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https://github.com/huggingface/datasets/pull/289

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is not required anymore - dataset can be loaded as follow: `nlp.load_dataset('xsum')` **Important** Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json" a more up-to-date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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https://github.com/huggingface/datasets/pull/284

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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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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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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MDExOlB1bGxSZXF1ZXN0NDMwMDQ0MzU0

add Toronto BooksCorpus

closed

CONTRIBUTOR

1. I knew there is a branch `toronto_books_corpus` - After I downloaded it, I found it is all non-english, and only have one row. - It seems that it cites the wrong paper - according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus` 2. It use a text mirror in google drive - `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere. - text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper. - You may want to download it and put it on your gs in case of it disappears someday. 3. Copyright ? The paper has said > **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus. and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more. This should solved #131

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MDExOlB1bGxSZXF1ZXN0NDI5MTMwMzQ2

Make all dataset downloads deterministic by applying `sorted` to glob and os.listdir

closed

MEMBER

This PR makes all datasets loading deterministic by applying `sorted()` to all `glob.glob` and `os.listdir` statements. Are there other "non-deterministic" functions apart from `glob.glob()` and `os.listdir()` that you can think of @thomwolf @lhoestq @mariamabarham @jplu ? **Important** It does break backward compatibility for these datasets because 1. When loading the complete dataset the order in which the examples are saved is different now 2. When loading only part of a split, the examples themselves might be different. @patrickvonplaten - the nlp / longformer notebook has to be updated since the examples might now be different

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MDU6SXNzdWU2MzIzODAwNTQ=

What is the best way to cache a dataset?

closed

NONE

For example if I want to use streamlit with a nlp dataset: ``` @st.cache def load_data(): return nlp.load_dataset('squad') ``` This code raises the error "uncachable object" Right now I just fixed with a constant for my specific case: ``` @st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0}) ``` But I was curious to know what is the best way in general

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MDU6SXNzdWU2MzE5ODUxMDg=

SST-2 test labels are all -1

closed

CONTRIBUTOR

I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1. ``` >>> import nlp >>> glue = nlp.load_dataset('glue', 'sst2') >>> glue {'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)} >>> list(l['label'] for l in glue['test']) [-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, -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, -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, -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, -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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MDExOlB1bGxSZXF1ZXN0NDI4NjgxMTcx

Add Allociné Dataset

closed

CONTRIBUTOR

This is a french binary sentiment classification dataset, which was used to train this model: https://huggingface.co/tblard/tf-allocine. Basically, it's a french "IMDB" dataset, with more reviews. More info on [this repo](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert).

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MDExOlB1bGxSZXF1ZXN0NDI4NTY2MTEy

Specify utf-8 encoding for GLUE

closed

CONTRIBUTOR

#242 This makes the GLUE-MNLI dataset readable on my machine, not sure if it's a Windows-only bug.

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MDU6SXNzdWU2MzE3MzM2ODM=

UnicodeDecodeError when downloading GLUE-MNLI

closed

CONTRIBUTOR

When I run ```python dataset = nlp.load_dataset('glue', 'mnli') ``` I get an encoding error (could it be because I'm using Windows?) : ```python # Lots of error log lines later... ~\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\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\glue.py in _generate_examples(self, data_file, split, mrpc_files) 529 --> 530 for n, row in enumerate(reader): 531 if is_cola_non_test: ~\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 6744: character maps to <undefined> ``` Anyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay.

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MDExOlB1bGxSZXF1ZXN0NDI4NTQwMDM0

Fix empty cache dir

closed

MEMBER

If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful. So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future. This should fix #239

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MDU6SXNzdWU2MzE0MzQ2Nzc=

Deterministic dataset loading

closed

MEMBER

When calling: ```python import nlp dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]") ``` the resulting dataset is not deterministic over different google colabs. After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line: https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/datasets/trivia_qa/trivia_qa.py#L180 which seems to return an ordering of files that depends on the filesystem: https://stackoverflow.com/questions/6773584/how-is-pythons-glob-glob-ordered I think we should go through all the dataset scripts and make sure to have deterministic behavior. A simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name. What do you think @lhoestq?

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MDU6SXNzdWU2MzEzNDA0NDA=

[Creating new dataset] Not found dataset_info.json

closed

CONTRIBUTOR

Hi, I am trying to create Toronto Book Corpus. #131 I ran `~/nlp % python nlp-cli test datasets/bookcorpus --save_infos --all_configs` but this doesn't create `dataset_info.json` and try to use it ``` INFO:nlp.load:Checking datasets/bookcorpus/bookcorpus.py for additional imports. INFO:filelock:Lock 139795325778640 acquired on datasets/bookcorpus/bookcorpus.py.lock INFO:nlp.load:Found main folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus INFO:nlp.load:Found specific version folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9 INFO:nlp.load:Found script file from datasets/bookcorpus/bookcorpus.py to /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.py INFO:nlp.load:Couldn't find dataset infos file at datasets/bookcorpus/dataset_infos.json INFO:nlp.load:Found metadata file for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.json INFO:filelock:Lock 139795325778640 released on datasets/bookcorpus/bookcorpus.py.lock INFO:nlp.builder:Overwrite dataset info from restored data version. INFO:nlp.info:Loading Dataset info from /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0 Traceback (most recent call last): File "nlp-cli", line 37, in <module> service.run() File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/commands/test.py", line 78, in run builders.append(builder_cls(name=config.name, data_dir=self._data_dir)) File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 610, in __init__ super(GeneratorBasedBuilder, self).__init__(*args, **kwargs) File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 152, in __init__ self.info = DatasetInfo.from_directory(self._cache_dir) File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/info.py", line 157, in from_directory with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), "r") as f: FileNotFoundError: [Errno 2] No such file or directory: '/home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/dataset_info.json' ``` btw, `ls /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/` show me nothing is in the directory. I have also pushed the script to my fork [bookcorpus.py](https://github.com/richardyy1188/nlp/blob/bookcorpusdev/datasets/bookcorpus/bookcorpus.py).

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MDU6SXNzdWU2MzEyNjAxNDM=

[Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0.

closed

NONE

When running BERT-Score, I'm meeting this warning : > Warning: Empty candidate sentence; Setting recall to be 0. Code : ``` import nlp metric = nlp.load_metric("bertscore") scores = metric.compute(["swag", "swags"], ["swags", "totally something different"], lang="en", device=0) ``` --- **What am I doing wrong / How can I hide this warning ?**

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MDU6SXNzdWU2MzExOTk5NDA=

Can't download MultiNLI

closed

CONTRIBUTOR

When I try to download MultiNLI with ```python dataset = load_dataset('multi_nli') ``` I get this long error: ```python --------------------------------------------------------------------------- OSError Traceback (most recent call last) <ipython-input-13-3b11f6be4cb9> in <module> 1 # Load a dataset and print the first examples in the training set 2 # nli_dataset = nlp.load_dataset('multi_nli') ----> 3 dataset = load_dataset('multi_nli') 4 # nli_dataset = nlp.load_dataset('multi_nli', split='validation_matched[:10%]') 5 # print(nli_dataset['train'][0]) ~\Miniconda3\envs\nlp\lib\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) 514 515 # Download and prepare data --> 516 builder_instance.download_and_prepare( 517 download_config=download_config, 518 download_mode=download_mode, ~\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) 417 with utils.temporary_assignment(self, "_cache_dir", tmp_data_dir): 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 ) ~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 455 split_dict = SplitDict(dataset_name=self.name) 456 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 457 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 458 # Checksums verification 459 if verify_infos: ~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\multi_nli\60774175381b9f3f1e6ae1028229e3cdb270d50379f45b9f2c01008f50f09e6b\multi_nli.py in _split_generators(self, dl_manager) 99 def _split_generators(self, dl_manager): 100 --> 101 downloaded_dir = dl_manager.download_and_extract( 102 "http://storage.googleapis.com/tfds-data/downloads/multi_nli/multinli_1.0.zip" 103 ) ~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in download_and_extract(self, url_or_urls) 214 extracted_path(s): `str`, extracted paths of given URL(s). 215 """ --> 216 return self.extract(self.download(url_or_urls)) 217 218 def get_recorded_sizes_checksums(self): ~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in extract(self, path_or_paths) 194 path_or_paths. 195 """ --> 196 return map_nested( 197 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths, 198 ) ~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\py_utils.py in map_nested(function, data_struct, dict_only, map_tuple) 168 return tuple(mapped) 169 # Singleton --> 170 return function(data_struct) 171 172 ~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in <lambda>(path) 195 """ 196 return map_nested( --> 197 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths, 198 ) 199 ~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 231 if is_zipfile(output_path): 232 with ZipFile(output_path, "r") as zip_file: --> 233 zip_file.extractall(output_path_extracted) 234 zip_file.close() 235 elif tarfile.is_tarfile(output_path): ~\Miniconda3\envs\nlp\lib\zipfile.py in extractall(self, path, members, pwd) 1644 1645 for zipinfo in members: -> 1646 self._extract_member(zipinfo, path, pwd) 1647 1648 @classmethod ~\Miniconda3\envs\nlp\lib\zipfile.py in _extract_member(self, member, targetpath, pwd) 1698 1699 with self.open(member, pwd=pwd) as source, \ -> 1700 open(targetpath, "wb") as target: 1701 shutil.copyfileobj(source, target) 1702 OSError: [Errno 22] Invalid argument: 'C:\\Users\\Python\\.cache\\huggingface\\datasets\\3e12413b8ec69f22dfcfd54a79d1ba9e7aac2e18e334bbb6b81cca64fd16bffc\\multinli_1.0\\Icon\r' ```

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MDExOlB1bGxSZXF1ZXN0NDI4MDUwNzI4

CompGuessWhat?! dataset

closed

CONTRIBUTOR

Hello, Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)). This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario. In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance: ```python cgw = nlp.load_dataset("compguesswhat") cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True) ``` The other option would be to have a separate dataset class. Any preferences?

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MDExOlB1bGxSZXF1ZXN0NDI3OTM1MjQ0

Add experimental datasets

closed

MEMBER

## Adding an *experimental datasets* folder After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community. My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests. I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets) ### ELI5 #### Dataset description This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers. #### Issues with the current testing 1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded. 2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files. ### Wikipedia Snippets #### Dataset description This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level. #### Issues with the current testing 1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)

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MDU6SXNzdWU2MzA1MzQ0Mjc=

Huggingface NLP, Uploading custom dataset

closed

NONE

Hello, Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp. Thank you!

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MDU6SXNzdWU2MzA0MzIxMzI=

Fail to download c4 english corpus

closed

NONE

i run following code to download c4 English corpus. ``` dataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner' , data_dir='/mypath') ``` and i met failure as follows ``` Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/adam/.cache/huggingface/datasets/c4/en/2.3.0... Traceback (most recent call last): File "download_corpus.py", line 38, in <module> , data_dir='/home/adam/data/corpus/en/c4') File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset save_infos=save_infos, File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 420, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 816, in _download_and_prepare dl_manager, verify_infos=False, pipeline=pipeline, File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 457, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/datasets/c4/f545de9f63300d8d02a6795e2eb34e140c47e62a803f572ac5599e170ee66ecc/c4.py", line 175, in _split_generators dl_manager.download_checksums(_CHECKSUMS_URL) AttributeError: 'DownloadManager' object has no attribute 'download_checksums ``` can i get any advice?

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MDExOlB1bGxSZXF1ZXN0NDI3MjI5NDcy

Nlp cli fix endpoints

closed

MEMBER

With this PR users will be able to upload their own datasets and metrics. As mentioned in #181, I had to use the new endpoints and revert the use of dataclasses (just in case we have changes in the API in the future). We now distinguish commands for datasets and commands for metrics: ```bash nlp-cli upload_dataset <path/to/dataset> nlp-cli upload_metric <path/to/metric> nlp-cli s3_datasets {rm, ls} nlp-cli s3_metrics {rm, ls} ``` Does it sound good to you @julien-c @thomwolf ?

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https://github.com/huggingface/datasets/pull/231

MDExOlB1bGxSZXF1ZXN0NDI3MTk3MTcz

Add .download to MockDownloadManager

closed

MEMBER

One method from the DownloadManager was missing and some users couldn't run the tests because of that. @yjernite

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MDExOlB1bGxSZXF1ZXN0NDI3MTkzMTQ0

Don't force to install apache beam for wikipedia dataset

closed

MEMBER

As pointed out in #227, we shouldn't force users to install apache beam if the processed dataset can be downloaded. I moved the imports of some datasets to avoid this problem

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MDExOlB1bGxSZXF1ZXN0NDI3MTcxMzc5

Rename dataset_infos.json to dataset_info.json

closed

NONE

As the file required for the viewing in the live nlp viewer is named as dataset_info.json

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MDU6SXNzdWU2Mjk5NTI0MDI=

Not able to access the XNLI dataset

closed

NONE

When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error. ``` FileNotFoundError: [Errno 2] No such file or directory: '/home/sasha/.cache/huggingface/datasets/xnli/plain_text/1.0.0/dataset_info.json' 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 86, in <module> dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None) 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 72, in get builder_instance = builder_cls(name=conf) File "/home/sasha/.local/lib/python3.7/site-packages/nlp/builder.py", line 610, in __init__ super(GeneratorBasedBuilder, self).__init__(*args, **kwargs) File "/home/sasha/.local/lib/python3.7/site-packages/nlp/builder.py", line 152, in __init__ self.info = DatasetInfo.from_directory(self._cache_dir) File "/home/sasha/.local/lib/python3.7/site-packages/nlp/info.py", line 157, in from_directory with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), "r") as f: ``` Is it possible to see if the dataset_info.json is correctly placed?

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MDU6SXNzdWU2Mjk4NDU3MDQ=

Should we still have to force to install apache_beam to download wikipedia ?

closed

CONTRIBUTOR

Hi, first thanks to @lhoestq 's revolutionary work, I successfully downloaded processed wikipedia according to the doc. 😍😍😍 But at the first try, it tell me to install `apache_beam` and `mwparserfromhell`, which I thought wouldn't be used according to #204 , it was kind of confusing me at that time. Maybe we should not force users to install these ? Or we just add them to`nlp`'s dependency ?

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MDExOlB1bGxSZXF1ZXN0NDI1OTA0MjEz

add BlendedSkillTalk dataset

closed

CONTRIBUTOR

This PR add the BlendedSkillTalk dataset, which is used to fine tune the blenderbot.

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MDU6SXNzdWU2MjgwODMzNjY=

[ROUGE] Different scores with `files2rouge`

closed

NONE

It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`. Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing --- `nlp` : (Only mid F-scores) >rouge1 0.33508031962733364 rouge2 0.14574333776191592 rougeL 0.2321187823256159 `files2rouge` : >Running ROUGE... =========================== 1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339) 1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445) 1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871) =========================== 1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244) 1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752) 1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240) =========================== 1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676) 1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136) 1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663) --- When using longer predictions/gold, the difference is bigger. **How can I reproduce same score as `files2rouge` ?** @lhoestq

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https://github.com/huggingface/datasets/issues/224

MDU6SXNzdWU2Mjc3OTE2OTM=

[Feature Request/Help] BLEURT model -> PyTorch

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NONE

Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter). I had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried https://colab.research.google.com/drive/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated! Thank you muchly!

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https://github.com/huggingface/datasets/issues/223

MDU6SXNzdWU2Mjc2ODMzODY=

[Feature request] Add FLUE dataset

closed

NONE

Hi, I think it would be interesting to add the FLUE dataset for francophones or anyone wishing to work on French. In other requests, I read that you are already working on some datasets, and I was wondering if FLUE was planned. If it is not the case, I can provide each of the cleaned FLUE datasets (in the form of a directly exploitable dataset rather than in the original xml formats which require additional processing, with the French part for cases where the dataset is based on a multilingual dataframe, etc.).

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https://github.com/huggingface/datasets/issues/222

MDU6SXNzdWU2Mjc1ODY2OTA=

Colab Notebook breaks when downloading the squad dataset

closed

NONE

When I run the notebook in Colab https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb breaks when running this cell: 

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https://github.com/huggingface/datasets/pull/221

MDExOlB1bGxSZXF1ZXN0NDI1MTI5OTc0

Fix tests/test_dataset_common.py

closed

CONTRIBUTOR

When I run the command `RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_arcd` while working on #220. I get the error ` unexpected keyword argument "'download_and_prepare_kwargs'"` at the level of `load_dataset`. Indeed, this [function](https://github.com/huggingface/nlp/blob/master/src/nlp/load.py#L441) no longer has the argument `download_and_prepare_kwargs` but rather `download_config`. So here I change the tests accordingly.

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https://github.com/huggingface/datasets/pull/220

MDExOlB1bGxSZXF1ZXN0NDI1MTEzMzEy

dataset_arcd

closed

CONTRIBUTOR

Added Arabic Reading Comprehension Dataset (ARCD): https://arxiv.org/abs/1906.05394

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https://github.com/huggingface/datasets/pull/219

MDExOlB1bGxSZXF1ZXN0NDI1MDc2NjQx

force mwparserfromhell as third party

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MEMBER

This should fix your env because you had `mwparserfromhell ` as a first party for `isort` @patrickvonplaten

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