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https://github.com/huggingface/datasets/pull/422

MDExOlB1bGxSZXF1ZXN0NDU0NTE3MDU2

- Corrected encoding for IMDB.

closed

CONTRIBUTOR

The preparation phase (after the download phase) crashed on windows because of charmap encoding not being able to decode certain characters. This change suggested in Issue #347 fixes it for the IMDB dataset.

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https://github.com/huggingface/datasets/pull/421

MDExOlB1bGxSZXF1ZXN0NDUzNzkzMzQ1

Style change

closed

CONTRIBUTOR

make quality and make style ran on scripts

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https://github.com/huggingface/datasets/pull/420

MDExOlB1bGxSZXF1ZXN0NDUzNjI5OTk2

Better handle nested features

closed

MEMBER

Changes: - added arrow schema to features conversion (it's going to be useful to fix #342 ) - make flatten handle deep features (useful for tfrecords conversion in #339 ) - add tests for flatten and features conversions - the reader now returns the kwargs to instantiate a Dataset (fix circular dependencies)

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https://github.com/huggingface/datasets/pull/419

MDExOlB1bGxSZXF1ZXN0NDUzNTgxNzQz

EmoContext dataset add

closed

CONTRIBUTOR

EmoContext Dataset add Signed-off-by: lordtt13 <[email protected]>

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https://github.com/huggingface/datasets/issues/418

MDU6SXNzdWU2NjE5MTQ4NzM=

Addition of google drive links to dl_manager

closed

CONTRIBUTOR

Hello there, I followed the template to create a download script of my own, which works fine for me, although I had to shun the dl_manager because it was downloading nothing from the drive links and instead use gdown. This is the script for me: ```python class EmoConfig(nlp.BuilderConfig): """BuilderConfig for SQUAD.""" def __init__(self, **kwargs): """BuilderConfig for EmoContext. Args: **kwargs: keyword arguments forwarded to super. """ super(EmoConfig, self).__init__(**kwargs) _TEST_URL = "https://drive.google.com/file/d/1Hn5ytHSSoGOC4sjm3wYy0Dh0oY_oXBbb/view?usp=sharing" _TRAIN_URL = "https://drive.google.com/file/d/12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X/view?usp=sharing" class EmoDataset(nlp.GeneratorBasedBuilder): """ SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Version 1.0.0 """ VERSION = nlp.Version("1.0.0") force = False def _info(self): return nlp.DatasetInfo( description=_DESCRIPTION, features=nlp.Features( { "text": nlp.Value("string"), "label": nlp.features.ClassLabel(names=["others", "happy", "sad", "angry"]), } ), supervised_keys=None, homepage="https://www.aclweb.org/anthology/S19-2005/", citation=_CITATION, ) def _get_drive_url(self, url): base_url = 'https://drive.google.com/uc?id=' split_url = url.split('/') return base_url + split_url[5] def _split_generators(self, dl_manager): """Returns SplitGenerators.""" if(not os.path.exists("emo-train.json") or self.force): gdown.download(self._get_drive_url(_TRAIN_URL), "emo-train.json", quiet = True) if(not os.path.exists("emo-test.json") or self.force): gdown.download(self._get_drive_url(_TEST_URL), "emo-test.json", quiet = True) return [ nlp.SplitGenerator( name=nlp.Split.TRAIN, gen_kwargs={ "filepath": "emo-train.json", "split": "train", }, ), nlp.SplitGenerator( name=nlp.Split.TEST, gen_kwargs={"filepath": "emo-test.json", "split": "test"}, ), ] def _generate_examples(self, filepath, split): """ Yields examples. """ with open(filepath, 'rb') as f: data = json.load(f) for id_, text, label in zip(data["text"].keys(), data["text"].values(), data["Label"].values()): yield id_, { "text": text, "label": label, } ``` Can someone help me in adding gdrive links to be used with default dl_manager or adding gdown as another dl_manager, because I'd like to add this dataset to nlp's official database.

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https://github.com/huggingface/datasets/pull/417

MDExOlB1bGxSZXF1ZXN0NDUzNDMyODE5

Fix docstrins multiple metrics instances

closed

MEMBER

We change the docstrings of `nlp.Metric.compute`, `nlp.Metric.add` and `nlp.Metric.add_batch` depending on which metric is instantiated. However we had issues when instantiating multiple metrics (docstrings were duplicated). This should fix #304

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https://github.com/huggingface/datasets/pull/416

MDExOlB1bGxSZXF1ZXN0NDUzMjg1NTM4

Fix xtreme panx directory

closed

MEMBER

Fix #412

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https://github.com/huggingface/datasets/issues/415

MDU6SXNzdWU2NjA2ODcwNzY=

Something is wrong with WMT 19 kk-en dataset

open

NONE

The translation in the `train` set does not look right: ``` >>>import nlp >>>from nlp import load_dataset >>>dataset = load_dataset('wmt19', 'kk-en') >>>dataset["train"]["translation"][0] {'kk': 'Trumpian Uncertainty', 'en': 'Трамптық белгісіздік'} >>>dataset["validation"]["translation"][0] {'kk': 'Ақша-несие саясатының сценарийін қайта жазсақ', 'en': 'Rewriting the Monetary-Policy Script'} ```

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https://github.com/huggingface/datasets/issues/414

MDU6SXNzdWU2NjA2NTQwMTM=

from_dict delete?

closed

NONE

AttributeError: type object 'Dataset' has no attribute 'from_dict'

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MDU6SXNzdWU2NjAwNjM2NTU=

Is there a way to download only NQ dev?

closed

NONE

Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)? As we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data. I tried ``` dataset = nlp.load_dataset('natural_questions', split="validation", beam_runner="DirectRunner") ``` But this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits / slicing options only available after downloading? Thanks!

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MDU6SXNzdWU2NjAwNDcxMzk=

Unable to load XTREME dataset from disk

closed

MEMBER

Hi 🤗 team! ## Description of the problem Following the [docs](https://huggingface.co/nlp/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https://github.com/google-research/xtreme) benchmark. I have manually downloaded the `AmazonPhotos.zip` file from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset. As far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path: ``` # path where load_dataset is looking for fr.tar.gz /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/ # path where it actually exists /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/panx_dataset/ ``` ## Steps to reproduce the problem 1. Manually download the XTREME benchmark from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) 2. Run the following code snippet ```python from nlp import load_dataset # AmazonPhotos.zip is in the root of the folder dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./') ``` 3. Here is the stack trace ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-4-26786bb5fa93> in <module> ----> 1 dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./') /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) 522 download_mode=download_mode, 523 ignore_verifications=ignore_verifications, --> 524 save_infos=save_infos, 525 ) 526 /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) 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 /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 464 split_dict = SplitDict(dataset_name=self.name) 465 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 466 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 467 # Checksums verification 468 if verify_infos: /usr/local/lib/python3.6/dist-packages/nlp/datasets/xtreme/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746/xtreme.py in _split_generators(self, dl_manager) 725 panx_dl_dir = dl_manager.extract(panx_path) 726 lang = self.config.name.split(".")[1] --> 727 lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + ".tar.gz")) 728 return [ 729 nlp.SplitGenerator( /usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in extract(self, path_or_paths) 196 """ 197 return map_nested( --> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths, 199 ) 200 /usr/local/lib/python3.6/dist-packages/nlp/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple) 170 return tuple(mapped) 171 # Singleton --> 172 return function(data_struct) 173 174 /usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in <lambda>(path) 196 """ 197 return map_nested( --> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths, 199 ) 200 /usr/local/lib/python3.6/dist-packages/nlp/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 203 elif urlparse(url_or_filename).scheme == "": 204 # File, but it doesn't exist. --> 205 raise FileNotFoundError("Local file {} doesn't exist".format(url_or_filename)) 206 else: 207 # Something unknown FileNotFoundError: Local file /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/fr.tar.gz doesn't exist ``` ## OS and hardware ``` - `nlp` version: 0.3.0 - Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyTorch version (GPU?): 1.4.0 (True) - Tensorflow version (GPU?): 2.1.0 (True) - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ```

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MDExOlB1bGxSZXF1ZXN0NDUxMjQxOTQy

Sbf

closed

CONTRIBUTOR

This PR adds the Social Bias Frames Dataset (ACL 2020) . dataset homepage: https://homes.cs.washington.edu/~msap/social-bias-frames/

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MDExOlB1bGxSZXF1ZXN0NDUxMTEzMTI3

20newsgroup

closed

CONTRIBUTOR

Add 20Newsgroup dataset. #353

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MDU6SXNzdWU2NTkxMjg2MTE=

train_test_split error: 'dict' object has no attribute 'deepcopy'

closed

NONE

`train_test_split` is giving me an error when I try and call it: `'dict' object has no attribute 'deepcopy'` ## To reproduce ``` dataset = load_dataset('glue', 'mrpc', split='train') dataset = dataset.train_test_split(test_size=0.2) ``` ## Full Stacktrace ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-12-feb740dbec9a> in <module> 1 dataset = load_dataset('glue', 'mrpc', split='train') ----> 2 dataset = dataset.train_test_split(test_size=0.2) ~/anaconda3/envs/fastai2_me/lib/python3.7/site-packages/nlp/arrow_dataset.py in train_test_split(self, test_size, train_size, shuffle, seed, generator, keep_in_memory, load_from_cache_file, train_cache_file_name, test_cache_file_name, writer_batch_size) 1032 "writer_batch_size": writer_batch_size, 1033 } -> 1034 train_kwargs = cache_kwargs.deepcopy() 1035 train_kwargs["split"] = "train" 1036 test_kwargs = cache_kwargs.deepcopy() AttributeError: 'dict' object has no attribute 'deepcopy' ```

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MDExOlB1bGxSZXF1ZXN0NDUwOTU1MTE0

Add tests datasets gcp

closed

MEMBER

Some datasets are available on our google cloud storage in arrow format, so that the users don't need to process the data. These tests make sure that they're always available. It also makes sure that their scripts are in sync between S3 and the repo. This should avoid future issues like #407

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MDU6SXNzdWU2NTg2NzI3MzY=

MissingBeamOptions for Wikipedia 20200501.en

closed

CONTRIBUTOR

There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available): ``` nlp.load_dataset('wikipedia', "20200501.en", split='train') ``` And now, having pulled master, I get: ``` Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to /home/hltcoe/mgordon/.cache/huggingface/datasets/wikipedia/20200501.en/1.0.0/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd... Traceback (most recent call last): File "scripts/download.py", line 11, in <module> fire.Fire(download_pretrain) File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 138, in Fire component_trace = _Fire(component, args, parsed_flag_args, context, name) File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 468, in _Fire target=component.__name__) File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 672, in _CallAndUpdateTrace component = fn(*varargs, **kwargs) File "scripts/download.py", line 6, in download_pretrain nlp.load_dataset('wikipedia', "20200501.en", split='train') File "/exp/mgordon/nlp/src/nlp/load.py", line 534, in load_dataset save_infos=save_infos, File "/exp/mgordon/nlp/src/nlp/builder.py", line 460, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/exp/mgordon/nlp/src/nlp/builder.py", line 870, 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, S park, 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.en', beam_runner='DirectRunner')` ```

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MDU6SXNzdWU2NTg1ODE3NjQ=

Faster Shuffling?

closed

CONTRIBUTOR

Consider shuffling bookcorpus: ``` dataset = nlp.load_dataset('bookcorpus', split='train') dataset.shuffle() ``` According to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`. But I can also just write the lines to a text file: ``` batch_size = 100000 with open('tmp.txt', 'w+') as out_f: for i in tqdm(range(0, len(dataset), batch_size)): batch = dataset[i:i+batch_size]['text'] print("\n".join(batch), file=out_f) ``` Which completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally, ``` dataset = nlp.load_dataset('text', data_files='tmp2.txt') ``` Which completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping. Is shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.)

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MDExOlB1bGxSZXF1ZXN0NDUwNTI1MTc3

Make select() faster by batching reads

closed

CONTRIBUTOR

Here's a benchmark: ``` dataset = nlp.load_dataset('bookcorpus', split='train') start = time.time() dataset.select(np.arange(1000), reader_batch_size=1, load_from_cache_file=False) end = time.time() print(f'{end - start}') start = time.time() dataset.select(np.arange(1000), reader_batch_size=1000, load_from_cache_file=False) end = time.time() print(f'{end - start}') ``` Without batching, select takes around 1.27 seconds. With batching, it takes around 0.01 seconds. The slowness was upsetting me because dataset.shuffle() was supposed to take ~27 hours for bookcorpus. Now with the fix it takes ~2.5 hours (which still is pretty slow, but I'll open a separate issue for that).

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https://github.com/huggingface/datasets/pull/404

MDExOlB1bGxSZXF1ZXN0NDUwMzY4Mjg4

Add seed in metrics

closed

MEMBER

With #361 we noticed that some metrics were not deterministic. In this PR I allow the user to specify numpy's seed when instantiating a metric with `load_metric`. The seed is set only when `compute` is called, and reset afterwards. Moreover when calling `compute` with the same metric instance (i.e. same experiment_id), the metric will always return the same results given the same inputs. This is the case even if the seed is was not specified by the user, as the previous seed is going to be reused. However, instantiating twice a metric (two different experiments) without specifying a seed can create different results.

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MDExOlB1bGxSZXF1ZXN0NDUwMzAzNjI2

return python objects instead of arrays by default

closed

MEMBER

We were using to_pandas() to convert from arrow types, however it returns numpy arrays instead of python lists. I fixed it by using to_pydict/to_pylist instead. Fix #387 It was mentioned in https://github.com/huggingface/transformers/issues/5729

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MDExOlB1bGxSZXF1ZXN0NDUwMDI2NTE0

Search qa

closed

CONTRIBUTOR

add SearchQA dataset #336

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MDExOlB1bGxSZXF1ZXN0NDUwMDIyNTc0

add web_questions

closed

CONTRIBUTOR

add Web Question dataset #336 Maybe @patrickvonplaten you can help with the dummy_data structure? it still broken

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MDExOlB1bGxSZXF1ZXN0NDUwMDA1MDU5

Web questions

closed

CONTRIBUTOR

add the WebQuestion dataset #336

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MDExOlB1bGxSZXF1ZXN0NDQ5ODkxNTEy

Spelling mistake

closed

CONTRIBUTOR

In "Formatting the dataset" part, "The two toehr modifications..." should be "The two other modifications..." ,the word "other" wrong spelled as "toehr".

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MDExOlB1bGxSZXF1ZXN0NDQ5NjE1OTk1

Add inline links

closed

CONTRIBUTOR

Add inline links to `Contributing.md`

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MDExOlB1bGxSZXF1ZXN0NDQ5NjE1MDA4

Add contiguous sharding

closed

CONTRIBUTOR

This makes dset.shard() play nice with nlp.concatenate_datasets(). When I originally wrote the shard() method, I was thinking about a distributed training scenario, but https://github.com/huggingface/nlp/pull/389 also uses it for splitting the dataset for distributed preprocessing. Usage: ``` nlp.concatenate_datasets([dset.shard(n, i, contiguous=True) for i in range(n)]) ```

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MDExOlB1bGxSZXF1ZXN0NDQ5NTg3MDQ4

Fix memory issue when doing select

closed

MEMBER

We were passing the `nlp.Dataset` object to get the hash for the new dataset's file name. Fix #395

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MDU6SXNzdWU2NTc0NTQ5ODM=

Memory issue when doing select

closed

MEMBER

As noticed in #389, the following code loads the entire wikipedia in memory. ```python import nlp w = nlp.load_dataset("wikipedia", "20200501.en", split="train") w.select([0]) ``` This is caused by [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/arrow_dataset.py#L626) for some reason, that tries to serialize the function with all the wikipedia data with it. It's not the case with `.map` or `.filter`. However functions that are based on `.select` like `.shuffle`, `.shard`, `.train_test_split`, `.sort` are affected.

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MDExOlB1bGxSZXF1ZXN0NDQ5NTQzNTE0

Remove remaining nested dict

closed

CONTRIBUTOR

This PR deletes the remaining unnecessary nested dict #378

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MDExOlB1bGxSZXF1ZXN0NDQ5NDY1MTAz

Fix extracted files directory for the DownloadManager

closed

MEMBER

The cache dir was often cluttered by extracted files because of the download manager. For downloaded files, we are using the `downloads` directory to make things easier to navigate, but extracted files were still placed at the root of the cache directory. To fix that I changed the directory for extracted files to cache_dir/downloads/extracted.

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MDExOlB1bGxSZXF1ZXN0NDQ5NDUwOTkx

Style change detection

closed

CONTRIBUTOR

Another [PAN task](https://pan.webis.de/clef20/pan20-web/style-change-detection.html). This time about identifying when the style/author changes in documents. - There's the possibility of adding the [PAN19](https://zenodo.org/record/3577602) and PAN18 style change detection tasks too (these are datasets whose labels are a subset of PAN20's). These would probably make more sense as separate datasets (like wmt is now) - I've converted the integer 0,1 values to a boolean - Using manually downloaded data again. This might be changed at some point following the discussion in https://github.com/huggingface/nlp/pull/349.

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MDExOlB1bGxSZXF1ZXN0NDQ5MTYxMzY3

Concatenate datasets

closed

CONTRIBUTOR

I'm constructing the "WikiBooks" dataset, which is a concatenation of Wikipedia & BookCorpus. So I implemented the `Dataset.from_concat()` method, which concatenates two datasets with the same schema. This would also be useful if someone wants to pretrain on a large generic dataset + their own custom dataset. Not in love with the method name, so would love to hear suggestions. Usage: ```python from nlp import Dataset, load_dataset data1, data2 = {"id": [0, 1, 2]}, {"id": [3, 4, 5]} dset1, dset2 = Dataset.from_dict(data1), Dataset.from_dict(data2) dset_concat = Dataset.from_concat([dset1, dset2]) print(dset_concat) # Dataset(schema: {'id': 'int64'}, num_rows: 6) ```

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MDExOlB1bGxSZXF1ZXN0NDQ5MTMyOTU5

Fix pickling of SplitDict

closed

CONTRIBUTOR

It would be nice to pickle and unpickle Datasets, as done in [this tutorial](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb). Example: ``` wiki = nlp.load_dataset('wikipedia', split='train') def sentencize(examples): ... wiki = wiki.map(sentencize, batched=True) torch.save(wiki, 'sentencized_wiki_dataset.pt') ``` However, upon unpickling the dataset via torch.load(...), this error is raised: ``` ValueError("Cannot add elem. Use .add() instead.") ``` On line [492 of splits.py](https://github.com/huggingface/nlp/blob/master/src/nlp/splits.py#L492). This is because SplitDict subclasses dict, and pickle treats [dicts specially](https://github.com/huggingface/nlp/blob/master/src/nlp/splits.py#L492). Pickle expects access to `dict.__setitem__`, but this is disallowed by the class. The workaround is to provide an explicit interface for pickle to call when pickling and unpickling, thereby avoiding the use of `__setitem__`. Testing: - Manually pickled and unpickled a modified wikipedia dataset. - Ran `make style` I would be happy to run any other tests, but I couldn't find any in the contributing guidelines.

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MDU6SXNzdWU2NTY3MDc0OTc=

🐛 [Dataset] Cannot download wmt14, wmt15 and wmt17

closed

NONE

1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code: ``` nlp.load_dataset('wmt14','de-en') nlp.load_dataset('wmt15','de-en') nlp.load_dataset('wmt17','de-en') nlp.load_dataset('wmt19','de-en') ``` The code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18` 2. When trying to download `wmt17 zh-en`, I got the following error: > ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz

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MDU6SXNzdWU2NTYzNjEzNTc=

Conversion through to_pandas output numpy arrays for lists instead of python objects

closed

MEMBER

In a related question, the conversion through to_pandas output numpy arrays for the lists instead of python objects. Here is an example: ```python >>> dataset._data.slice(key, 1).to_pandas().to_dict("list") {'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [array([ 101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102])], 'token_type_ids': [array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])], 'attention_mask': [array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])]} >>> type(dataset._data.slice(key, 1).to_pandas().to_dict("list")['input_ids'][0]) <class 'numpy.ndarray'> >>> dataset._data.slice(key, 1).to_pydict() {'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [[101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} ```

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MDExOlB1bGxSZXF1ZXN0NDQ4MjQ1NDI4

Update dataset loading and features - Add TREC dataset

closed

MEMBER

This PR: - add a template for a new dataset script - update the caching structure so that the path to the cached data files is also a function of the dataset loading script hash. This way when you update a loading script the data will be automatically updated instead of falling back to the previous version (which is usually a outdated). This makes it in particular easier to iterate when writing a new dataset loading script. - fix a bug in the `ClassLabel` feature and make it more flexible so that its methods `str2int` and `int2str` can also accept list, numpy arrays and PyTorch/TensorFlow tensors. - add the TREC-6 dataset

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MDExOlB1bGxSZXF1ZXN0NDQ4MTAzMjY5

Remove unnecessary nested dict

closed

CONTRIBUTOR

This PR is removing unnecessary nested dictionary used in some datasets. For now the following datasets are updated: - MLQA - RACE Will be adding more if necessary. #378

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MDExOlB1bGxSZXF1ZXN0NDQ3ODI0OTky

Adding the Linguistic Code-switching Evaluation (LinCE) benchmark

closed

CONTRIBUTOR

Hi, First of all, this library is really cool! Thanks for putting all of this together! This PR contains the [Linguistic Code-switching Evaluation (LinCE) benchmark](https://ritual.uh.edu/lince). As described in the official website (FAQ): > 1. Why do we need LinCE? >LinCE brings 10 code-switching datasets together for 4 tasks and 4 language pairs with 5 leaderboards in a single evaluation platform. We examined each dataset and fixed major issues on the partitions (or even define official partitions) with a comprehensive stratification method (see our paper for more details). >Besides, we believe that online benchmarks like LinCE bring steady research progress and allow to compare state-of-the-art models at the pace of the progress in NLP. We expect to benefit greatly the code-switching community with this benchmark. The data comes from social media and here's the summary table of tasks per language pair: | Language Pairs | LID | POS | NER | SA | |----------------------------------------|-----|-----|-----|----| | Spanish-English | ✅ | ✅ | ✅ | ✅ | | Hindi-English | ✅ | ✅ | ✅ | | | Modern Standard Arabic-Egyptian Arabic | ✅ | | ✅ | | | Nepali-English | ✅ | | | | The tasks are as follows: * LID: token-level language identification * POS: part-of-speech tagging * NER: named entity recognition * SA: sentiment analysis With the exception of MSA-EA, the rest of the datasets contain token-level LID labels. ## Usage For Spanish-English LID, we can load the data as follows: ``` import nlp data = nlp.load_dataset('./datasets/lince/lince.py', 'lid_spaeng') for split in data: print(data[split]) ``` Here's the output: ``` Dataset(schema: {'idx': 'int32', 'tokens': 'list<item: string>', 'lid': 'list<item: string>'}, num_rows: 21030) Dataset(schema: {'idx': 'int32', 'tokens': 'list<item: string>', 'lid': 'list<item: string>'}, num_rows: 3332) Dataset(schema: {'idx': 'int32', 'tokens': 'list<item: string>', 'lid': 'list<item: string>'}, num_rows: 8289) ``` Here's the list of shortcut names for every dataset available in LinCE: * `lid_spaeng` * `lid_hineng` * `lid_nepeng` * `lid_msaea` * `pos_spaeng` * `pos_hineng` * `ner_spaeng` * `ner_hineng` * `ner_msaea` * `sa_spaeng` All the numbers match with Table 3 in the LinCE [paper](https://www.aclweb.org/anthology/2020.lrec-1.223.pdf). Also, note that the MSA-EA datasets use the Persian script while the other datasets use the Roman script. ## Features Here is how the features look in the case of language identification (LID) tasks: | LID Feature | Type | Description | |----------------------|---------------|-------------------------------------------| | `idx` | `int` | Dataset index of current sentence | | `tokens` | `list<str>` | List of tokens (string) of a sentence | | `lid` | `list<str>` | List of LID labels (string) of a sentence | For part-of-speech (POS) tagging: | POS Feature | Type | Description | |----------------------|---------------|-------------------------------------------| | `idx` | `int` | Dataset index of current sentence | | `tokens` | `list<str>` | List of tokens (string) of a sentence | | `lid` | `list<str>` | List of LID labels (string) of a sentence | | `pos` | `list<str>` | List of POS tags (string) of a sentence | For named entity recognition (NER): | NER Feature | Type | Description | |----------------------|---------------|-------------------------------------------| | `idx` | `int` | Dataset index of current sentence | | `tokens` | `list<str>` | List of tokens (string) of a sentence | | `lid` | `list<str>` | List of LID labels (string) of a sentence | | `ner` | `list<str>` | List of NER labels (string) of a sentence | **NOTE**: the MSA-EA NER dataset does not contain the `lid` feature. For sentiment analysis (SA): | SA Feature | Type | Description | |---------------------|-------------|-------------------------------------------| | `idx` | `int` | Dataset index of current sentence | | `tokens` | `list<str>` | List of tokens (string) of a sentence | | `lid` | `list<str>` | List of LID labels (string) of a sentence | | `sa` | `str` | Sentiment label (string) of a sentence |

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MDU6SXNzdWU2NTUyOTA0ODI=

1080

closed

NONE

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MDU6SXNzdWU2NTUyNzcxMTk=

NLp

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MDU6SXNzdWU2NTUyMjYzMTY=

[dataset] Structure of MLQA seems unecessary nested

closed

MEMBER

The features of the MLQA dataset comprise several nested dictionaries with a single element inside (for `questions` and `ids`): https://github.com/huggingface/nlp/blob/master/datasets/mlqa/mlqa.py#L90-L97 Should we keep this @mariamabarham @patrickvonplaten? Was this added for compatibility with tfds? ```python features=nlp.Features( { "context": nlp.Value("string"), "questions": nlp.features.Sequence({"question": nlp.Value("string")}), "answers": nlp.features.Sequence( {"text": nlp.Value("string"), "answer_start": nlp.Value("int32"),} ), "ids": nlp.features.Sequence({"idx": nlp.Value("string")}) ```

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MDU6SXNzdWU2NTUyMTU3OTA=

Iyy!!!

closed

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MDU6SXNzdWU2NTUwNDc4MjY=

to_pandas conversion doesn't always work

closed

MEMBER

For some complex nested types, the conversion from Arrow to python dict through pandas doesn't seem to be possible. Here is an example using the official SQUAD v2 JSON file. This example was found while investigating #373. ```python >>> squad = load_dataset('json', data_files={nlp.Split.TRAIN: ["./train-v2.0.json"]}, download_mode=nlp.GenerateMode.FORCE_REDOWNLOAD, version="1.0.0", field='data') >>> squad['train'] Dataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 442) >>> squad['train'][0] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/thomwolf/Documents/GitHub/datasets/src/nlp/arrow_dataset.py", line 589, in __getitem__ format_kwargs=self._format_kwargs, File "/Users/thomwolf/Documents/GitHub/datasets/src/nlp/arrow_dataset.py", line 529, in _getitem outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict("list")) File "pyarrow/array.pxi", line 559, in pyarrow.lib._PandasConvertible.to_pandas File "pyarrow/table.pxi", line 1367, in pyarrow.lib.Table._to_pandas File "/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 766, in table_to_blockmanager blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes) File "/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 1101, in _table_to_blocks list(extension_columns.keys())) File "pyarrow/table.pxi", line 881, in pyarrow.lib.table_to_blocks File "pyarrow/error.pxi", line 105, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string> ``` cc @lhoestq would we have a way to detect this from the schema maybe? Here is the schema for this pretty complex JSON: ```python >>> squad['train'].schema title: string paragraphs: list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>> child 0, item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string> child 0, qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>> child 0, item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>> child 0, question: string child 1, id: string child 2, answers: list<item: struct<text: string, answer_start: int64>> child 0, item: struct<text: string, answer_start: int64> child 0, text: string child 1, answer_start: int64 child 3, is_impossible: bool child 4, plausible_answers: list<item: struct<text: string, answer_start: int64>> child 0, item: struct<text: string, answer_start: int64> child 0, text: string child 1, answer_start: int64 child 1, context: string ```

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MDU6SXNzdWU2NTUwMjMzMDc=

TypeError when computing bertscore

closed

NONE

Hi, I installed nlp 0.3.0 via pip, and my python version is 3.7. When I tried to compute bertscore with the code: ``` import nlp bertscore = nlp.load_metric('bertscore') # load hyps and refs ... print (bertscore.compute(hyps, refs, lang='en')) ``` I got the following error. ``` Traceback (most recent call last): File "bert_score_evaluate.py", line 16, in <module> print (bertscore.compute(hyps, refs, lang='en')) File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metric.py", line 200, in compute output = self._compute(predictions=predictions, references=references, **metrics_kwargs) File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metrics/bertscore/fb176889831bf0ce995ed197edc94b2e9a83f647a869bb8c9477dbb2d04d0f08/bertscore.py", line 105, in _compute hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline) TypeError: get_hash() takes 3 positional arguments but 4 were given ``` It seems like there is something wrong with get_hash() function?

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MDExOlB1bGxSZXF1ZXN0NDQ3NTMxMzUy

Add dataset post processing for faiss indexes

closed

MEMBER

# Post processing of datasets for faiss indexes Now that we can have datasets with embeddings (see `wiki_pr` for example), we can allow users to load the dataset + get the Faiss index that comes with it to do nearest neighbors queries. ## Implementation proposition - Faiss indexes have to be added to the `nlp.Dataset` object, and therefore it's in a different scope that what are doing the `_split_generators` and `_generate_examples` methods of `nlp.DatasetBuilder`. Therefore I added a new method for post processing of the `nlp.Dataset` object called `_post_process` (name could change) - The role of `_post_process` is to apply dataset transforms (filter/map etc.) or indexing functions (add_faiss_index) to modify/enrich the `nlp.Dataset` object. It is not part of the `download_and_prepare` process (that is focused on arrow files creation) so the post processing is run inside the `as_dataset` method. - `_post_process` can generate new files (cached files from dataset transforms or serialized faiss indexes) and their names are defined by `_post_processing_resources` - as we know what are the post processing resources, we can download them automatically from google storage instead of computing them if they're available (as we do for arrow files) I'd happy to discuss these choices ! ## The `wiki_dpr` index It takes 1h20 and ~7GB of memory to compute. The final index is 1.42GB and takes ~1.5GB of memory. This is pretty cool given that a naive flat index would take 170GB of memory to store the 21M vectors of dim 768. I couldn't use directly the Faiss `index_factory` as I needed to set the metric to inner product. ## Example of usage ```python import nlp dset = nlp.load_dataset( "wiki_dpr", "psgs_w100_with_nq_embeddings", split="train", with_index=True ) print(len(dset), dset.list_indexes()) # (21015300, ['embeddings']) ``` (it also works with the dataset configuration without the embeddings because I added the index file in google storage for this one too) ## Demo You can also check a demo on google colab that shows how to use it with the DPRQuestionEncoder from transformers: https://colab.research.google.com/drive/1FakNU8W5EPMcWff7iP1H6REg3XSS0YLp?usp=sharing

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MDU6SXNzdWU2NTQ4NDUxMzM=

Segmentation fault when loading local JSON dataset as of #372

closed

CONTRIBUTOR

The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. ``` dataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: ["./datasets/train-v2.0.json"]}, field='data') ``` causes ``` Using custom data configuration default Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/XXX/.cache/huggingface/datasets/json/default/0.0.0... 0 tables [00:00, ? tables/s]Segmentation fault (core dumped) ``` where `./datasets/train-v2.0.json` is downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/. This is consistent with other SQuAD-formatted JSON files. When attempting to load the dataset again, I get the following: ``` Using custom data configuration default Traceback (most recent call last): File "dataloader.py", line 6, in <module> 'json', data_files={nlp.Split.TRAIN: ["./datasets/train-v2.0.json"]}, field='data') 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 382, in download_and_prepare with incomplete_dir(self._cache_dir) as tmp_data_dir: File "/home/XXX/.conda/envs/torch/lib/python3.7/contextlib.py", line 112, in __enter__ return next(self.gen) File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 368, in incomplete_dir os.makedirs(tmp_dir) File "/home/XXX/.conda/envs/torch/lib/python3.7/os.py", line 223, in makedirs mkdir(name, mode) FileExistsError: [Errno 17] File exists: '/home/XXX/.cache/huggingface/datasets/json/default/0.0.0.incomplete' ``` (Not sure if you wanted this in the previous issue #369 or not as it was closed.)

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MDExOlB1bGxSZXF1ZXN0NDQ3NDMzNTA4

Make the json script more flexible

closed

MEMBER

Fix https://github.com/huggingface/nlp/issues/359 Fix https://github.com/huggingface/nlp/issues/369 JSON script now can accept JSON files containing a single dict with the records as a list in one attribute to the dict (previously it only accepted JSON files containing records as rows of dicts in the file). In this case, you should indicate using `field=XXX` the name of the field in the JSON structure which contains the records you want to load. The records can be a dict of lists or a list of dicts. E.g. to load the SQuAD dataset JSON (without using the `squad` specific dataset loading script), in which the data rows are in the `data` field of the JSON dict, you can do: ```python from nlp import load_dataset dataset = load_dataset('json', data_files='/PATH/TO/JSON', field='data') ```

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MDExOlB1bGxSZXF1ZXN0NDQ3MzQ4NDgw

Fix cached file path for metrics with different config names

closed

MEMBER

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

closed

CONTRIBUTOR

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MDU6SXNzdWU2NTQxODY4OTA=

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

closed

CONTRIBUTOR

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

closed

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

NONE

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

closed

CONTRIBUTOR

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: - [x] TREC-6 dataset (see here for instance: https://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.datasets.html#torchnlp.datasets.trec_dataset) **[done]** - #386 - [x] Yelp-5 - #1315 - [x] Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]** - [x] SST (Stanford Sentiment Treebank) **[include in glue]** - #1934 - [ ] Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]** - [x] 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 - #791 - #1389 - [x] 20 Newsgroups. The 20 Newsgroups dataset **[done]** - #410 - [x] Sogou News dataset **[done]** - #450 - [x] Reuters news. The Reuters-21578 dataset [165] **[done]** - #471 - [x] DBpedia. The DBpedia dataset [170] - #1116 - [ ] Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database - [ ] EUR-Lex. The EUR-Lex dataset - [x] WOS. The Web Of Science (WOS) dataset **[done]** - #424 - [ ] PubMed. PubMed [173] - [x] TREC-QA: TREC-6 + TREC-50 - See above: TREC-6 dataset - [x] Quora. The Quora dataset [180] - #366 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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