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https://api.github.com/repos/huggingface/datasets/issues/318

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https://github.com/huggingface/datasets/pull/318

MDExOlB1bGxSZXF1ZXN0NDQwOTExOTYy

Multitask

closed

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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https://github.com/huggingface/datasets/issues/317

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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https://github.com/huggingface/datasets/issues/315

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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https://github.com/huggingface/datasets/pull/310

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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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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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

NONE

Hi, I am the author of `bert_score`. Recently, we received [ an issue ](https://github.com/Tiiiger/bert_score/issues/62) reporting a problem in using `bert_score` from the `nlp` package (also see #238 in this repo). After looking into this, I realized that the problem arises from the format `nlp.Metric` takes input. Here is a minimal example: ```python 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") ``` The problem in the above code is that `scorer.add()` expects a list of strings as input for the references. As a result, the `scorer` here would take a list of characters in `lg` to be the references. The correct implementation would be calling ```python scorer.add(lp, [lg]) ``` I just want to raise this issue to you to prevent future user errors of a similar kind. I assume some simple type checking can prevent this from happening? Thanks!

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MDU6SXNzdWU2NDMxODExNzk=

Cannot load arxiv dataset on MacOS?

closed

CONTRIBUTOR

I am having trouble loading the `"arxiv"` config from the `"scientific_papers"` dataset on MacOS. When I try loading the dataset with: ```python arxiv = nlp.load_dataset("scientific_papers", "arxiv") ``` I get the following stack trace: ```bash JSONDecodeError Traceback (most recent call last) <ipython-input-2-8e00c55d5a59> in <module> ----> 1 arxiv = nlp.load_dataset("scientific_papers", "arxiv") ~/miniconda3/envs/t2t/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 ~/miniconda3/envs/t2t/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) 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 ~/miniconda3/envs/t2t/lib/python3.7/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 "")) ~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/builder.py in _prepare_split(self, split_generator) 662 663 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) 666 writer.write(example) ~/miniconda3/envs/t2t/lib/python3.7/site-packages/tqdm/std.py in __iter__(self) 1106 fp_write=getattr(self.fp, 'write', sys.stderr.write)) 1107 -> 1108 for obj in iterable: 1109 yield obj 1110 # Update and possibly print the progressbar. ~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/datasets/scientific_papers/107a416c0e1958cb846f5934b5aae292f7884a5b27e86af3f3ef1a093e058bbc/scientific_papers.py in _generate_examples(self, path) 114 # "section_names": list[str], list of section names. 115 # "sections": list[list[str]], list of sections (list of paragraphs) --> 116 d = json.loads(line) 117 summary = "\n".join(d["abstract_text"]) 118 # In original paper, <S> and </S> are not used in vocab during training ~/miniconda3/envs/t2t/lib/python3.7/json/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 346 parse_int is None and parse_float is None and 347 parse_constant is None and object_pairs_hook is None and not kw): --> 348 return _default_decoder.decode(s) 349 if cls is None: 350 cls = JSONDecoder ~/miniconda3/envs/t2t/lib/python3.7/json/decoder.py in decode(self, s, _w) 335 336 """ --> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end()) 338 end = _w(s, end).end() 339 if end != len(s): ~/miniconda3/envs/t2t/lib/python3.7/json/decoder.py in raw_decode(self, s, idx) 351 """ 352 try: --> 353 obj, end = self.scan_once(s, idx) 354 except StopIteration as err: 355 raise JSONDecodeError("Expecting value", s, err.value) from None JSONDecodeError: Unterminated string starting at: line 1 column 46983 (char 46982) 163502 examples [02:10, 2710.68 examples/s] ``` I am not sure how to trace back to the specific JSON file that has the "Unterminated string". Also, I do not get this error on colab so I suspect it may be MacOS specific. Copy pasting the relevant lines from `transformers-cli env` below: - Platform: Darwin-19.5.0-x86_64-i386-64bit - Python version: 3.7.5 - PyTorch version (GPU?): 1.5.0 (False) - Tensorflow version (GPU?): 2.2.0 (False) Any ideas?

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MDExOlB1bGxSZXF1ZXN0NDM3ODM1ODI4

Don't test community datasets

closed

MEMBER

This PR disables testing for community datasets on aws. It should fix the CI that is currently failing.

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MDExOlB1bGxSZXF1ZXN0NDM3Nzk4NTM2

Update metadata for x_stance dataset

closed

CONTRIBUTOR

Thank you for featuring the x_stance dataset in your library. This PR updates some metadata: - Citation: Replace preprint with proceedings - URL: Use a URL with long-term availability

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MDExOlB1bGxSZXF1ZXN0NDM3NjM1NjMy

break statement not required

closed

NONE

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MDU6SXNzdWU2NDE5NzgyODY=

ConnectionError - Eli5 dataset download

closed

NONE

Hi, I have a problem with downloading Eli5 dataset. When typing `nlp.load_dataset('eli5')`, I get ConnectionError: Couldn't reach https://storage.googleapis.com/huggingface-nlp/cache/datasets/eli5/LFQA_reddit/1.0.0/explain_like_im_five-train_eli5.arrow I would appreciate if you could help me with this issue.

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MDExOlB1bGxSZXF1ZXN0NDM3MDc0MTM3

update xsum

closed

CONTRIBUTOR

This PR makes the following update to the xsum dataset: - Manual download is not required anymore - dataset can be loaded as follow: `nlp.load_dataset('xsum')` **Important** Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json" a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used , so that the user does not need to manually download the data anymore. There might be slight breaking changes here for xsum.

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MDU6SXNzdWU2NDE4ODg2MTA=

Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill'

closed

NONE

/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint8 = np.dtype([("qint8", np.int8, 1)]) /Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:470: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint8 = np.dtype([("quint8", np.uint8, 1)]) /Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:471: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint16 = np.dtype([("qint16", np.int16, 1)]) /Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:472: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_quint16 = np.dtype([("quint16", np.uint16, 1)]) /Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:473: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. _np_qint32 = np.dtype([("qint32", np.int32, 1)]) /Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:476: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. np_resource = np.dtype([("resource", np.ubyte, 1)]) /Users/parasol_tree/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6 return f(*args, **kwds) /Users/parasol_tree/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters Traceback (most recent call last): File "/Users/parasol_tree/Resource/019 - Github/AcademicEnglishToolkit /test.py", line 7, in <module> import nlp File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/__init__.py", line 27, in <module> from .arrow_dataset import Dataset File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/arrow_dataset.py", line 31, in <module> from nlp.utils.py_utils import dumps File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/__init__.py", line 20, in <module> from .download_manager import DownloadManager, GenerateMode File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/download_manager.py", line 25, in <module> from .py_utils import flatten_nested, map_nested, size_str File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/py_utils.py", line 244, in <module> class Pickler(dill.Pickler): File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/py_utils.py", line 247, in Pickler dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy()) AttributeError: module 'dill' has no attribute '_dill'

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MDExOlB1bGxSZXF1ZXN0NDM2OTY0NTg0

fix squad_v2 metric

closed

MEMBER

Fix #280 The imports were wrong

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MDExOlB1bGxSZXF1ZXN0NDM2NzkzMjI4

Add ANLI dataset.

closed

CONTRIBUTOR

I completed all the steps in https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset and push the code for ANLI. Please let me know if there are any errors.

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MDExOlB1bGxSZXF1ZXN0NDM2NjAyMjk4

Consistent formatting of citations

closed

CONTRIBUTOR

#283

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MDExOlB1bGxSZXF1ZXN0NDM2NTgxODQ2

Fix manual download instructions

closed

MEMBER

This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`. Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs. After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed. Also this PR should unblock solves a bug with `wmt16 - ro-en` @sshleifer from this branch you should be able to succesfully run ```python import nlp ds = nlp.load_dataset('./datasets/wmt16', 'ro-en') ``` and once this PR is merged S3 should be synched so that ```python import nlp ds = nlp.load_dataset("wmt16", "ro-en") ``` works as well. **Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility.

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MDU6SXNzdWU2NDEyNzA0Mzk=

Consistent formatting of citations

closed

CONTRIBUTOR

The citations are all of a different format, some have "```" and have text inside, others are proper bibtex. Can we make it so that they all are proper citations, i.e. parse by the bibtex spec: https://bibtexparser.readthedocs.io/en/master/

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MDExOlB1bGxSZXF1ZXN0NDM2NDgxNzMy

Update dataset_info from gcs

closed

MEMBER

Some datasets are hosted on gcs (wikipedia for example). In this PR I make sure that, when a user loads such datasets, the file_instructions are built using the dataset_info.json from gcs and not from the info extracted from the local `dataset_infos.json` (the one that contain the info for each config). Indeed local files may end up outdated. Furthermore, to avoid outdated dataset_infos.json, I now make sure that each time you run `load_dataset` it also tries to update the file locally.

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MDU6SXNzdWU2NDEwNjc4NTY=

Private/sensitive data

closed

CONTRIBUTOR

Hi all, Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch. Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information. Is there support/a plan to support such data with NLP, e.g. by reading it from local sources? Use case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive/private data without the need to rethink data processing pipelines. Many thanks for your responses ahead of time and kind regards, MFreidank

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MDU6SXNzdWU2NDA2Nzc2MTU=

Error with SquadV2 Metrics

closed

NONE

I can't seem to import squad v2 metrics. **squad_metric = nlp.load_metric('squad_v2')** **This throws me an error.:** ``` ImportError Traceback (most recent call last) <ipython-input-8-170b6a170555> in <module> ----> 1 squad_metric = nlp.load_metric('squad_v2') ~/env/lib64/python3.6/site-packages/nlp/load.py in load_metric(path, name, process_id, num_process, data_dir, experiment_id, in_memory, download_config, **metric_init_kwargs) 426 """ 427 module_path = prepare_module(path, download_config=download_config, dataset=False) --> 428 metric_cls = import_main_class(module_path, dataset=False) 429 metric = metric_cls( 430 name=name, ~/env/lib64/python3.6/site-packages/nlp/load.py in import_main_class(module_path, dataset) 55 """ 56 importlib.invalidate_caches() ---> 57 module = importlib.import_module(module_path) 58 59 if dataset: /usr/lib64/python3.6/importlib/__init__.py in import_module(name, package) 124 break 125 level += 1 --> 126 return _bootstrap._gcd_import(name[level:], package, level) 127 128 /usr/lib64/python3.6/importlib/_bootstrap.py in _gcd_import(name, package, level) /usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load(name, import_) /usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_) /usr/lib64/python3.6/importlib/_bootstrap.py in _load_unlocked(spec) /usr/lib64/python3.6/importlib/_bootstrap_external.py in exec_module(self, module) /usr/lib64/python3.6/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds) ~/env/lib64/python3.6/site-packages/nlp/metrics/squad_v2/a15e787c76889174874386d3def75321f0284c11730d2a57e28fe1352c9b5c7a/squad_v2.py in <module> 16 17 import nlp ---> 18 from .evaluate import evaluate 19 20 _CITATION = """\ ImportError: cannot import name 'evaluate' ```

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MDU6SXNzdWU2NDA2MTE2OTI=

Dataset Preprocessing Cache with .map() function not working as expected

closed

NONE

I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system. Is there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file. Could you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess. I was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set. Thanks!

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MDU6SXNzdWU2NDA1MTg5MTc=

MemoryError when loading German Wikipedia

closed

NONE

Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :) I'm trying to download the German Wikipedia dataset as follows: ``` wiki = nlp.load_dataset("wikipedia", "20200501.de", split="train") ``` However, when I do so, I get the following error: ``` Downloading and preparing dataset wikipedia/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/ubuntu/.cache/huggingface/datasets/wikipedia/20200501.de/1.0.0... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset save_infos=save_infos, File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 433, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 824, in _download_and_prepare "\n\t`{}`".format(usage_example) nlp.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.de', beam_runner='DirectRunner')` ``` So, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned. This isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset? My nlp version is 0.2.1. Thank you!

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MDU6SXNzdWU2NDAxNjMwNTM=

Empty samples in glue/qqp

closed

CONTRIBUTOR

``` qqp = nlp.load_dataset('glue', 'qqp') print(qqp['train'][310121]) print(qqp['train'][362225]) ``` ``` {'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137} {'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246} ``` Notice that question 2 is empty string. BTW, I have checked and these two are the only naughty ones in all splits of qqp.

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MDExOlB1bGxSZXF1ZXN0NDM1MDY5Nzg5

Fix metric compute (original_instructions missing)

closed

MEMBER

When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset. However metrics load data the same way but don't need instructions (we use one single file). In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object. This should fix #269

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MDU6SXNzdWU2Mzk0MzkwNTI=

NonMatchingChecksumError when loading pubmed dataset

closed

NONE

I get this error when i run `nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')`. The error is: ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-2-7742dea167d0> in <module>() ----> 1 df = nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]') 2 df = pd.DataFrame(df) 3 gc.collect() 3 frames /usr/local/lib/python3.6/dist-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) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 /usr/local/lib/python3.6/dist-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) 431 verify_infos = not save_infos and not ignore_verifications 432 self._download_and_prepare( --> 433 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 434 ) 435 # Sync info /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 468 # Checksums verification 469 if verify_infos: --> 470 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 471 for split_generator in split_generators: 472 if str(split_generator.split_info.name).lower() == "all": /usr/local/lib/python3.6/dist-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download'] ``` I'm currently working on google colab. That is quite strange because yesterday it was fine.

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MDU6SXNzdWU2MzkxNTY2MjU=

PG-19

closed

CONTRIBUTOR

Hi, and thanks for all your open-sourced work, as always! I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling.

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MDExOlB1bGxSZXF1ZXN0NDM0NjM0MzU4

update cos_e to add cos_e v1.0

closed

CONTRIBUTOR

This PR updates the cos_e dataset to add v1.0 as requested here #163 @nazneenrajani

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MDExOlB1bGxSZXF1ZXN0NDM0MTExOTQ3

asd

closed

NONE

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MDExOlB1bGxSZXF1ZXN0NDMzOTg3NDkw

Fix allociné dataset configuration

closed

CONTRIBUTOR

This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with : ```python dataset = load_dataset('allocine', 'allocine') ``` This is redundant, as there is only one "dataset configuration", and should only be: ```python dataset = load_dataset('allocine') ``` This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one). I believe this PR should solve this issue, making the Allociné dataset more convenient to use.

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MDU6SXNzdWU2MzgxMjE2MTc=

c4 dataset is not viewable in nlpviewer demo

closed

NONE

I get the following error when I try to view the c4 dataset in [nlpviewer](https://huggingface.co/nlp/viewer/) ```python ModuleNotFoundError: No module named 'langdetect' Traceback: File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script exec(code, module.__dict__) File "/home/sasha/nlp_viewer/run.py", line 54, in <module> configs = get_confs(option.id) File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 591, in wrapped_func return get_or_create_cached_value() File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 575, in get_or_create_cached_value return_value = func(*args, **kwargs) File "/home/sasha/nlp_viewer/run.py", line 48, in get_confs builder_cls = nlp.load.import_main_class(module_path, dataset=True) File "/home/sasha/.local/lib/python3.7/site-packages/nlp/load.py", line 57, in import_main_class module = importlib.import_module(module_path) File "/usr/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/sasha/.local/lib/python3.7/site-packages/nlp/datasets/c4/88bb1b1435edad3fb772325710c4a43327cbf4a23b9030094556e6f01e14ec19/c4.py", line 29, in <module> from .c4_utils import ( File "/home/sasha/.local/lib/python3.7/site-packages/nlp/datasets/c4/88bb1b1435edad3fb772325710c4a43327cbf4a23b9030094556e6f01e14ec19/c4_utils.py", line 29, in <module> import langdetect ```

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MDU6SXNzdWU2MzgxMDY3NzQ=

Error in metric.compute: missing `original_instructions` argument

closed

NONE

I'm running into an error using metrics for computation in the latest master as well as version 0.2.1. Here is a minimal example: ```python import nlp rte_metric = nlp.load_metric('glue', name="rte") rte_metric.compute( [0, 0, 1, 1], [0, 1, 0, 1], ) ``` ``` 181 # Read the predictions and references 182 reader = ArrowReader(path=self.data_dir, info=None) --> 183 self.data = reader.read_files(node_files) 184 185 # Release all of our locks TypeError: read_files() missing 1 required positional argument: 'original_instructions' ``` I believe this might have been introduced with cc8d2508b75f7ba0e5438d0686ee02dcec43c7f4, which added the `original_instructions` argument. Elsewhere, an empty-string default is provided--perhaps that could be done here too?

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MDExOlB1bGxSZXF1ZXN0NDMzNzU5NzQ1

add Rotten Tomatoes Movie Review sentences sentiment dataset

closed

CONTRIBUTOR

Sentence-level movie reviews v1.0 from here: http://www.cs.cornell.edu/people/pabo/movie-review-data/

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MDU6SXNzdWU2Mzc0MTU1NDU=

How can I load/find WMT en-romanian?

closed

CONTRIBUTOR

I believe it is from `wmt16` When I run ```python wmt = nlp.load_dataset('wmt16') ``` I get: ```python AssertionError: The dataset wmt16 with config cs-en requires manual data. Please follow the manual download instructions: Some of the wmt configs here, require a manual download. Please look into wmt.py to see the exact path (and file name) that has to be downloaded. . Manual data can be loaded with `nlp.load(wmt16, data_dir='<path/to/manual/data>') ``` There is no wmt.py,as the error message suggests, and wmt16.py doesn't have manual download instructions. Any idea how to do this? Thanks in advance!

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MDExOlB1bGxSZXF1ZXN0NDMzMTk1NDgw

Add sort, shuffle, test_train_split and select methods

closed

MEMBER

Add a bunch of methods to reorder/split/select rows in a dataset: - `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...) - `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type) - `dataset.shuffle(seed)`: shuffle a dataset rows - `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits) All these methods are **not** in-place which means they return new ``Dataset``. This is the default behavior in the library. Fix #147 #166 #259

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MDExOlB1bGxSZXF1ZXN0NDMzMTgxNDMz

Add pyarrow warning colab

closed

MEMBER

When a user installs `nlp` on google colab, then google colab doesn't update pyarrow, and the runtime needs to be restarted to use the updated version of pyarrow. This is an issue because `nlp` requires the updated version to work correctly. In this PR I added en error that is shown to the user in google colab if the user tries to `import nlp` without having restarted the runtime. The error tells the user to restart the runtime.

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MDExOlB1bGxSZXF1ZXN0NDMzMTU0ODQ4

Fix small issues creating dataset

closed

MEMBER

Fix many small issues mentioned in #249: - don't force to install apache beam for commands - fix None cache dir when using `dl_manager.download_custom` - added new extras in `setup.py` named `dev` that contains tests and quality dependencies - mock dataset sizes when running tests with dummy data - add a note about the naming convention of datasets (camel case - snake case) in CONTRIBUTING.md This should help users create their datasets. Next step is the `add_dataset.md` docs :)

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MDU6SXNzdWU2MzcwMjgwMTU=

[Feature request] Support for external modality for language datasets

closed

CONTRIBUTOR

# Background In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https://arxiv.org/abs/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https://github.com/huggingface/nlp/pull/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. # Language + Vision ## Use case Typically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https://cs.stanford.edu/people/dorarad/gqa/download.html#seconddown) dataset. Currently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https://arxiv.org/abs/1611.08481), [Shekhar et.al, 2019](https://www.aclweb.org/anthology/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https://arxiv.org/abs/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https://arxiv.org/abs/1908.03557), is to use FastRCNN features. For all these types of features, people use one of the following formats: 1. [HD5F](https://pypi.org/project/h5py/) 2. [NumPy](https://numpy.org/doc/stable/reference/generated/numpy.savez.html) 3. [LMDB](https://lmdb.readthedocs.io/en/release/) ## Implementation considerations I was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following: 1. Download dataset 2. Download images associated with the dataset 3. Write a script that generates the visual features for every image and store them in a specific file 4. Create a DataLoader that maps the visual features to the corresponding language example In my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https://github.com/pytorch/pytorch/issues/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it. For ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. Looking forward to hearing your thoughts about it!

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MDExOlB1bGxSZXF1ZXN0NDMyODI3Mzcz

Add new dataset ANLI Round 1

closed

CONTRIBUTOR

Adding new dataset [ANLI](https://github.com/facebookresearch/anli/). I'm not familiar with how to add new dataset. Let me know if there is any issue. I only include round 1 data here. There will be round 2, round 3 and more in the future with potentially different format. I think it will be better to separate them.

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MDU6SXNzdWU2MzYzNzIzODA=

Downloading dataset error with pyarrow.lib.RecordBatch

closed

NONE

I am trying to download `sentiment140` and I have the following error ``` /usr/local/lib/python3.6/dist-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) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 /usr/local/lib/python3.6/dist-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) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 472 try: 473 # Prepare split will record examples associated to the split --> 474 self._prepare_split(split_generator, **prepare_split_kwargs) 475 except OSError: 476 raise OSError("Cannot find data file. " + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or "")) /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _prepare_split(self, split_generator) 652 for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False): 653 example = self.info.features.encode_example(record) --> 654 writer.write(example) 655 num_examples, num_bytes = writer.finalize() 656 /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write(self, example, writer_batch_size) 143 self._build_writer(pa_table=pa.Table.from_pydict(example)) 144 if writer_batch_size is not None and len(self.current_rows) >= writer_batch_size: --> 145 self.write_on_file() 146 147 def write_batch( /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_on_file(self) 127 else: 128 # All good --> 129 self._write_array_on_file(pa_array) 130 self.current_rows = [] 131 /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in _write_array_on_file(self, pa_array) 96 def _write_array_on_file(self, pa_array): 97 """Write a PyArrow Array""" ---> 98 pa_batch = pa.RecordBatch.from_struct_array(pa_array) 99 self._num_bytes += pa_array.nbytes 100 self.pa_writer.write_batch(pa_batch) AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array' ``` I installed the last version and ran the following command: ```python import nlp sentiment140 = nlp.load_dataset('sentiment140', cache_dir='/content') ```

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MDExOlB1bGxSZXF1ZXN0NDMyNDY3NDM5

Consistency fixes

closed

MEMBER

A few bugs I've found while hacking

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MDU6SXNzdWU2MzYyMzk1Mjk=

documentation missing how to split a dataset

closed

NONE

I am trying to understand how to split a dataset ( as arrow_dataset). I know I can do something like this to access a split which is already in the original dataset : `ds_test = nlp.load_dataset('imdb, split='test') ` But how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)? I guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: > See the [guide on splits](https://github.com/huggingface/nlp/tree/master/docs/splits.md) for more information. But the guide seems to be missing. To clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https://www.tensorflow.org/datasets/splits). But to come back to the example above: I cannot simply split the testset doing this: `ds_test = nlp.load_dataset('imdb, split='test'[:5000]) ` `ds_val = nlp.load_dataset('imdb, split='test'[5000:])` because the imdb test data is sorted by class (probably not a good idea anyway)

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MDU6SXNzdWU2MzU4NTk1MjU=

Why is dataset after tokenization far more larger than the orginal one ?

closed

CONTRIBUTOR

I tokenize wiki dataset by `map` and cache the results. ``` def tokenize_tfm(example): example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text'])) return example wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train'] wiki.map(tokenize_tfm, cache_file_name=cache_dir/"wikipedia/20200501.en/1.0.0/tokenized_wiki.arrow") ``` and when I see their size ``` ls -l --block-size=M 17460M wikipedia-train.arrow 47511M tokenized_wiki.arrow ``` The tokenized one is over 2x size of original one. Is there something I did wrong ?

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MDU6SXNzdWU2MzU2MjA5Nzk=

Tokenizer pickling issue fix not landed in `nlp` yet?

closed

NONE

Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function: ``` dataset = nlp.load_dataset('cos_e') tokenizer = GPT2TokenizerFast.from_pretrained('gpt2', cache_dir=cache_dir) for split in dataset.keys(): dataset[split].map(lambda x: some_function(x, tokenizer)) ``` ``` 06/09/2020 10:09:19 - INFO - nlp.builder - Constructing Dataset for split train[:10], from /home/sarahw/.cache/huggingface/datasets/cos_e/default/0.0.1 Traceback (most recent call last): File "generation/input_to_label_and_rationale.py", line 390, in <module> main() File "generation/input_to_label_and_rationale.py", line 263, in main dataset[split] = dataset[split].map(lambda x: input_to_explanation_plus_label(x, tokenizer, max_length, datasource=data_args.task_name, wt5=(model_class=='t5'), expl_only=model_args.rationale_only), batched=False) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/arrow_dataset.py", line 522, in map cache_file_name = self._get_cache_file_path(function, cache_kwargs) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/arrow_dataset.py", line 381, in _get_cache_file_path function_bytes = dumps(function) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/utils/py_utils.py", line 257, in dumps dump(obj, file) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/utils/py_utils.py", line 250, in dump Pickler(file).dump(obj) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 445, in dump StockPickler.dump(self, obj) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 485, in dump self.save(obj) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save f(self, obj) # Call unbound method with explicit self File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 1410, in save_function pickler.save_reduce(_create_function, (obj.__code__, File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 690, in save_reduce save(args) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save f(self, obj) # Call unbound method with explicit self File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 899, in save_tuple save(element) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save f(self, obj) # Call unbound method with explicit self File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 899, in save_tuple save(element) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save f(self, obj) # Call unbound method with explicit self File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 1147, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 690, in save_reduce save(args) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save f(self, obj) # Call unbound method with explicit self File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 884, in save_tuple save(element) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 601, in save self.save_reduce(obj=obj, *rv) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 715, in save_reduce save(state) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save f(self, obj) # Call unbound method with explicit self File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 912, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 969, in save_dict self._batch_setitems(obj.items()) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 995, in _batch_setitems save(v) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 601, in save self.save_reduce(obj=obj, *rv) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 715, in save_reduce save(state) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save f(self, obj) # Call unbound method with explicit self File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 912, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 969, in save_dict self._batch_setitems(obj.items()) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 995, in _batch_setitems save(v) File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 576, in save rv = reduce(self.proto) TypeError: cannot pickle 'Tokenizer' object ``` Fix seems to be in the tokenizers [`0.8.0.dev1 pre-release`](https://github.com/huggingface/tokenizers/issues/87), which I can't install with any package managers.

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MDU6SXNzdWU2MzU1OTYyOTU=

[Feature request] Add a feature to dataset

closed

NONE

Is there a straightforward way to add a field to the arrow_dataset, prior to performing map?

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MDExOlB1bGxSZXF1ZXN0NDMxNjg3MDM0

Add dataset/piaf

closed

CONTRIBUTOR

Small SQuAD-like French QA dataset [PIAF](https://www.aclweb.org/anthology/2020.lrec-1.673.pdf)

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MDU6SXNzdWU2MzUwNTc1Njg=

[Feature request] Be able to remove a specific sample of the dataset

closed

NONE

As mentioned in #117, it's currently not possible to remove a sample of the dataset. But it is a important use case : After applying some preprocessing, some samples might be empty for example. We should be able to remove these samples from the dataset, or at least mark them as `removed` so when iterating the dataset, we don't iterate these samples. I think it should be a feature. What do you think ? --- Any work-around in the meantime ?

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MDExOlB1bGxSZXF1ZXN0NDMxMjgwOTYz

add flue dataset

closed

CONTRIBUTOR

This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.

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MDU6SXNzdWU2MzQ1NjMyMzk=

NonMatchingSplitsSizesError error when reading the IMDB dataset

closed

NONE

Hi! I am trying to load the `imdb` dataset with this line: `dataset = nlp.load_dataset('imdb', data_dir='/A/PATH', cache_dir='/A/PATH')` but I am getting the following error: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/load.py", line 517, in load_dataset save_infos=save_infos, File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 363, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 421, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 70, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=5929447, num_examples=4537, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}] ``` Am I overlooking something? Thanks!

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https://github.com/huggingface/datasets/pull/251

MDExOlB1bGxSZXF1ZXN0NDMxMDgwMDkw

Better access to all dataset information

closed

MEMBER

Moves all the dataset info down one level from `dataset.info.XXX` to `dataset.XXX` This way it's easier to access `dataset.feature['label']` for instance Also, add the original split instructions used to create the dataset in `dataset.split` Ex: ``` from nlp import load_dataset stsb = load_dataset('glue', name='stsb', split='train') stsb.split >>> NamedSplit('train') ```

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https://github.com/huggingface/datasets/pull/250

MDExOlB1bGxSZXF1ZXN0NDMwOTcyMzg4

Remove checksum download in c4

closed

MEMBER

There was a line from the original tfds script that was still there and causing issues when loading the c4 script. This one should fix #233 and allow anyone to load the c4 script to generate the dataset

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MDU6SXNzdWU2MzMzOTM0NDM=

[Dataset created] some critical small issues when I was creating a dataset

closed

CONTRIBUTOR

Hi, I successfully created a dataset and has made a pr #248. But I have encountered several problems when I was creating it, and those should be easy to fix. 1. Not found dataset_info.json should be fixed by #241 , eager to wait it be merged. 2. Forced to install `apach_beam` If we should install it, then it might be better to include it in the pakcage dependency or specified in `CONTRIBUTING.md` ``` Traceback (most recent call last): File "nlp-cli", line 10, in <module> from nlp.commands.run_beam import RunBeamCommand File "/home/yisiang/nlp/src/nlp/commands/run_beam.py", line 6, in <module> import apache_beam as beam ModuleNotFoundError: No module named 'apache_beam' ``` 3. `cached_dir` is `None` ``` File "/home/yisiang/nlp/src/nlp/datasets/bookscorpus/aea0bd5142d26df645a8fce23d6110bb95ecb81772bb2a1f29012e329191962c/bookscorpus.py", line 88, in _split_generators downloaded_path_or_paths = dl_manager.download_custom(_GDRIVE_FILE_ID, download_file_from_google_drive) File "/home/yisiang/nlp/src/nlp/utils/download_manager.py", line 128, in download_custom downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls) File "/home/yisiang/nlp/src/nlp/utils/py_utils.py", line 172, in map_nested return function(data_struct) File "/home/yisiang/nlp/src/nlp/utils/download_manager.py", line 126, in url_to_downloaded_path return os.path.join(self._download_config.cache_dir, hash_url_to_filename(url)) File "/home/yisiang/miniconda3/envs/nlppr/lib/python3.7/posixpath.py", line 80, in join a = os.fspath(a) ``` This is because this line https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/src/nlp/commands/test.py#L30-L32 And I add `--cache_dir="...."` to `python nlp-cli test datasets/<your-dataset-folder> --save_infos --all_configs` in the doc, finally I could pass this error. But it seems to ignore my arg and use `/home/yisiang/.cache/huggingface/datasets/bookscorpus/plain_text/1.0.0` as cahe_dir 4. There is no `pytest` So maybe in the doc we should specify a step to install pytest 5. Not enough capacity in my `/tmp` When run test for dummy data, I don't know why it ask me for 5.6g to download something, ``` def download_and_prepare ... if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root): raise IOError( "Not enough disk space. Needed: {} (download: {}, generated: {})".format( utils.size_str(self.info.size_in_bytes or 0), utils.size_str(self.info.download_size or 0), > utils.size_str(self.info.dataset_size or 0), ) ) E OSError: Not enough disk space. Needed: 5.62 GiB (download: 1.10 GiB, generated: 4.52 GiB) ``` I add a `processed_temp_dir="some/dir"; raw_temp_dir="another/dir"` to 71, and the test passed https://github.com/huggingface/nlp/blob/a67a6c422dece904b65d18af65f0e024e839dbe8/tests/test_dataset_common.py#L70-L72 I suggest we can create tmp dir under the `/home/user/tmp` but not `/tmp`, because take our lab server for example, everyone use `/tmp` thus it has not much capacity. Or at least we can improve error message, so the user know is what directory has no space and how many has it lefted. Or we could do both. 6. name of datasets I was surprised by the dataset name `books_corpus`, and didn't know it is from `class BooksCorpus(nlp.GeneratorBasedBuilder)` . I change it to `Bookscorpus` afterwards. I think this point shold be also on the doc. 7. More thorough doc to how to create `dataset.py` I believe there will be. **Feel free to close this issue** if you think these are solved.

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