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MDExOlB1bGxSZXF1ZXN0NDQ3MzQ4NDgw

Fix cached file path for metrics with different config names

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The config name was not taken into account to build the cached file path. It should fix #368

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MDExOlB1bGxSZXF1ZXN0NDQ3MDU3NTIw

Allow indexing Dataset via np.ndarray

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MDU6SXNzdWU2NTQxODY4OTA=

can't load local dataset: pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries

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Trying to load a local SQuAD-formatted dataset (from a JSON file, about 60MB): ``` dataset = nlp.load_dataset(path='json', data_files={nlp.Split.TRAIN: ["./path/to/file.json"]}) ``` causes ``` Traceback (most recent call last): File "dataloader.py", line 9, in <module> ["./path/to/file.json"]}) File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/load.py", line 524, in load_dataset save_infos=save_infos, File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 483, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 719, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False): File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/tqdm/std.py", line 1129, in __iter__ for obj in iterable: File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/datasets/json/88c1bc5c68489f7eda549ed05a5a738527c613b3e7a4ee3524d9d233353a949b/json.py", line 53, in _generate_tables file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options, File "pyarrow/_json.pyx", line 191, in pyarrow._json.read_json File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?) ``` I haven't been able to find any reports of this specific pyarrow error here or elsewhere.

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MDU6SXNzdWU2NTQwODcyNTE=

load_metric can't acquire lock anymore

closed

NONE

I can't load metric (glue) anymore after an error in a previous run. I even removed the whole cache folder `/home/XXX/.cache/huggingface/`, and the issue persisted. What are the steps to fix this? Traceback (most recent call last): File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 101, in __init__ self.filelock.acquire(timeout=1) File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/filelock.py", line 278, in acquire raise Timeout(self._lock_file) filelock.Timeout: The file lock '/home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock' could not be acquired. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "examples_huggingface_nlp.py", line 268, in <module> main() File "examples_huggingface_nlp.py", line 242, in main dataset, metric = get_dataset_metric(glue_task) File "examples_huggingface_nlp.py", line 77, in get_dataset_metric metric = nlp.load_metric('glue', glue_config, experiment_id=1) File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/load.py", line 440, in load_metric **metric_init_kwargs, File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 104, in __init__ "Cannot acquire lock, caching file might be used by another process, " ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id' for this run. I0709 15:54:41.008838 139854118430464 filelock.py:318] Lock 139852058030936 released on /home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock

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MDExOlB1bGxSZXF1ZXN0NDQ2ODIxNTAz

Update Xtreme to add PAWS-X es

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CONTRIBUTOR

This PR adds the `PAWS-X.es` in the Xtreme dataset #362

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MDExOlB1bGxSZXF1ZXN0NDQ2NzcyODE2

Add quora dataset

closed

CONTRIBUTOR

Added the [Quora question pairs dataset](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs). Implementation Notes: - I used the original version provided on the quora website. There's also a [Kaggle competition](https://www.kaggle.com/c/quora-question-pairs) which has a nice train/test split but I can't find an easy way to download it. - I've made the questions into a list: ```python { "questions": [ {"id":0, "text": "Is this an example question?"}, {"id":1, "text": "Is this a sample question?"}, ], ... } ``` rather than: ```python { "question1": "Is this an example question?", "question2": "Is this a sample question?" "qid0": 0 "qid1": 1 ... } ``` Not sure if this was the right call. - Can't find a good citation for this dataset

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MDU6SXNzdWU2NTM4NDU5NjQ=

How to augment data ?

closed

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Is there any clean way to augment data ? For now my work-around is to use batched map, like this : ```python def aug(samples): # Simply copy the existing data to have x2 amount of data for k, v in samples.items(): samples[k].extend(v) return samples dataset = dataset.map(aug, batched=True) ```

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MDExOlB1bGxSZXF1ZXN0NDQ2NjY0NzM5

add MS MARCO dataset

closed

CONTRIBUTOR

This PR adds the MS MARCO dataset as requested in this issue #336. MS mARCO has multiple task including: - Passage and Document Retrieval - Keyphrase Extraction - QA and NLG This PR only adds the 2 versions of the QA and NLG task dataset which was realeased with the original paper here https://arxiv.org/pdf/1611.09268.pdf Tests are failing because of the dummy data. I tried to fix it without success. Can you please have a look at it? @patrickvonplaten , @lhoestq

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MDExOlB1bGxSZXF1ZXN0NDQ2NjY0NDIy

Adding support for generic multi dimensional tensors and auxillary image data for multimodal datasets

closed

CONTRIBUTOR

nlp/features.py: The main factory class is MultiArray, every single time this class is called, a corresponding pyarrow extension array and type class is generated (and added to the list of globals for future use) for a given root data type and set of dimensions/shape. I provide examples on working with this in datasets/lxmert_pretraining_beta/test_multi_array.py src/nlp/arrow_writer.py I had to add a method for writing batches that include extension array types because despite having a unique class for each multidimensional array shape, pyarrow is unable to write any other "array-like" data class to a batch object unless it is of the type pyarrow.ExtensionType. The problem in this is that when writing multiple batches, the order of the schema and data to be written get mixed up (where the pyarrow datatype in the schema only refers to as ExtensionAray, but each ExtensionArray subclass has a different shape) ... possibly I am missing something here and would be grateful if anyone else could take a look! datasets/lxmert_pretraining_beta/lxmert_pretraining_beta.py & datasets/lxmert_pretraining_beta/to_arrow_data.py: I have begun adding the data from the original LXMERT paper (https://arxiv.org/abs/1908.07490) hosted here: (https://github.com/airsplay/lxmert). The reason I am not pulling from the source of truth for each individual dataset is because it seems that there will also need to be functionality to aggregate multimodal datasets to create a pre-training corpus (:sleepy: ). For now, this is just being used to test and run edge-cases for the MultiArray feature, so ive labeled it as "beta_pretraining"! (still working on the pretraining, just wanted to push out the new functionality sooner than later)

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MDU6SXNzdWU2NTM3NjYyNDU=

[dateset subset missing] xtreme paws-x

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I tried nlp.load_dataset('xtreme', 'PAWS-X.es') but get the value error It turns out that the subset for Spanish is missing https://github.com/google-research-datasets/paws/tree/master/pawsx

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MDU6SXNzdWU2NTM3NTczNzY=

🐛 [Metrics] ROUGE is non-deterministic

closed

NONE

If I run the ROUGE metric 2 times, with same predictions / references, the scores are slightly different. Refer to [this Colab notebook](https://colab.research.google.com/drive/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem. Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run : > ['0.3350', '0.1470', '0.2329'] ['0.3358', '0.1451', '0.2332'] --- Why ROUGE is not deterministic ?

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MDU6SXNzdWU2NTM2ODcxNzY=

[Feature request] Add dataset.ragged_map() function for many-to-many transformations

closed

CONTRIBUTOR

`dataset.map()` enables one-to-one transformations. Input one example and output one example. This is helpful for tokenizing and cleaning individual lines. `dataset.filter()` enables one-to-(one-or-none) transformations. Input one example and output either zero/one example. This is helpful for removing portions from the dataset. However, some dataset transformations are many-to-many. Consider constructing BERT training examples from a dataset of sentences, where you map `["a", "b", "c"] -> ["a[SEP]b", "a[SEP]c", "b[SEP]c", "c[SEP]b", ...]` I propose a more general `ragged_map()` method that takes in a batch of examples of length `N` and return a batch of examples `M`. This is different from the `map(batched=True)` method, which takes examples of length `N` and returns a batch of length `N`, processing individual examples in parallel. I don't have a clear vision of how this would be implemented efficiently and lazily, but would love to hear the community's feedback on this. My specific use case is creating an end-to-end ELECTRA data pipeline. I would like to take the raw WikiText data and generate training examples from this using the `ragged_map()` method, then export to TFRecords and train quickly. This would be a reproducible pipeline with no bash scripts. Currently I'm relying on scripts like https://github.com/google-research/electra/blob/master/build_pretraining_dataset.py, which are less general.

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MDU6SXNzdWU2NTM2NTYyNzk=

ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures

closed

NONE

I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function. ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-23-9aecfbee53bd> in <module> 55 from nlp import load_dataset 56 ---> 57 ds = load_dataset("../text2struct/model/dataset_builder.py", data_files=rel_datafiles) 58 59 ~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 522 download_mode=download_mode, 523 ignore_verifications=ignore_verifications, --> 524 save_infos=save_infos, 525 ) 526 ~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 430 verify_infos = not save_infos and not ignore_verifications 431 self._download_and_prepare( --> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 433 ) 434 # Sync info ~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 481 try: 482 # Prepare split will record examples associated to the split --> 483 self._prepare_split(split_generator, **prepare_split_kwargs) 484 except OSError: 485 raise OSError("Cannot find data file. " + (self.manual_download_instructions or "")) ~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _prepare_split(self, split_generator) 736 schema_dict[field.name] = Value(str(field.type)) 737 --> 738 parse_schema(writer.schema, features) 739 self.info.features = Features(features) 740 ~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in parse_schema(schema, schema_dict) 734 parse_schema(field.type.value_type, schema_dict[field.name]) 735 else: --> 736 schema_dict[field.name] = Value(str(field.type)) 737 738 parse_schema(writer.schema, features) <string> in __init__(self, dtype, id, _type) ~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in __post_init__(self) 55 56 def __post_init__(self): ---> 57 self.pa_type = string_to_arrow(self.dtype) 58 59 def __call__(self): ~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in string_to_arrow(type_str) 32 if str(type_str + "_") not in pa.__dict__: 33 raise ValueError( ---> 34 f"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. " 35 f"Please make sure to use a correct data type, see: " 36 f"https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions" ValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions ``` If I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well.

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MDExOlB1bGxSZXF1ZXN0NDQ2NTI0NjQ5

Starting to add some real doc

closed

MEMBER

Adding a lot of documentation for: - load a dataset - explore the dataset object - process data with the dataset - add a new dataset script - share a dataset script - full package reference This version of the doc can be explored here: https://2219-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html Also: - fix a bug in `train_test_split` - update the `csv` script - add a verbose argument to the dataset processing methods Still missing: - doc for the metrics - how to directly upload a community provided dataset with the CLI - clean up more docstrings - add the `features` argument to `load_dataset` (should be another PR)

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MDExOlB1bGxSZXF1ZXN0NDQ2NTIyMzU2

Add hashes to cnn_dailymail

closed

CONTRIBUTOR

The URL hashes are helpful for comparing results from other sources.

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MDExOlB1bGxSZXF1ZXN0NDQ2NDM3MDQ5

Add text dataset

closed

CONTRIBUTOR

Usage: ```python from nlp import load_dataset dset = load_dataset("text", data_files="/path/to/file.txt")["train"] ``` I created a dummy_data.zip which contains three files: `train.txt`, `test.txt`, `dev.txt`. Each of these contains two lines. It passes ```bash RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_text ``` but I would like a second set of eyes to ensure I did it right.

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MDU6SXNzdWU2NTM0NTEwMTM=

can't load SNLI dataset

closed

CONTRIBUTOR

`nlp` seems to load `snli` from some URL based on nlp.stanford.edu. This subdomain is frequently down -- including right now, when I'd like to load `snli` in a Colab notebook, but can't. Is there a plan to move these datasets to huggingface servers for a more stable solution? Btw, here's the stack trace: ``` File "/content/nlp/src/nlp/builder.py", line 432, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/content/nlp/src/nlp/builder.py", line 466, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/content/nlp/src/nlp/datasets/snli/e417f6f2e16254938d977a17ed32f3998f5b23e4fcab0f6eb1d28784f23ea60d/snli.py", line 76, in _split_generators dl_dir = dl_manager.download_and_extract(_DATA_URL) File "/content/nlp/src/nlp/utils/download_manager.py", line 217, in download_and_extract return self.extract(self.download(url_or_urls)) File "/content/nlp/src/nlp/utils/download_manager.py", line 156, in download lambda url: cached_path(url, download_config=self._download_config,), url_or_urls, File "/content/nlp/src/nlp/utils/py_utils.py", line 190, in map_nested return function(data_struct) File "/content/nlp/src/nlp/utils/download_manager.py", line 156, in <lambda> lambda url: cached_path(url, download_config=self._download_config,), url_or_urls, File "/content/nlp/src/nlp/utils/file_utils.py", line 198, in cached_path local_files_only=download_config.local_files_only, File "/content/nlp/src/nlp/utils/file_utils.py", line 356, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://nlp.stanford.edu/projects/snli/snli_1.0.zip ```

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MDExOlB1bGxSZXF1ZXN0NDQ2MjkyMTc4

More faiss control

closed

MEMBER

Allow users to specify a faiss index they created themselves, as sometimes indexes can be composite for examples

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MDU6SXNzdWU2NTMyNTA2MTE=

[Dataset requests] New datasets for Text Classification

open

MEMBER

We are missing a few datasets for Text Classification which is an important field. Namely, it would be really nice to add: - TREC-6 dataset (see here for instance: https://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.datasets.html#torchnlp.datasets.trec_dataset) **[done]** - Yelp-5 - Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]** - SST (Stanford Sentiment Treebank) **[include in glue]** - Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]** - Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification - 20 Newsgroups. The 20 Newsgroups dataset **[done]** - Sogou News dataset **[done]** - Reuters news. The Reuters-21578 dataset [165] **[done]** - DBpedia. The DBpedia dataset [170] - Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database - EUR-Lex. The EUR-Lex dataset - WOS. The Web Of Science (WOS) dataset **[done]** - PubMed. PubMed [173] - TREC-QA. TREC-QA - Quora. The Quora dataset [180] All these datasets are cited in https://arxiv.org/abs/2004.03705

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MDExOlB1bGxSZXF1ZXN0NDQ2MTA1Mjky

🐛[BugFix]fix seqeval

closed

CONTRIBUTOR

Fix seqeval process labels such as 'B', 'B-ARGM-LOC'

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MDExOlB1bGxSZXF1ZXN0NDQ1NDk0NTE4

add pandas dataset

closed

MEMBER

Create a dataset from serialized pandas dataframes. Usage: ```python from nlp import load_dataset dset = load_dataset("pandas", data_files="df.pkl")["train"] ```

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MDExOlB1bGxSZXF1ZXN0NDQ1NDczODYz

add from_pandas and from_dict

closed

MEMBER

I added two new methods to the `Dataset` class: - `from_pandas()` to create a dataset from a pandas dataframe - `from_dict()` to create a dataset from a dictionary (keys = columns) It uses the `pa.Table.from_pandas` and `pa.Table.from_pydict` funcitons to do so. It is also possible to specify the features types via `features=...` if there are ambiguities (null/nan values), otherwise the arrow schema is infered from the data automatically by pyarrow. One question that I have right now: + Should we also add a `save()` method that would write the dataset on the disk ? Right now if we create a `Dataset` using those two new methods, the data are kept in RAM. Then to reload it we can call the `from_file()` method.

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MDExOlB1bGxSZXF1ZXN0NDQ1MzQwMTQ1

Hyperpartisan news detection

closed

CONTRIBUTOR

Adding the hyperpartisan news detection dataset from PAN. This contains news article text, labelled with whether they're hyper-partisan and why kinds of biases they display. Implementation notes: - As with many PAN tasks, the data is hosted on [Zenodo](https://zenodo.org/record/1489920) and must be requested before use. I've used the manual download stuff for this, although the dataset is provided under a Creative Commons Attribution 4.0 International License, so we could host a version if we wanted to? - The 'bias' attribute doesn't exist for the 'byarticle' configuration. I've added an empty string to the class labels to deal with this. Is there a more standard value for empty data? - Should we always subclass `nlp.BuilderConfig`?

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MDExOlB1bGxSZXF1ZXN0NDQ1MjgwNjk3

Add OSCAR dataset

closed

CONTRIBUTOR

I don't know if tests pass, when I run them it tries to download the whole corpus which is around 3.5TB compressed and I don't have that kind of space. I'll really need some help with it 😅 Thanks!

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MDU6SXNzdWU2NTIxMDY1Njc=

'cp950' codec error from load_dataset('xtreme', 'tydiqa')

closed

CONTRIBUTOR

 I guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps : https://www.python.org/dev/peps/pep-0263/ I guess the error was triggered by the code " module = importlib.import_module(module_path)" at line 57 in the source code: nlp/src/nlp/load.py / (https://github.com/huggingface/nlp/blob/911d5596f9b500e39af8642fe3d1b891758999c7/src/nlp/load.py#L51) Any ideas? p.s. tried the same code on colab, that runs perfectly

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MDExOlB1bGxSZXF1ZXN0NDQ1MTg4MTUz

Add emotion dataset

closed

MEMBER

Hello 🤗 team! I am trying to add an emotion classification dataset ([link](https://github.com/dair-ai/emotion_dataset)) to `nlp` but I am a bit stuck about what I should do when the URL for the dataset is not a ZIP file, but just a pickled `pandas.DataFrame` (see [here](https://www.dropbox.com/s/607ptdakxuh5i4s/merged_training.pkl)). With the current implementation, running ```bash python nlp-cli test datasets/emotion --save_infos --all_configs ``` throws a `_pickle.UnpicklingError: invalid load key, '<'.` error (full stack trace below). The strange thing is that the path to the file does not carry the `.pkl` extension and instead appears to be some md5 hash (see the `FILE PATH` print statement in the stack trace). Note: I have checked that the `merged_training.pkl` file is not corrupted when I download it with `wget`. Any pointers on what I'm doing wrong would be greatly appreciated! **Stack trace** ``` INFO:nlp.load:Checking datasets/emotion/emotion.py for additional imports. INFO:filelock:Lock 140330435928512 acquired on datasets/emotion/emotion.py.lock INFO:nlp.load:Found main folder for dataset datasets/emotion/emotion.py at /Users/lewtun/git/nlp/src/nlp/datasets/emotion INFO:nlp.load:Creating specific version folder for dataset datasets/emotion/emotion.py at /Users/lewtun/git/nlp/src/nlp/datasets/emotion/59666994754d1b369228a749b695e377643d141fa98c6972be00407659788c7b INFO:nlp.load:Copying script file from datasets/emotion/emotion.py to /Users/lewtun/git/nlp/src/nlp/datasets/emotion/59666994754d1b369228a749b695e377643d141fa98c6972be00407659788c7b/emotion.py INFO:nlp.load:Couldn't find dataset infos file at datasets/emotion/dataset_infos.json INFO:nlp.load:Creating metadata file for dataset datasets/emotion/emotion.py at /Users/lewtun/git/nlp/src/nlp/datasets/emotion/59666994754d1b369228a749b695e377643d141fa98c6972be00407659788c7b/emotion.json INFO:filelock:Lock 140330435928512 released on datasets/emotion/emotion.py.lock INFO:nlp.builder:Generating dataset emotion (/Users/lewtun/.cache/huggingface/datasets/emotion/emotion/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source Downloading and preparing dataset emotion/emotion (download: Unknown size, generated: Unknown size, total: Unknown size) to /Users/lewtun/.cache/huggingface/datasets/emotion/emotion/1.0.0... INFO:nlp.builder:Generating split train 0 examples [00:00, ? examples/s]FILE PATH /Users/lewtun/.cache/huggingface/datasets/3615dcb52b7ba052ef63e1571894c4b67e8e12a6ab1ef2f756ec3c380bf48490 Traceback (most recent call last): File "nlp-cli", line 37, in <module> service.run() File "/Users/lewtun/git/nlp/src/nlp/commands/test.py", line 83, in run builder.download_and_prepare( File "/Users/lewtun/git/nlp/src/nlp/builder.py", line 431, in download_and_prepare self._download_and_prepare( File "/Users/lewtun/git/nlp/src/nlp/builder.py", line 483, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/lewtun/git/nlp/src/nlp/builder.py", line 664, in _prepare_split for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False): File "/Users/lewtun/miniconda3/envs/nlp/lib/python3.8/site-packages/tqdm/std.py", line 1129, in __iter__ for obj in iterable: File "/Users/lewtun/git/nlp/src/nlp/datasets/emotion/59666994754d1b369228a749b695e377643d141fa98c6972be00407659788c7b/emotion.py", line 87, in _generate_examples data = pickle.load(f) _pickle.UnpicklingError: invalid load key, '<'. ```

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MDU6SXNzdWU2NTE3NjEyMDE=

Supporting documents in ELI5

closed

NONE

I was attempting to use the ELI5 dataset, when I realized that huggingface does not provide the supporting documents (the source documents from the common crawl). Without the supporting documents, this makes the dataset about as useful for my project as a block of cheese, or some other more apt metaphor. According to facebook, the entire document collection is quite large. However, it would still be helpful to at least include a subset of the supporting documents i.e., having some data is better than having a block of cheese, in my case at least. If you choose not to include them, it would be helpful to have documentation mentioning this specifically. It is especially confusing because the hf nlp ELI5 dataset has the key `'document'` but there are no documents to be found :(

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MDExOlB1bGxSZXF1ZXN0NDQ0NzQwMTIw

Search qa

closed

CONTRIBUTOR

This PR adds the Search QA dataset used in **SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine**. The dataset has the following config name: - raw_jeopardy: raw data - train_test_val: which is the splitted version #336

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MDExOlB1bGxSZXF1ZXN0NDQ0Njc4NDEw

Fix nested tensorflow format

closed

MEMBER

In #339 and #337 we are thinking about adding a way to export datasets to tfrecords. However I noticed that it was not possible to do `dset.set_format("tensorflow")` on datasets with nested features like `squad`. I fixed that using a nested map operations to convert features to `tf.ragged.constant`. I also added tests on the `set_format` function.

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MDU6SXNzdWU2NTEzMzMxOTQ=

Features should be updated when `map()` changes schema

closed

MEMBER

`dataset.map()` can change the schema and column names. We should update the features in this case (with what is possible to infer).

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MDExOlB1bGxSZXF1ZXN0NDQ0MDcwMjEx

add fever dataset

closed

CONTRIBUTOR

This PR add the FEVER dataset https://fever.ai/ used in with the paper: FEVER: a large-scale dataset for Fact Extraction and VERification (https://arxiv.org/pdf/1803.05355.pdf). #336

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MDExOlB1bGxSZXF1ZXN0NDQ0MDA2Nzcy

Update cfq.py

closed

CONTRIBUTOR

Make the dataset name consistent with in the paper: Compositional Freebase Question => Compositional Freebase Questions.

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MDExOlB1bGxSZXF1ZXN0NDQzNzAyNTcw

Add dataset.export() to TFRecords

closed

CONTRIBUTOR

Fixes https://github.com/huggingface/nlp/issues/337 Some design decisions: - Simplified the function API to not handle sharding. It writes the entire dataset as a single TFRecord file. This simplifies the function logic and users can use other functions (`select`, `shard`, etc) to handle custom sharding or splitting. - Use `from_generator()` instead of `from_tensor_slices()` to address the memory issues discussed in https://github.com/huggingface/nlp/issues/315 and https://github.com/huggingface/nlp/issues/193. - Performs introspection using the values from `dataset.set_format()` to identify the TF datatypes. Currently it supports string, float, and int. If this should be extended for other datatypes, let me know. - There are quite a few helper functions required within the `export()` method. If these are better placed in a utils file somewhere, let me know. Also, I noticed that ```python dataset = dataset.select(indices) dataset.set_format("tensorflow") # dataset._format_type is "tensorflow" ``` gives a different output than ```python dataset.set_format("tensorflow") dataset = dataset.select(indices) # dataset._format_type is None ``` The latter loses the format of its parent dataset. Is there interest in making `set_format` a functional method that returns itself (can be chained), and that derived datasets maintain the format of their parent?

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MDExOlB1bGxSZXF1ZXN0NDQzNjIxMTEx

Run `make style`

closed

CONTRIBUTOR

These files get changed when I run `make style` on an unrelated PR. Upstreaming these changes so development on a different branch can be easier.

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MDU6SXNzdWU2NTAwMzU4ODc=

[Feature request] Export Arrow dataset to TFRecords

closed

CONTRIBUTOR

The TFRecord generation process is error-prone and requires complex separate Python scripts to download and preprocess the data. I propose to combine the user-friendly features of `nlp` with the speed and efficiency of TFRecords. Sample API: ```python # use these existing methods ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train") ds = ds.map(lambda ex: tokenizer(ex)) ds.set_format("tensorflow", columns=["input_ids", "token_type_ids", "attention_mask"]) # then add this method ds.export(folder="/my/tfrecords", prefix="myrecord", num_shards=8, format="tfrecord") ``` which would create files like so: ```bash /my/tfrecords/myrecord_1.tfrecord /my/tfrecords/myrecord_2.tfrecord ... ``` I would be happy to contribute this method. We could use a similar approach for PyTorch. Thoughts?

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MDU6SXNzdWU2NDk5MTQyMDM=

[Dataset requests] New datasets for Open Question Answering

closed

MEMBER

We are still a few datasets missing for Open-Question Answering which is currently a field in strong development. Namely, it would be really nice to add: - WebQuestions (Berant et al., 2013) [done] - CuratedTrec (Baudis et al. 2015) [not open-source] - MS-MARCO (NGuyen et al. 2016) [done] - SearchQA (Dunn et al. 2017) [done] - FEVER (Thorne et al. 2018) - [ done] All these datasets are cited in http://arxiv.org/abs/2005.11401

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MDExOlB1bGxSZXF1ZXN0NDQzMzgwMjI1

BioMRC Dataset presented in BioNLP 2020 ACL Workshop

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NDQzMjk1NjQ0

Add dataset.shard() method

closed

CONTRIBUTOR

Fixes https://github.com/huggingface/nlp/issues/312

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MDExOlB1bGxSZXF1ZXN0NDQyOTE1NDQ0

fix variable name typo

closed

MEMBER

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MDExOlB1bGxSZXF1ZXN0NDQyODMwMzMz

Add wiki_dpr

closed

MEMBER

Presented in the [Dense Passage Retrieval paper](https://arxiv.org/pdf/2004.04906.pdf), this dataset consists in 21M passages from the english wikipedia along with their 768-dim embeddings computed using DPR's context encoder. Note on the implementation: - There are two configs: with and without the embeddings (73GB vs 14GB) - I used a non-fixed-size sequence of floats to describe the feature format of the embeddings. I wanted to use fixed-size sequences but I had issues with reading the arrow file afterwards (for example `dataset[0]` was crashing) - I added the case for lists of urls as input of the download_manager

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MDU6SXNzdWU2NDg1MzMxOTk=

Loading CNN/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError`

closed

CONTRIBUTOR

``` >>> import nlp >>> nlp.load_dataset('cnn_dailymail', '3.0.0') Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/p/qdata/jm8wx/datasets/nlp/src/nlp/load.py", line 520, in load_dataset builder_instance.download_and_prepare( File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 431, in download_and_prepare self._download_and_prepare( File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 488, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/p/qdata/jm8wx/datasets/nlp/src/nlp/utils/info_utils.py", line 70, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}] ```

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MDExOlB1bGxSZXF1ZXN0NDQyMzIxMjEw

Doc red

closed

CONTRIBUTOR

Adding [DocRED](https://github.com/thunlp/DocRED) - a relation extraction dataset which tests document-level RE. A few implementation notes: - There are 2 separate versions of the training set - *annotated* and *distant*. Instead of `nlp.Split.Train` I've used the splits `"train_annotated"` and `"train_distant"` to reflect this. - As well as the relation id, the full relation name is mapped from `rel_info.json` - I renamed the 'h', 'r', 't' keys to 'head', 'relation' and 'tail' to make them more readable. - Used the fix from #319 to allow nested sequences of dicts.

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MDU6SXNzdWU2NDg0NDY5Nzk=

[Bug] FileLock dependency incompatible with filesystem

closed

CONTRIBUTOR

I'm downloading a dataset successfully with `load_dataset("wikitext", "wikitext-2-raw-v1")` But when I attempt to cache it on an external volume, it hangs indefinitely: `load_dataset("wikitext", "wikitext-2-raw-v1", cache_dir="/fsx") # /fsx is an external volume mount` The filesystem when hanging looks like this: ```bash /fsx ----downloads ----94be...73.lock ----wikitext ----wikitext-2-raw ----wikitext-2-raw-1.0.0.incomplete ``` It appears that on this filesystem, the FileLock object is forever stuck in its "acquire" stage. I have verified that the issue lies specifically with the `filelock` dependency: ```python open("/fsx/hello.txt").write("hello") # succeeds from filelock import FileLock with FileLock("/fsx/hello.lock"): open("/fsx/hello.txt").write("hello") # hangs indefinitely ``` Has anyone else run into this issue? I'd raise it directly on the FileLock repo, but that project appears abandoned with the last update over a year ago. Or if there's a solution that would remove the FileLock dependency from the project, I would appreciate that.

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MDU6SXNzdWU2NDgzMjY4NDE=

Fork dataset

closed

NONE

We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. We're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers. Our preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads. Is there some good way to "fork" dataset- EG 1. text + json -> Dataset1 1. Dataset1 -> DatasetNER 1. Dataset1 -> DatasetREL or 1. text + json -> Dataset1 1. Dataset1 -> DatasetNER 1. Dataset1 + DatasetNER -> DatasetREL

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MDExOlB1bGxSZXF1ZXN0NDQyMTQyOTQw

set seed for suffling tests

closed

MEMBER

Some tests were randomly failing because of a missing seed in a test for `train_test_split(shuffle=True)`

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MDU6SXNzdWU2NDgxMjYxMDM=

Large dataset in Squad2-format

closed

NONE

At the moment we are building an large question answering dataset and think about sharing it with the huggingface community. Caused the computing power we splitted it into multiple tiles, but they are all in the same format. Right now the most important facts about are this: - Contexts: 1.047.671 - questions: 1.677.732 - Answers: 6.742.406 - unanswerable: 377.398 It is already cleaned <pre><code> train_data = [ { 'context': "this is the context", 'qas': [ { 'id': "00002", 'is_impossible': False, 'question': "whats is this", 'answers': [ { 'text': "answer", 'answer_start': 0 } ] }, { 'id': "00003", 'is_impossible': False, 'question': "question2", 'answers': [ { 'text': "answer2", 'answer_start': 1 } ] } ] } ] </code></pre> Cause it is growing every day we are thinking about an structure like this: We host an Json file, containing all the download links and the script can load it dynamically. At the moment it is around ~20GB Any advice how to handle this, or an ready to use template ?

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MDExOlB1bGxSZXF1ZXN0NDQxNTk3NTgw

Add SQuADShifts dataset

closed

CONTRIBUTOR

This PR adds the four new variants of the SQuAD dataset used in [The Effect of Natural Distribution Shift on Question Answering Models](https://arxiv.org/abs/2004.14444) to facilitate evaluating model robustness to distribution shift.

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MDU6SXNzdWU2NDc1MjU3MjU=

Error when calculating glue score

closed

NONE

I was trying glue score along with other metrics here. But glue gives me this error; ``` import nlp glue_metric = nlp.load_metric('glue',name="cola") glue_score = glue_metric.compute(predictions, references) ``` ``` --------------------------------------------------------------------------- --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-8-b9210a524504> in <module>() ----> 1 glue_score = glue_metric.compute(predictions, references) 6 frames /usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs) 191 """ 192 if predictions is not None: --> 193 self.add_batch(predictions=predictions, references=references) 194 self.finalize(timeout=timeout) 195 /usr/local/lib/python3.6/dist-packages/nlp/metric.py in add_batch(self, predictions, references, **kwargs) 207 if self.writer is None: 208 self._init_writer() --> 209 self.writer.write_batch(batch) 210 211 def add(self, prediction=None, reference=None, **kwargs): /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 155 if self.pa_writer is None: 156 self._build_writer(pa_table=pa.Table.from_pydict(batch_examples)) --> 157 pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema) 158 if writer_batch_size is None: 159 writer_batch_size = self.writer_batch_size /usr/local/lib/python3.6/dist-packages/pyarrow/types.pxi in __iter__() /usr/local/lib/python3.6/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray() /usr/local/lib/python3.6/dist-packages/pyarrow/array.pxi in pyarrow.lib.array() /usr/local/lib/python3.6/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() TypeError: an integer is required (got type str) ``` I'm not sure whether I'm doing this wrong or whether it's an issue. I would like to know a workaround. Thank you.

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MDExOlB1bGxSZXF1ZXN0NDQxNTMxOTY3

Add package path to sys when downloading package as github archive

closed

MEMBER

This fixes the `coval.py` metric so that imports within the downloaded module work correctly. We can use a similar trick to add the BLEURT metric (@ankparikh) @thomwolf not sure how you feel about adding to the `PYTHONPATH` from the script. This is the only way I could make it work with my understanding of `importlib` but there might be a more elegant method. This PR fixes https://github.com/huggingface/nlp/issues/305

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MDExOlB1bGxSZXF1ZXN0NDQxNTAyMjc2

output nested dict in get_nearest_examples

closed

MEMBER

As we are using a columnar format like arrow as the backend for datasets, we expect to have a dictionary of columns when we slice a dataset like in this example: ```python my_examples = dataset[0:10] print(type(my_examples)) # >>> dict print(my_examples["my_column"][0] # >>> this is the first element of the column 'my_column' ``` Therefore I wanted to keep this logic when calling `get_nearest_examples` that returns the top 10 nearest examples: ```python dataset.add_faiss_index(column="embeddings") scores, examples = dataset.get_nearest_examples("embeddings", query=my_numpy_embedding) print(type(examples)) # >>> dict ``` Previously it was returning a list[dict]. It was the only place that was using this output format. To make it work I had to implement `__getitem__(key)` where `key` is a list. This is different from `.select` because `.select` is a dataset transform (it returns a new dataset object) while `__getitem__` is an extraction method (it returns python dictionaries).

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MDU6SXNzdWU2NDcyNzE1MjY=

ERROR:root:mwparserfromhell

open

NONE

Hi, I am trying to download some wikipedia data but I got this error for spanish "es" (but there are maybe some others languages which have the same error I haven't tried all of them ). `ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.` The code I have use was : `dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`

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MDU6SXNzdWU2NDcxODgxNjc=

Blog Authorship Corpus, Non Matching Splits Sizes Error, nlp viewer

closed

CONTRIBUTOR

Selecting `blog_authorship_corpus` in the nlp viewer throws the following error: ``` NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}] Traceback: File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script exec(code, module.__dict__) File "/home/sasha/nlp-viewer/run.py", line 172, in <module> dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None) File "/home/sasha/streamlit/lib/streamlit/caching.py", line 591, in wrapped_func return get_or_create_cached_value() File "/home/sasha/streamlit/lib/streamlit/caching.py", line 575, in get_or_create_cached_value return_value = func(*args, **kwargs) File "/home/sasha/nlp-viewer/run.py", line 132, in get builder_instance.download_and_prepare() File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 488, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 70, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) ``` @srush @lhoestq

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MDU6SXNzdWU2NDY3OTI0ODc=

Nested sequences with dicts

closed

CONTRIBUTOR

Am pretty much finished [adding a dataset](https://github.com/ghomasHudson/nlp/blob/DocRED/datasets/docred/docred.py) for [DocRED](https://github.com/thunlp/DocRED), but am getting an error when trying to add a nested `nlp.features.sequence(nlp.features.sequence({key:value,...}))`. The original data is in this format: ```python { 'title': "Title of wiki page", 'vertexSet': [ [ { 'name': "mention_name", 'sent_id': "mention in which sentence", 'pos': ["postion of mention in a sentence"], 'type': "NER_type"}, {another mention} ], [another entity] ] ... } ``` So to represent this I've attempted to write: ``` ... features=nlp.Features({ "title": nlp.Value("string"), "vertexSet": nlp.features.Sequence(nlp.features.Sequence({ "name": nlp.Value("string"), "sent_id": nlp.Value("int32"), "pos": nlp.features.Sequence(nlp.Value("int32")), "type": nlp.Value("string"), })), ... }), ... ``` This is giving me the error: ``` pyarrow.lib.ArrowTypeError: Could not convert [{'pos': [[0,2], [2,4], [3,5]], "type": ["ORG", "ORG", "ORG"], "name": ["Lark Force", "Lark Force", "Lark Force", "sent_id": [0, 3, 4]}..... with type list: was not a dict, tuple, or recognized null value for conversion to struct type ``` Do we expect the pyarrow stuff to break when doing this deeper nesting? I've checked that it still works when you do `nlp.features.Sequence(nlp.features.Sequence(nlp.Value("string"))` or `nlp.features.Sequence({key:value,...})` just not nested sequences with a dict. If it's not possible, I can always convert it to a shallower structure. I'd rather not change the DocRED authors' structure if I don't have to though.

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MDExOlB1bGxSZXF1ZXN0NDQwOTExOTYy

Multitask

open

CONTRIBUTOR

Following our discussion in #217, I've implemented a first working version of `MultiDataset`. There's a function `build_multitask()` which takes either individual `nlp.Dataset`s or `dicts` of splits and constructs `MultiDataset`(s). I've added a notebook with example usage. I've implemented many of the `nlp.Dataset` methods (cache_files, columns, nbytes, num_columns, num_rows, column_names, schema, shape). Some of the other methods are complicated as they change the number of examples. These raise `NotImplementedError`s at the moment. This will need some tests which I haven't written yet. There's definitely room for improvements but I think the general approach is sound.

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MDU6SXNzdWU2NDY1NTUzODQ=

Adding a dataset with multiple subtasks

closed

NONE

I intent to add the datasets of the MT Quality Estimation shared tasks to `nlp`. However, they have different subtasks -- such as word-level, sentence-level and document-level quality estimation, each of which having different language pairs, and some of the data reused in different subtasks. For example, in [QE 2019,](http://www.statmt.org/wmt19/qe-task.html) we had the same English-Russian and English-German data for word-level and sentence-level QE. I suppose these datasets could have both their word and sentence-level labels inside `nlp.Features`; but what about other subtasks? Should they be considered a different dataset altogether? I read the discussion on #217 but the case of QE seems a lot simpler.

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https://github.com/huggingface/datasets/pull/316

MDExOlB1bGxSZXF1ZXN0NDQwNjY5NzY5

add AG News dataset

closed

CONTRIBUTOR

adds support for the AG-News topic classification dataset

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MDU6SXNzdWU2NDU4ODg5NDM=

[Question] Best way to batch a large dataset?

open

CONTRIBUTOR

I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow: ```python train_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False) columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions'] train_tf_dataset.set_format(type='tensorflow', columns=columns) features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])} labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1]) ### Question about this last line ### tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8) ``` This code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia. So I tried manual batching using `dataset.select()`: ```python idxs = np.random.randint(len(dataset), size=bsz) batch = dataset.select(idxs).map(lambda example: {"input_ids": tokenizer(example["text"])}) tf_batch = tf.constant(batch["ids"], dtype=tf.int64) ``` This appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop. Is there a performant scalable way to lazily load batches of nlp Datasets?

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https://github.com/huggingface/datasets/pull/314

MDExOlB1bGxSZXF1ZXN0NDM5OTM4MTMw

Fixed singlular very minor spelling error

closed

CONTRIBUTOR

An instance of "independantly" was changed to "independently". That's all.

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https://github.com/huggingface/datasets/pull/313

MDExOlB1bGxSZXF1ZXN0NDM5ODc4MDg5

Add MWSC

closed

CONTRIBUTOR

Adding the [Modified Winograd Schema Challenge](https://github.com/salesforce/decaNLP/blob/master/local_data/schema.txt) dataset which formed part of the [decaNLP](http://decanlp.com/) benchmark. Not sure how much use people would find for it it outside of the benchmark, but it is general purpose. Code is heavily borrowed from the [decaNLP repo](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L773-L877). There's a few (possibly overly opinionated) design choices I made: - I used the train/test/dev split [buried in the decaNLP code](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L852-L855) - I split out each example into the 2 alternatives. Originally the data uses the format: ``` The city councilmen refused the demonstrators a permit because they [feared/advocated] violence. Who [feared/advocated] violence? councilmen/demonstrators ``` I split into the 2 variants: ``` The city councilmen refused the demonstrators a permit because they feared violence. Who feared violence? councilmen/demonstrators The city councilmen refused the demonstrators a permit because they advocated violence. Who advocated violence? councilmen/demonstrators ``` I can't see any use for having the options combined into a single example (splitting them is [the way decaNLP processes](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L846-L850)) them. You can't train on both versions with them combined, and splitting the examples later would be a pain to do. I think [winogrande.py](https://github.com/huggingface/nlp/blob/master/datasets/winogrande/winogrande.py) presents the data in this way? - I've not used the decaNLP framing (appending the options to the question e.g. `Who feared violence? -- councilmen or demonstrators?`) but left it more generic by adding the options as a new key: `"options":["councilmen","demonstrators"]` This should be an easy thing to change using `map` if needed by a specific application. Dataset is working as-is but if anyone has any thoughts/preferences on the design decisions here I'm definitely open to different choices.

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MDU6SXNzdWU2NDUwMjU1NjE=

[Feature request] Add `shard()` method to dataset

closed

CONTRIBUTOR

Currently, to shard a dataset into 10 pieces on different ranks, you can run ```python rank = 3 # for example size = 10 dataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f"train[{rank*10}%:{(rank+1)*10}%]") ``` However, this breaks down if you have a number of ranks that doesn't divide cleanly into 100, such as 64 ranks. Is there interest in adding a method shard() that looks like this? ```python rank = 3 size = 64 dataset = nlp.load_dataset("wikitext", "wikitext-2-raw-v1", split="train").shard(rank=rank, size=size) ``` TensorFlow has a similar API: https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard. I'd be happy to contribute this code.

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https://github.com/huggingface/datasets/pull/311

MDExOlB1bGxSZXF1ZXN0NDM5NTQ3OTg0

Add qa_zre

closed

CONTRIBUTOR

Adding the QA-ZRE dataset from ["Zero-Shot Relation Extraction via Reading Comprehension"](http://nlp.cs.washington.edu/zeroshot/). A common processing step seems to be replacing the `XXX` placeholder with the `subject`. I've left this out as it's something you could easily do with `map`.

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MDExOlB1bGxSZXF1ZXN0NDM5MzY1MDg5

add wikisql

closed

CONTRIBUTOR

Adding the [WikiSQL](https://github.com/salesforce/WikiSQL) dataset. Interesting things to note: - Have copied the function (`_convert_to_human_readable`) which converts the SQL query to a human-readable (string) format as this is what most people will want when actually using this dataset for NLP applications. - `conds` was originally a tuple but is converted to a dictionary to support differing types. Would be nice to add the logical_form metrics too at some point.

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https://github.com/huggingface/datasets/pull/309

MDExOlB1bGxSZXF1ZXN0NDM5MzQ1NzYz

Add narrative qa

closed

NONE

Test cases for dummy data don't pass Only contains data for summaries (not whole story)

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MDExOlB1bGxSZXF1ZXN0NDM4ODYyMzYy

Specify utf-8 encoding for MRPC files

closed

CONTRIBUTOR

Fixes #307, again probably a Windows-related issue.

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MDU6SXNzdWU2NDQxODcyNjI=

Specify encoding for MRPC

closed

CONTRIBUTOR

Same as #242, but with MRPC: on Windows, I get a `UnicodeDecodeError` when I try to download the dataset: ```python dataset = nlp.load_dataset('glue', 'mrpc') ``` ```python Downloading and preparing dataset glue/mrpc (download: Unknown size, generated: Unknown size, total: Unknown size) to C:\Users\Python\.cache\huggingface\datasets\glue\mrpc\1.0.0... --------------------------------------------------------------------------- UnicodeDecodeError Traceback (most recent call last) ~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in incomplete_dir(dirname) 369 try: --> 370 yield tmp_dir 371 if os.path.isdir(dirname): ~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 430 verify_infos = not save_infos and not ignore_verifications --> 431 self._download_and_prepare( 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs ~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 482 # Prepare split will record examples associated to the split --> 483 self._prepare_split(split_generator, **prepare_split_kwargs) 484 except OSError: ~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _prepare_split(self, split_generator) 663 generator = self._generate_examples(**split_generator.gen_kwargs) --> 664 for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False): 665 example = self.info.features.encode_example(record) ~\Miniconda3\envs\nlp\lib\site-packages\tqdm\notebook.py in __iter__(self, *args, **kwargs) 217 try: --> 218 for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs): 219 # return super(tqdm...) will not catch exception ~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self) 1128 try: -> 1129 for obj in iterable: 1130 yield obj ~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\7fc58099eb3983a04c8dac8500b70d27e6eceae63ffb40d7900c977897bb58c6\glue.py in _generate_examples(self, data_file, split, mrpc_files) 514 examples = self._generate_example_mrpc_files(mrpc_files=mrpc_files, split=split) --> 515 for example in examples: 516 yield example["idx"], example ~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\7fc58099eb3983a04c8dac8500b70d27e6eceae63ffb40d7900c977897bb58c6\glue.py in _generate_example_mrpc_files(self, mrpc_files, split) 576 reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) --> 577 for n, row in enumerate(reader): 578 is_row_in_dev = [row["#1 ID"], row["#2 ID"]] in dev_ids ~\Miniconda3\envs\nlp\lib\csv.py in __next__(self) 110 self.fieldnames --> 111 row = next(self.reader) 112 self.line_num = self.reader.line_num ~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final) 22 def decode(self, input, final=False): ---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0] 24 UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 1180: character maps to <undefined> ``` The fix is the same: specify `utf-8` encoding when opening the file. The previous fix didn't work as MRPC's download process is different from the others in GLUE. I am going to propose a new PR :)

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MDExOlB1bGxSZXF1ZXN0NDM4ODQ2MTI3

add pg19 dataset

closed

CONTRIBUTOR

https://github.com/huggingface/nlp/issues/274 Add functioning PG19 dataset with dummy data `cos_e.py` was just auto-linted by `make style`

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MDU6SXNzdWU2NDQxNDgxNDk=

Importing downloaded package repository fails

closed

MEMBER

The `get_imports` function in `src/nlp/load.py` has a feature to download a package as a zip archive of the github repository and import functions from the unpacked directory. This is used for example in the `metrics/coval.py` file, and would be useful to add BLEURT (@ankparikh). Currently however, the code seems to have trouble with imports within the package. For example: ``` import nlp coval = nlp.load_metric('coval') ``` yields: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/yacine/Code/nlp/src/nlp/load.py", line 432, in load_metric metric_cls = import_main_class(module_path, dataset=False) File "/home/yacine/Code/nlp/src/nlp/load.py", line 57, in import_main_class module = importlib.import_module(module_path) File "/home/yacine/anaconda3/lib/python3.7/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1006, in _gcd_import 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 677, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 728, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/yacine/Code/nlp/src/nlp/metrics/coval/a78807df33ac45edbb71799caf2b3b47e55df4fd690267808fe963a5e8b30952/coval.py", line 21, in <module> from .coval_backend.conll import reader # From: https://github.com/ns-moosavi/coval File "/home/yacine/Code/nlp/src/nlp/metrics/coval/a78807df33ac45edbb71799caf2b3b47e55df4fd690267808fe963a5e8b30952/coval_backend/conll/reader.py", line 2, in <module> from conll import mention ModuleNotFoundError: No module named 'conll' ``` Not sure what the fix would be there.

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MDU6SXNzdWU2NDQwOTE5NzA=

Problem while printing doc string when instantiating multiple metrics.

closed

CONTRIBUTOR

When I load more than one metric and try to print doc string of a particular metric,. It shows the doc strings of all imported metric one after the other which looks quite confusing and clumsy. Attached [Colab](https://colab.research.google.com/drive/13H0ZgyQ2se0mqJ2yyew0bNEgJuHaJ8H3?usp=sharing) Notebook for problem clarification..

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https://github.com/huggingface/datasets/pull/303

MDExOlB1bGxSZXF1ZXN0NDM4NjI3Nzcw

allow to move files across file systems

closed

MEMBER

Users are allowed to use the `cache_dir` that they want. Therefore it can happen that we try to move files across filesystems. We were using `os.rename` that doesn't allow that, so I changed some of them to `shutil.move`. This should fix #301

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MDU6SXNzdWU2NDM5MTA0MTg=

Question - Sign Language Datasets

closed

CONTRIBUTOR

An emerging field in NLP is SLP - sign language processing. I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable. The metrics for sign language to text translation are the same. So, what do you think about (me, or others) adding datasets here? An example dataset would be [RWTH-PHOENIX-Weather 2014 T](https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/) For every item in the dataset, the data object includes: 1. video_path - path to mp4 file 2. pose_path - a path to `.pose` file with human pose landmarks 3. openpose_path - a path to a `.json` file with human pose landmarks 4. gloss - string 5. text - string 6. video_metadata - height, width, frames, framerate ------ To make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so.

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MDU6SXNzdWU2NDM3NjM1MjU=

Setting cache_dir gives error on wikipedia download

closed

NONE

First of all thank you for a super handy library! I'd like to download large files to a specific drive so I set `cache_dir=my_path`. This works fine with e.g. imdb and squad. But on wikipedia I get an error: ``` nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=my_path) ``` ``` OSError Traceback (most recent call last) <ipython-input-2-23551344d7bc> in <module> 1 import nlp ----> 2 nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=path) ~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 522 download_mode=download_mode, 523 ignore_verifications=ignore_verifications, --> 524 save_infos=save_infos, 525 ) 526 ~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 385 with utils.temporary_assignment(self, "_cache_dir", tmp_data_dir): 386 reader = ArrowReader(self._cache_dir, self.info) --> 387 reader.download_from_hf_gcs(self._cache_dir, self._relative_data_dir(with_version=True)) 388 downloaded_info = DatasetInfo.from_directory(self._cache_dir) 389 self.info.update(downloaded_info) ~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/arrow_reader.py in download_from_hf_gcs(self, cache_dir, relative_data_dir) 231 remote_dataset_info = os.path.join(remote_cache_dir, "dataset_info.json") 232 downloaded_dataset_info = cached_path(remote_dataset_info) --> 233 os.rename(downloaded_dataset_info, os.path.join(cache_dir, "dataset_info.json")) 234 if self._info is not None: 235 self._info.update(self._info.from_directory(cache_dir)) OSError: [Errno 18] Invalid cross-device link: '/home/local/NTU/nn/.cache/huggingface/datasets/025fa4fd4f04aaafc9e939260fbc8f0bb190ce14c61310c8ae1ddd1dcb31f88c.9637f367b6711a79ca478be55fe6989b8aea4941b7ef7adc67b89ff403020947' -> '/data/nn/nlp/wikipedia/20200501.de/1.0.0.incomplete/dataset_info.json' ```

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https://github.com/huggingface/datasets/pull/300

MDExOlB1bGxSZXF1ZXN0NDM4NDQ4Mjk1

Fix bertscore references

closed

MEMBER

I added some type checking for metrics. There was an issue where a metric could interpret a string a a list. A `ValueError` is raised if a string is given instead of a list. Moreover I added support for both strings and lists of strings for `references` in `bertscore`, as it is the case in the original code. Both ways work: ``` import nlp scorer = nlp.load_metric("bertscore") with open("pred.txt") as p, open("ref.txt") as g: for lp, lg in zip(p, g): scorer.add(lp, [lg]) score = scorer.compute(lang="en") ``` ``` import nlp scorer = nlp.load_metric("bertscore") with open("pred.txt") as p, open("ref.txt") as g: for lp, lg in zip(p, g): scorer.add(lp, lg) score = scorer.compute(lang="en") ``` This should fix #295 and #238

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MDExOlB1bGxSZXF1ZXN0NDM4Mzg0NDgw

remove some print in snli file

closed

CONTRIBUTOR

This PR removes unwanted `print` statements in some files such as `snli.py`

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https://github.com/huggingface/datasets/pull/298

MDExOlB1bGxSZXF1ZXN0NDM4Mzc4MDM4

Add searchable datasets

closed

MEMBER

# Better support for Numpy format + Add Indexed Datasets I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib. ## Better support for Numpy format New features: - New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas. - Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays. Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures. Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format. With these changes you can easily compute embeddings of texts using `.map()`. For example: ```python def embed(text): tokenized_example = tokenizer.encode(text, return_tensors="pt") embeddings = bert_encoder(tokenized_examples).numpy() return embeddings dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])}) ``` And then reading the embeddings from the arrow format is be very fast. PS1: Note that right now only 1d arrays are supported. PS2: It seems possible to do without pandas but it will require more _trickery_. PS3: I did a simple benchmark with google colab that you can view here: https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing ## Add Indexed Datasets For many retrieval tasks it is convenient to index a dataset to be able to run fast queries. For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important. Therefore I added two ways to add an index to a column of a dataset: 1) You can index it using a Dense Index like Faiss. It is used to index vectors. Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. 2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity. Example of usage: ```python ds = nlp.load_dataset('crime_and_punish', split='train') ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array` ds_with_embeddings.add_vector_index(column='embeddings') scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10) ``` ```python ds = nlp.load_dataset('crime_and_punish', split='train') es_client = elasticsearch.Elasticsearch() ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index") scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10) ``` PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings. ## Tests I added tests for Faiss, Elasticsearch and indexed datasets. I had to edit the CI config because all the test scripts were not being run by CircleCI. ------------------ I'd be really happy to have some feedbacks :)

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MDU6SXNzdWU2NDM0NDQ2MjU=

Error in Demo for Specific Datasets

closed

NONE

Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following. 

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MDU6SXNzdWU2NDM0MjM3MTc=

snli -1 labels

closed

CONTRIBUTOR

I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels? ``` import nlp from collections import Counter data = nlp.load_dataset('snli')['train'] print(Counter(data['label'])) Counter({0: 183416, 2: 183187, 1: 182764, -1: 785}) ```

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MDU6SXNzdWU2NDMyNDU0MTI=

Improve input warning for evaluation metrics

closed