Z

MDExOlB1bGxSZXF1ZXN0NTg2Njg5Nzk2

closed

Don't gitignore dvc.lock

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2021-03-08T11:13:08Z

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MEMBER

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

2021-03-08T11:28:35Z

https://github.com/huggingface/datasets/pull/2006

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2021-03-09T17:58:13Z

completed

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closed

Setting to torch format not working with torchvision and MNIST

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2021-03-08T07:38:11Z

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CONTRIBUTOR

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.

2021-03-09T17:58:13Z

https://github.com/huggingface/datasets/issues/2005

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2021-03-17T10:43:20Z

MDExOlB1bGxSZXF1ZXN0NTg2MzcyODY1

closed

LaRoSeDa

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2021-03-08T01:06:32Z

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CONTRIBUTOR

Add LaRoSeDa to huggingface datasets.

2021-03-17T10:43:20Z

https://github.com/huggingface/datasets/pull/2004

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2023-07-25T16:35:21Z

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Messages are being printed to the `stdout`

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2021-03-07T22:09:34Z

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NONE

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.

2023-07-25T16:35:21Z

https://github.com/huggingface/datasets/issues/2003

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2021-03-19T09:52:06Z

MDExOlB1bGxSZXF1ZXN0NTg2MjgwNzE3

closed

MOROCO

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2021-03-07T16:22:17Z

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CONTRIBUTOR

Add MOROCO to huggingface datasets.

2021-03-19T09:52:06Z

https://github.com/huggingface/datasets/pull/2002

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2021-03-17T05:51:01Z

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Empty evidence document ("provenance") in KILT ELI5 dataset

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2021-03-07T15:41:35Z

https://api.github.com/repos/huggingface/datasets/issues/2001

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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?

2022-12-19T19:25:14Z

https://github.com/huggingface/datasets/issues/2001

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2021-03-09T12:42:57Z

completed

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Windows Permission Error (most recent version of datasets)

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2021-03-07T11:55:28Z

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NONE

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() ```

2021-03-09T12:42:57Z

https://github.com/huggingface/datasets/issues/2000

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2021-03-09T09:52:11Z

MDExOlB1bGxSZXF1ZXN0NTg2MTM5ODMy

closed

Add FashionMNIST dataset

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2021-03-06T21:36:57Z

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CONTRIBUTOR

This PR adds [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) dataset.

2021-03-09T09:52:11Z

https://github.com/huggingface/datasets/pull/1999

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2021-03-11T02:20:07Z

MDExOlB1bGxSZXF1ZXN0NTg2MTE4NTQ4

closed

Add -DOCSTART- note to dataset card of conll-like datasets

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2021-03-06T19:08:29Z

https://api.github.com/repos/huggingface/datasets/issues/1998

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CONTRIBUTOR

Closes #1983

2021-03-11T02:20:07Z

https://github.com/huggingface/datasets/pull/1998

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2021-03-06T16:13:26Z

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from datasets import MoleculeDataset, GEOMDataset

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2021-03-06T15:50:19Z

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I met the ImportError: cannot import name 'MoleculeDataset' from 'datasets'. Have anyone met the similar issues? Thanks!

2021-03-06T16:13:26Z

https://github.com/huggingface/datasets/issues/1997

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2022-10-05T13:24:26Z

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Error when exploring `arabic_speech_corpus`

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2021-03-06T05:55:20Z

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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" ```

2022-10-05T13:24:26Z

https://github.com/huggingface/datasets/issues/1996

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2021-03-05T08:58:59Z

MDExOlB1bGxSZXF1ZXN0NTg1NDI5NTg0

closed

[Timit_asr] Make sure not only the first sample is used

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2021-03-05T08:42:51Z

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CONTRIBUTOR

When playing around with timit I noticed that only the first sample is used for all indices. I corrected this typo so that the dataset is correctly loaded.

2021-06-30T06:25:53Z

https://github.com/huggingface/datasets/pull/1995

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not being able to get wikipedia es language

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2021-03-05T08:31:48Z

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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

2021-03-11T20:46:21Z

https://github.com/huggingface/datasets/issues/1994

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2021-03-22T04:05:50Z

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How to load a dataset with load_from disk and save it again after doing transformations without changing the original?

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2021-03-05T05:25:50Z

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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?

2021-03-22T04:05:50Z

https://github.com/huggingface/datasets/issues/1993

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`datasets.map` multi processing much slower than single processing

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2021-03-05T02:10:02Z

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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. 

2023-06-08T12:31:55Z

https://github.com/huggingface/datasets/issues/1992

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2021-03-17T10:37:39Z

MDExOlB1bGxSZXF1ZXN0NTg1MTYwNDkx

closed

Adding the conllpp dataset

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2021-03-04T22:19:43Z

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Adding the conllpp dataset, is a revision from https://github.com/huggingface/datasets/pull/1910.

2021-03-17T10:37:39Z

https://github.com/huggingface/datasets/pull/1991

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2021-08-04T18:04:25Z

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OSError: Memory mapping file failed: Cannot allocate memory

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2021-03-04T18:21:58Z

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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 ```

2021-08-04T18:04:25Z

https://github.com/huggingface/datasets/issues/1990

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2023-07-24T14:39:33Z

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Question/problem with dataset labels

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2021-03-04T17:06:53Z

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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.

2023-07-24T14:39:33Z

https://github.com/huggingface/datasets/issues/1989

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2021-08-04T18:05:23Z

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Readme.md is misleading about kinds of datasets?

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2021-03-04T17:04:20Z

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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?

2021-08-04T18:05:23Z

https://github.com/huggingface/datasets/issues/1988

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2022-10-05T13:12:26Z

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wmt15 is broken

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2021-03-04T16:46:25Z

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CONTRIBUTOR

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 ```

2022-10-05T13:12:26Z

https://github.com/huggingface/datasets/issues/1987

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2021-03-04T14:31:07Z

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wmt datasets fail to load

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2021-03-04T14:18:55Z

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~\.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

2021-03-04T14:31:07Z

https://github.com/huggingface/datasets/issues/1986

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2021-03-16T09:44:00Z

MDExOlB1bGxSZXF1ZXN0NTg0ODM4NjIw

closed

Optimize int precision

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2021-03-04T14:12:23Z

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Optimize int precision to reduce dataset file size. Close #1973, close #1825, close #861.

2021-03-22T12:04:40Z

https://github.com/huggingface/datasets/pull/1985

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2022-11-04T14:19:16Z

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Add tests for WMT datasets

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2021-03-04T06:46:42Z

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As requested in #1981, we need tests for WMT datasets, using dummy data.

2022-11-04T14:19:16Z

https://github.com/huggingface/datasets/issues/1984

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2022-10-05T13:13:26Z

completed

MDU6SXNzdWU4MjE3NDYwMDg=

closed

The size of CoNLL-2003 is not consistant with the official release.

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2021-03-04T04:41:34Z

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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~

2022-10-05T13:13:26Z

https://github.com/huggingface/datasets/issues/1983

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2021-03-03T22:48:36Z

MDExOlB1bGxSZXF1ZXN0NTg0MjM2NzQ0

closed

Fix NestedDataStructure.data for empty dict

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2021-03-03T20:16:51Z

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Fix #1981

2021-03-04T16:46:04Z

https://github.com/huggingface/datasets/pull/1982

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2021-03-03T22:48:36Z

completed

MDU6SXNzdWU4MjE0MTExMDk=

closed

wmt datasets fail to load

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2021-03-03T19:21:39Z

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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

2021-03-04T14:16:47Z

https://github.com/huggingface/datasets/issues/1981

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2021-03-15T11:27:26Z

MDExOlB1bGxSZXF1ZXN0NTg0MTI1OTUy

closed

Loading all answers from drop

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2021-03-03T17:13:07Z

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CONTRIBUTOR

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

2021-03-15T11:27:26Z

https://github.com/huggingface/datasets/pull/1980

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2021-03-12T13:10:50Z

MDExOlB1bGxSZXF1ZXN0NTgzODQ3MTk3

closed

Add article_id and process test set template for semeval 2020 task 11…

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2021-03-03T10:34:32Z

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CONTRIBUTOR

… dataset - `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.

2021-03-13T10:59:40Z

https://github.com/huggingface/datasets/pull/1979

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2021-03-05T09:33:55Z

MDExOlB1bGxSZXF1ZXN0NTgzODI5Njgz

closed

Adding ro sts dataset

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2021-03-03T10:08:53Z

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CONTRIBUTOR

Adding [RO-STS](https://github.com/dumitrescustefan/RO-STS) dataset

2021-03-05T10:00:14Z

https://github.com/huggingface/datasets/pull/1978

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MDU6SXNzdWU4MjAzMTIwMjI=

open

ModuleNotFoundError: No module named 'apache_beam' for wikipedia datasets

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2021-03-02T19:21:28Z

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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' ```

2021-03-03T10:17:40Z

https://github.com/huggingface/datasets/issues/1977

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2021-03-03T15:45:30Z

MDExOlB1bGxSZXF1ZXN0NTgzMjA3NDI4

closed

Add datasets full offline mode with HF_DATASETS_OFFLINE

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2021-03-02T17:26:59Z

https://api.github.com/repos/huggingface/datasets/issues/1976

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Add the HF_DATASETS_OFFLINE environment variable for users who want to use `datasets` offline without having to wait for the network timeouts/retries to happen. This was requested in https://github.com/huggingface/datasets/issues/1939 cc @stas00

2021-03-03T15:45:31Z

https://github.com/huggingface/datasets/pull/1976

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2021-03-04T10:43:22Z

MDExOlB1bGxSZXF1ZXN0NTgzMTg4NjM3

closed

Fix flake8

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2021-03-02T16:59:13Z

https://api.github.com/repos/huggingface/datasets/issues/1975

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Fix flake8 style.

2021-03-04T10:43:22Z

https://github.com/huggingface/datasets/pull/1975

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2021-03-04T10:42:48Z

MDExOlB1bGxSZXF1ZXN0NTgzMTE5MDI0

closed

feat(docs): navigate with left/right arrow keys

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2021-03-02T15:24:50Z

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Enables docs navigation with left/right arrow keys. It can be useful for the ones who navigate with keyboard a lot. 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

2021-03-04T10:44:12Z

https://github.com/huggingface/datasets/pull/1974

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2021-03-16T09:44:00Z

completed

MDU6SXNzdWU4MjAwNzczMTI=

closed

Question: what gets stored in the datasets cache and why is it so huge?

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2021-03-02T14:35:53Z

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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!

2021-03-30T14:03:59Z

https://github.com/huggingface/datasets/issues/1973

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2022-06-01T16:08:47Z

completed

MDU6SXNzdWU4MTk3NTI3NjE=

closed

'Dataset' object has no attribute 'rename_column'

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2021-03-02T08:01:49Z

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'Dataset' object has no attribute 'rename_column'

2022-06-01T16:08:47Z

https://github.com/huggingface/datasets/issues/1972

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2021-03-10T16:36:57Z

MDExOlB1bGxSZXF1ZXN0NTgyNzgyNTU0

closed

Fix ArrowWriter closes stream at exit

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2021-03-02T07:12:34Z

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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.

2021-03-10T16:36:57Z

https://github.com/huggingface/datasets/pull/1971

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2021-03-02T02:01:33Z

MDExOlB1bGxSZXF1ZXN0NTgyNjAzMzEw

closed

Fixing the URL filtering for bad MLSUM examples in GEM

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2021-03-02T01:22:58Z

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This updates the code and metadata to use the updated `gem_mlsum_bad_ids_fixed.json` file provided by @juand-r cc @sebastianGehrmann

2021-03-02T03:19:06Z

https://github.com/huggingface/datasets/pull/1970

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2021-03-02T17:25:00Z

MDExOlB1bGxSZXF1ZXN0NTgyMjc5OTEx

closed

Add Turkish News Category Dataset - 270K - Lite Version

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2021-03-01T18:21:59Z

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CONTRIBUTOR

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.

2021-03-02T17:25:00Z

https://github.com/huggingface/datasets/pull/1967

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2021-03-02T13:05:44Z

MDExOlB1bGxSZXF1ZXN0NTgyMjU2MzE0

closed

Fix metrics collision in separate multiprocessed experiments

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2021-03-01T17:45:18Z

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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.

2021-03-02T13:05:45Z

https://github.com/huggingface/datasets/pull/1966

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2021-03-04T19:40:42Z

completed

MDU6SXNzdWU4MTg4MzM0NjA=

closed

Can we parallelized the add_faiss_index process over dataset shards ?

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2021-03-01T12:47:34Z

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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

2021-03-04T19:40:56Z

https://github.com/huggingface/datasets/issues/1965

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2022-10-05T13:09:47Z

completed

MDU6SXNzdWU4MTg2MjQ4NjQ=

closed

Datasets.py function load_dataset does not match squad dataset

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2021-03-01T08:41:31Z

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### 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!

2022-10-05T13:09:47Z

https://github.com/huggingface/datasets/issues/1964

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2022-10-05T13:13:46Z

completed

MDU6SXNzdWU4MTgyODk5Njc=

closed

bug in SNLI dataset

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2021-02-28T19:36:20Z

https://api.github.com/repos/huggingface/datasets/issues/1963

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NONE

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

2022-10-05T13:13:46Z

https://github.com/huggingface/datasets/issues/1963

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2021-03-03T16:37:50Z

MDExOlB1bGxSZXF1ZXN0NTgxNDQwNzM4

closed

Fix unused arguments

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2021-02-28T02:47:07Z

https://api.github.com/repos/huggingface/datasets/issues/1962

https://api.github.com/repos/huggingface/datasets

CONTRIBUTOR

Noticed some args in the codebase are not used, so managed to find all such occurrences with Pylance and fix them.

2021-03-11T02:18:17Z

https://github.com/huggingface/datasets/pull/1962

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2021-03-04T10:38:53Z

MDExOlB1bGxSZXF1ZXN0NTgxNDM3NDI0

closed

Add sst dataset

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2021-02-28T02:08:29Z

https://api.github.com/repos/huggingface/datasets/issues/1961

https://api.github.com/repos/huggingface/datasets

CONTRIBUTOR

Related to #1934&mdash;Add the Stanford Sentiment Treebank dataset.

2021-03-04T10:38:53Z

https://github.com/huggingface/datasets/pull/1961

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2021-03-23T15:26:49Z

MDExOlB1bGxSZXF1ZXN0NTgxNDMzOTY4

closed

Allow stateful function in dataset.map

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2021-02-28T01:29:05Z

https://api.github.com/repos/huggingface/datasets/issues/1960

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CONTRIBUTOR

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.

2021-03-23T15:26:49Z

https://github.com/huggingface/datasets/pull/1960

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2021-03-09T10:21:32Z

completed

MDU6SXNzdWU4MTgwNTU2NDQ=

closed

Bug in skip_rows argument of load_dataset function ?

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2021-02-27T23:32:54Z

https://api.github.com/repos/huggingface/datasets/issues/1959

https://api.github.com/repos/huggingface/datasets

NONE

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

2021-03-09T10:21:32Z

https://github.com/huggingface/datasets/issues/1959

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2021-02-27T21:50:16Z

completed

MDU6SXNzdWU4MTgwMzc1NDg=

closed

XSum dataset download link broken

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2021-02-27T21:47:56Z

https://api.github.com/repos/huggingface/datasets/issues/1958

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NONE

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`

2021-02-27T21:50:16Z

https://github.com/huggingface/datasets/issues/1958

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2021-03-01T17:24:42Z

completed

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closed

[distributed env] potentially unsafe parallel execution

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2021-02-27T20:38:45Z

https://api.github.com/repos/huggingface/datasets/issues/1956

https://api.github.com/repos/huggingface/datasets

CONTRIBUTOR

``` 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

2021-03-01T17:24:42Z

https://github.com/huggingface/datasets/issues/1956

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2021-03-01T14:43:19Z

MDExOlB1bGxSZXF1ZXN0NTgxMzk2OTA5

closed

typos + grammar

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2021-02-27T20:21:43Z

https://api.github.com/repos/huggingface/datasets/issues/1955

https://api.github.com/repos/huggingface/datasets

CONTRIBUTOR

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 ...".

2021-03-01T17:20:38Z

https://github.com/huggingface/datasets/pull/1955

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2021-04-29T14:50:43Z

completed

MDU6SXNzdWU4MTc1NjU1NjM=

closed

add a new column

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2021-02-26T18:17:27Z

https://api.github.com/repos/huggingface/datasets/issues/1954

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NONE

Hi I'd need to add a new column to the dataset, I was wondering how this can be done? thanks @lhoestq

2021-04-29T14:50:43Z

https://github.com/huggingface/datasets/issues/1954

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2021-03-01T14:03:47Z

MDExOlB1bGxSZXF1ZXN0NTgwOTgyMDMz

closed

Documentation for to_csv, to_pandas and to_dict

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2021-02-26T16:35:49Z

https://api.github.com/repos/huggingface/datasets/issues/1953

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MEMBER

I added these methods to the documentation with a small paragraph. I also fixed some formatting issues in the docstrings

2021-03-01T14:03:48Z

https://github.com/huggingface/datasets/pull/1953

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2021-03-01T14:29:24Z

MDExOlB1bGxSZXF1ZXN0NTgwOTIyNjQw

closed

Handle timeouts

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2021-02-26T15:02:07Z

https://api.github.com/repos/huggingface/datasets/issues/1952

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MEMBER

As noticed in https://github.com/huggingface/datasets/issues/1939, timeouts were not properly handled when loading a dataset. This caused the connection to hang indefinitely when working in a firewalled environment cc @stas00 I added a default timeout, and included an option to our offline environment for tests to be able to simulate both connection errors and timeout errors (previously it was simulating connection errors only). Now networks calls don't hang indefinitely. The default timeout is set to 10sec (we might reduce it).

2021-03-01T14:29:24Z

https://github.com/huggingface/datasets/pull/1952

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2021-02-26T15:30:26Z

MDExOlB1bGxSZXF1ZXN0NTgwOTE4ODE2

closed

Add cross-platform support for datasets-cli

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2021-02-26T14:56:25Z

https://api.github.com/repos/huggingface/datasets/issues/1951

https://api.github.com/repos/huggingface/datasets

CONTRIBUTOR

One thing I've noticed while going through the codebase is the usage of `scripts` in `setup.py`. This [answer](https://stackoverflow.com/a/28119736/14095927) on SO explains it nicely why it's better to use `entry_points` instead of `scripts`. To add cross-platform support to the CLI, this PR replaces `scripts` with `entry_points` in `setup.py` and moves datasets-cli to src/datasets/commands/datasets_cli.py. All *.md and *.rst files are updated accordingly. The same changes were made in the transformers repo to add cross-platform ([link to PR](https://github.com/huggingface/transformers/pull/4131)).

2021-03-11T02:18:26Z

https://github.com/huggingface/datasets/pull/1951

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2021-03-01T11:08:29Z

MDExOlB1bGxSZXF1ZXN0NTgwODExMjMz

closed

updated multi_nli dataset with missing fields

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2021-02-26T11:54:36Z

https://api.github.com/repos/huggingface/datasets/issues/1950

https://api.github.com/repos/huggingface/datasets

CONTRIBUTOR

1) updated fields which were missing earlier 2) added tags to README 3) updated a few fields of README 4) new dataset_infos.json and dummy files

2021-03-01T11:08:30Z

https://github.com/huggingface/datasets/pull/1950

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MDU6SXNzdWU4MTY5ODY5MzY=

open

Enable Fast Filtering using Arrow Dataset

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2021-02-26T02:53:37Z

https://api.github.com/repos/huggingface/datasets/issues/1949

https://api.github.com/repos/huggingface/datasets

CONTRIBUTOR

Hi @lhoestq, As mentioned in Issue #1796, I would love to work on enabling fast filtering/mapping. Can you please share the expectations? It would be great if you could point me to the relevant methods/files involved. Or the docs or maybe an overview of `arrow_dataset.py`. I only ask this because I am having trouble getting started ;-; Any help would be appreciated. Thanks, Gunjan

2021-02-26T19:18:29Z

https://github.com/huggingface/datasets/issues/1949

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2023-07-12T17:19:30Z

completed

MDU6SXNzdWU4MTY2ODkzMjk=

closed

dataset loading logger level

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2021-02-25T18:33:37Z

https://api.github.com/repos/huggingface/datasets/issues/1948

https://api.github.com/repos/huggingface/datasets

CONTRIBUTOR

on master I get this with `--dataset_name wmt16 --dataset_config ro-en`: ``` WARNING:datasets.arrow_dataset:Loading cached processed dataset at /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f/cache-2e01bead8cf42e26.arrow WARNING:datasets.arrow_dataset:Loading cached processed dataset at /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f/cache-ac3bebaf4f91f776.arrow WARNING:datasets.arrow_dataset:Loading cached processed dataset at /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f/cache-810c3e61259d73a9.arrow ``` why are those WARNINGs? Should be INFO, no? warnings should only be used when a user needs to pay attention to something, this is just informative - I'd even say it should be DEBUG, but definitely not WARNING. Thank you.

2023-07-12T17:19:30Z

https://github.com/huggingface/datasets/issues/1948

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2021-03-01T14:36:53Z

MDExOlB1bGxSZXF1ZXN0NTgwMjI2MDk5

closed

Update documentation with not in place transforms and update DatasetDict

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2021-02-25T16:23:18Z

https://api.github.com/repos/huggingface/datasets/issues/1947

https://api.github.com/repos/huggingface/datasets

MEMBER

In #1883 were added the not in-place transforms `flatten`, `remove_columns`, `rename_column` and `cast`. I added them to the documentation and added a paragraph on how to use them You can preview the documentation [here](https://28862-250213286-gh.circle-artifacts.com/0/docs/_build/html/processing.html#renaming-removing-casting-and-flattening-columns) I also added these methods to the DatasetDict class.

2021-03-01T14:36:54Z

https://github.com/huggingface/datasets/pull/1947

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2021-03-12T09:42:48Z

MDExOlB1bGxSZXF1ZXN0NTgwMTcyNzI2

closed

Implement Dataset from CSV

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2021-02-25T15:10:13Z

https://api.github.com/repos/huggingface/datasets/issues/1946

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MEMBER

Implement `Dataset.from_csv`. Analogue to #1943. If finally, the scripts should be used instead, at least we can reuse the tests here.

2021-03-12T09:42:48Z

https://github.com/huggingface/datasets/pull/1946

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2021-02-25T13:20:26Z

completed

MDU6SXNzdWU4MTY0MjE5NjY=

closed

AttributeError: 'DatasetDict' object has no attribute 'concatenate_datasets'

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2021-02-25T13:09:45Z

https://api.github.com/repos/huggingface/datasets/issues/1945

https://api.github.com/repos/huggingface/datasets

NONE

Hi I am trying to concatenate a list of huggingface datastes as: ` train_dataset = datasets.concatenate_datasets(train_datasets) ` Here is the `train_datasets` when I print: ``` [Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 120361 }), Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 2670 }), Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 6944 }), Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 38140 }), Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 173711 }), Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 1655 }), Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 4274 }), Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 2019 }), Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 2109 }), Dataset({ features: ['attention_mask', 'idx', 'input_ids', 'label', 'question1', 'question2', 'token_type_ids'], num_rows: 11963 })] ``` I am getting the following error: `AttributeError: 'DatasetDict' object has no attribute 'concatenate_datasets' ` I was wondering if you could help me with this issue, thanks a lot

2021-02-25T13:20:35Z

https://github.com/huggingface/datasets/issues/1945

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2021-03-01T18:23:21Z

MDExOlB1bGxSZXF1ZXN0NTc5OTU2Nzc3

closed

Add Turkish News Category Dataset (270K - Lite Version)

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2021-02-25T09:45:22Z

https://api.github.com/repos/huggingface/datasets/issues/1944

https://api.github.com/repos/huggingface/datasets

CONTRIBUTOR

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. @SBrandeis @lhoestq, can you please review this PR?

2021-03-02T17:46:41Z

https://github.com/huggingface/datasets/pull/1944

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2021-03-18T09:42:08Z

MDExOlB1bGxSZXF1ZXN0NTc5ODY5NTk0

closed

Implement Dataset from JSON and JSON Lines

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2021-02-25T07:17:33Z

https://api.github.com/repos/huggingface/datasets/issues/1943

https://api.github.com/repos/huggingface/datasets

MEMBER

Implement `Dataset.from_jsonl`.

2021-03-18T09:42:08Z

https://github.com/huggingface/datasets/pull/1943

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2022-10-05T13:08:45Z

completed

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closed