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https://github.com/huggingface/datasets/pull/271

MDExOlB1bGxSZXF1ZXN0NDMzOTg3NDkw

Fix allociné dataset configuration

closed

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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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https://github.com/huggingface/datasets/issues/270

MDU6SXNzdWU2MzgxMjE2MTc=

c4 dataset is not viewable in nlpviewer demo

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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

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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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https://github.com/huggingface/datasets/pull/268

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?

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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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https://github.com/huggingface/datasets/pull/266

MDExOlB1bGxSZXF1ZXN0NDMzMTk1NDgw

Add sort, shuffle, test_train_split and select methods

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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

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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

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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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https://github.com/huggingface/datasets/pull/262

MDExOlB1bGxSZXF1ZXN0NDMyODI3Mzcz

Add new dataset ANLI Round 1

closed

CONTRIBUTOR

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

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MDU6SXNzdWU2MzYzNzIzODA=

Downloading dataset error with pyarrow.lib.RecordBatch

closed

NONE

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

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MDExOlB1bGxSZXF1ZXN0NDMyNDY3NDM5

Consistency fixes

closed

MEMBER

A few bugs I've found while hacking

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MDU6SXNzdWU2MzYyMzk1Mjk=

documentation missing how to split a dataset

closed

NONE

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

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MDU6SXNzdWU2MzU4NTk1MjU=

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

closed

CONTRIBUTOR

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

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MDU6SXNzdWU2MzU2MjA5Nzk=

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

closed

NONE

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

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MDU6SXNzdWU2MzU1OTYyOTU=

[Feature request] Add a feature to dataset

closed

NONE

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

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MDExOlB1bGxSZXF1ZXN0NDMxNjg3MDM0

Add dataset/piaf

closed

CONTRIBUTOR

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

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MDU6SXNzdWU2MzUwNTc1Njg=

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

closed

NONE

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

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MDExOlB1bGxSZXF1ZXN0NDMxMjgwOTYz

add flue dataset

closed

CONTRIBUTOR

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

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MDU6SXNzdWU2MzQ1NjMyMzk=

NonMatchingSplitsSizesError error when reading the IMDB dataset

closed

NONE

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

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

MDExOlB1bGxSZXF1ZXN0NDMxMDgwMDkw

Better access to all dataset information

closed

MEMBER

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

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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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https://github.com/huggingface/datasets/pull/235

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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MDU6SXNzdWU2Mjc3OTE2OTM=

[Feature Request/Help] BLEURT model -> PyTorch

closed

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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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

closed

MEMBER

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

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https://github.com/huggingface/datasets/pull/218

MDExOlB1bGxSZXF1ZXN0NDI1MDI2NzEz

Add Natual Questions and C4 scripts

closed

MEMBER

Scripts are ready ! However they are not processed nor directly available from gcp yet.

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MDU6SXNzdWU2MjcxMjg0MDM=

Multi-task dataset mixing

open

CONTRIBUTOR

It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks). The [T5 paper](https://arxiv.org/pdf/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning: - **Examples-proportional mixing** - sample from tasks proportionally to their dataset size - **Equal mixing** - sample uniformly from each task - **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T. Following this discussion https://github.com/huggingface/transformers/issues/4340 in [transformers](https://github.com/huggingface/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality. Some method for combining datasets could be implemented ,e.g. ``` dataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...) ``` We would need a few additions: - Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '? - Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score. It would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. I'm willing to write bits/most of this I just need some guidance on the interface and other library details so I can integrate it properly.

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https://github.com/huggingface/datasets/issues/216

MDU6SXNzdWU2MjY4OTY4OTA=

❓ How to get ROUGE-2 with the ROUGE metric ?

closed

NONE

I'm trying to use ROUGE metric, but I don't know how to get the ROUGE-2 metric. --- I compute scores with : ```python import nlp rouge = nlp.load_metric('rouge') with open("pred.txt") as p, open("ref.txt") as g: for lp, lg in zip(p, g): rouge.add([lp], [lg]) score = rouge.compute() ``` then : _(print only the F-score for readability)_ ```python for k, s in score.items(): print(k, s.mid.fmeasure) ``` It gives : >rouge1 0.7915168355671788 rougeL 0.7915168355671788 --- **How can I get the ROUGE-2 score ?** Also, it's seems weird that ROUGE-1 and ROUGE-L scores are the same. Did I made a mistake ? @lhoestq

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MDU6SXNzdWU2MjY4Njc4Nzk=

NonMatchingSplitsSizesError when loading blog_authorship_corpus

closed

NONE

Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. ``` raise NonMatchingSplitsSizesError(str(bad_splits)) nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, dataset_name='blog_authorship_corpus')}] ``` Upon checking it seems like there is a disparity between the information in `datasets/blog_authorship_corpus/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.

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MDExOlB1bGxSZXF1ZXN0NDI0NTk1NjIx

[arrow_dataset.py] add new filter function

closed

MEMBER

The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples. I think, filtering out examples is also a very common operation people would like to perform on datasets. This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function. Here is a sample code you can play around with: ```python ds = nlp.load_dataset("squad", split="validation[:10%]") def remove_under_idx_5(example, idx): return idx < 5 def only_keep_examples_with_is_in_context(example): return "is" in example["context"] result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False) result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False) print("Original number of examples: {}".format(len(ds))) print("First five examples number of examples: {}".format(len(result_keep_only_first_5))) print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context))) ```

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https://github.com/huggingface/datasets/pull/213

MDExOlB1bGxSZXF1ZXN0NDI0NTUxODE3

better message if missing beam options

closed

MEMBER

WDYT @yjernite ? For example: ```python dataset = nlp.load_dataset('wikipedia', '20200501.aa') ``` Raises: ``` 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.aa', beam_runner='DirectRunner')` ```

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https://github.com/huggingface/datasets/pull/212

MDExOlB1bGxSZXF1ZXN0NDI0NTQ1NjAy

have 'add' and 'add_batch' for metrics

closed

MEMBER

This should fix #116 Previously the `.add` method of metrics expected a batch of examples. Now `.add` expects one prediction/reference and `.add_batch` expects a batch. I think it is more coherent with the way the ArrowWriter works.

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MDU6SXNzdWU2MjY1NjU5OTQ=

[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type

closed

MEMBER

Running the following code ``` import nlp ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards... ds.map(lambda x: x, load_from_cache_file=False) ``` triggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error. On the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works: ``` import nlp ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards... ds.map(lambda x: x, remove_columns=["entity_pages"], load_from_cache_file=False) ``` . Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be? **Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly.

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https://github.com/huggingface/datasets/pull/210

MDExOlB1bGxSZXF1ZXN0NDI0NDgyNDgz

fix xnli metric kwargs description

closed

MEMBER

The text was wrong as noticed in #202

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MDExOlB1bGxSZXF1ZXN0NDI0NDAwOTc4

Add a Google Drive exception for small files

closed

CONTRIBUTOR

I tried to use the ``nlp`` library to load personnal datasets. I mainly copy-paste the code for ``multi-news`` dataset because my files are stored on Google Drive. One of my dataset is small (< 25Mo) so it can be verified by Drive without asking the authorization to the user. This makes the download starts directly. Currently the ``nlp`` raises a error: ``ConnectionError: Couldn't reach https://drive.google.com/uc?export=download&id=1DGnbUY9zwiThTdgUvVTSAvSVHoloCgun`` while the url is working. So I just add a new exception as you have already done for ``firebasestorage.googleapis.com`` : ``` elif (response.status_code == 400 and "firebasestorage.googleapis.com" in url) or (response.status_code == 405 and "drive.google.com" in url) ``` I make an example of the error that you can run on [](https://colab.research.google.com/drive/1ae_JJ9uvUt-9GBh0uGZhjbF5aXkl-BPv?usp=sharing) I avoid the error by adding an exception but there is maybe a proper way to do it. Many thanks :hugs: Best,

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https://github.com/huggingface/datasets/pull/208

MDExOlB1bGxSZXF1ZXN0NDI0Mzk0ODIx

[Dummy data] insert config name instead of config

closed

MEMBER

Thanks @yjernite for letting me know. in the dummy data command the config name shuold be passed to the dataset builder and not the config itself. Also, @lhoestq fixed small import bug introduced by beam command I think.

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MDU6SXNzdWU2MjU5MzIyMDA=

Remove test set from NLP viewer

closed

NONE

While the new [NLP viewer](https://huggingface.co/nlp/viewer/) is a great tool, I think it would be best to outright remove the option of looking at the test sets. At the very least, a warning should be displayed to users before showing the test set. Newcomers to the field might not be aware of best practices, and small things like this can help increase awareness.

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MDU6SXNzdWU2MjU4NDI5ODk=

[Question] Combine 2 datasets which have the same columns

closed

CONTRIBUTOR

Hi, I am using ``nlp`` to load personal datasets. I created summarization datasets in multi-languages based on wikinews. I have one dataset for english and one for german (french is getting to be ready as well). I want to keep these datasets independent because they need different pre-processing (add different task-specific prefixes for T5 : *summarize:* for english and *zusammenfassen:* for german) My issue is that I want to train T5 on the combined english and german datasets to see if it improves results. So I would like to combine 2 datasets (which have the same columns) to make one and train T5 on it. I was wondering if there is a proper way to do it? I assume that it can be done by combining all examples of each dataset but maybe you have a better solution. Hoping this is clear enough, Thanks a lot 😊 Best

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https://github.com/huggingface/datasets/pull/205

MDExOlB1bGxSZXF1ZXN0NDIzOTY2ODE1

Better arrow dataset iter

closed

MEMBER

I tried to play around with `tf.data.Dataset.from_generator` and I found out that the `__iter__` that we have for `nlp.arrow_dataset.Dataset` ignores the format that has been set (torch or tensorflow). With these changes I should be able to come up with a `tf.data.Dataset` that uses lazy loading, as asked in #193.

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https://github.com/huggingface/datasets/pull/204

MDExOlB1bGxSZXF1ZXN0NDIzODE5MTQw

Add Dataflow support + Wikipedia + Wiki40b

closed

MEMBER

# Add Dataflow support + Wikipedia + Wiki40b ## Support datasets processing with Apache Beam Some datasets are too big to be processed on a single machine, for example: wikipedia, wiki40b, etc. Apache Beam allows to process datasets on many execution engines like Dataflow, Spark, Flink, etc. To process such datasets with Beam, I added a command to run beam pipelines `nlp-cli run_beam path/to/dataset/script`. Then I used it to process the english + french wikipedia, and the english of wiki40b. The processed arrow files are on GCS and are the result of a Dataflow job. I added a markdown documentation file in `docs` that explains how to use it properly. ## Load already processed datasets Now that we have those datasets already processed, I made it possible to load datasets that are already processed. You can do `load_dataset('wikipedia', '20200501.en')` and it will download the processed files from the Hugging Face GCS directly into the user's cache and be ready to use ! The Wikipedia dataset was already asked in #187 and this PR should soon allow to add Natural Questions as asked in #129 ## Other changes in the code To make things work, I had to do a few adjustments: - add a `ship_files_with_pipeline` method to the `DownloadManager`. This is because beam pipelines can be run in the cloud and therefore need to have access to your downloaded data. I used it in the wikipedia script: ```python if not pipeline.is_local(): downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline) ``` - add parquet to arrow conversion. This is because the output of beam pipelines are parquet files so we need to convert them to arrow and have the arrow files on GCS - add a test script with a dummy beam dataset - minor adjustments to allow read/write operations on remote files using `apache_beam.io.filesystems.FileSystems` if we want (it can be connected to gcp, s3, hdfs, etc...)

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https://github.com/huggingface/datasets/pull/203

MDExOlB1bGxSZXF1ZXN0NDIzNzEyMTQ3

Raise an error if no config name for datasets like glue

closed

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Some datasets like glue (see #130) and scientific_papers (see #197) have many configs. For example for glue there are cola, sst2, mrpc etc. Currently if a user does `load_dataset('glue')`, then Cola is loaded by default and it can be confusing. Instead, we should raise an error to let the user know that he has to pick one of the available configs (as proposed in #152). For example for glue, the message looks like: ``` ValueError: Config name is missing. Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax'] Example of usage: `load_dataset('glue', 'cola')` ``` The error is raised if the config name is missing and if there are >=2 possible configs.

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https://github.com/huggingface/datasets/issues/202

MDU6SXNzdWU2MjU0OTM5ODM=

Mistaken `_KWARGS_DESCRIPTION` for XNLI metric

closed

NONE

Hi! The [`_KWARGS_DESCRIPTION`](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/xnli/xnli.py#L45) for the XNLI metric uses `Args` and `Returns` text from [BLEU](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/bleu/bleu.py#L58) metric: ``` _KWARGS_DESCRIPTION = """ Computes XNLI score which is just simple accuracy. Args: predictions: list of translations to score. Each translation should be tokenized into a list of tokens. references: list of lists of references for each translation. Each reference should be tokenized into a list of tokens. max_order: Maximum n-gram order to use when computing BLEU score. smooth: Whether or not to apply Lin et al. 2004 smoothing. Returns: 'bleu': bleu score, 'precisions': geometric mean of n-gram precisions, 'brevity_penalty': brevity penalty, 'length_ratio': ratio of lengths, 'translation_length': translation_length, 'reference_length': reference_length """ ``` But it should be something like: ``` _KWARGS_DESCRIPTION = """ Computes XNLI score which is just simple accuracy. Args: predictions: Predicted labels. references: Ground truth labels. Returns: 'accuracy': accuracy ```

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https://api.github.com/repos/huggingface/datasets/issues/201

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https://github.com/huggingface/datasets/pull/201

MDExOlB1bGxSZXF1ZXN0NDIzNDkzNTMw

Fix typo in README

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https://github.com/huggingface/datasets/pull/200

MDExOlB1bGxSZXF1ZXN0NDIzNDg2NTM0

[ArrowWriter] Set schema at first write example

closed

MEMBER

Right now if the schema was not specified when instantiating `ArrowWriter`, then it could be set with the first `write_table` for example (it calls `self._build_writer()` to do so). I noticed that it was not done if the first example is added via `.write`, so I added it for coherence.

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https://api.github.com/repos/huggingface/datasets/issues/199

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https://github.com/huggingface/datasets/pull/199

MDExOlB1bGxSZXF1ZXN0NDIzNDc4ODIx

Fix GermEval 2014 dataset infos

closed

CONTRIBUTOR

Hi, this PR just removes the `dataset_info.json` file and adds a newly generated `dataset_infos.json` file.

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https://api.github.com/repos/huggingface/datasets/issues/198

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https://github.com/huggingface/datasets/issues/198

MDU6SXNzdWU2MjUyMDA2Mjc=

Index outside of table length

closed

NONE

The offset input box warns of numbers larger than a limit (like 2000) but then the errors start at a smaller value than that limit (like 1955). > ValueError: Index (2000) outside of table length (2000). > 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 116, in <module> > v = d[item][k] > File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__ > output_all_columns=self._output_all_columns, > File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 290, in _getitem > raise ValueError(f"Index ({key}) outside of table length ({self._data.num_rows}).")

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https://github.com/huggingface/datasets/issues/197

MDU6SXNzdWU2MjQ5NjY5MDQ=

Scientific Papers only downloading Pubmed

closed

NONE

Hi! I have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following: ``` dataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.') Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.05k/5.05k [00:00<00:00, 2.66MB/s] Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.90k/4.90k [00:00<00:00, 2.42MB/s] Downloading and preparing dataset scientific_papers/pubmed (download: 4.20 GiB, generated: 2.33 GiB, total: 6.53 GiB) to ./scientific_papers/pubmed/1.1.1... Downloading: 3.62GB [00:40, 90.5MB/s] Downloading: 880MB [00:08, 101MB/s] Dataset scientific_papers downloaded and prepared to ./scientific_papers/pubmed/1.1.1. Subsequent calls will reuse this data. ``` only a pubmed folder is created. There doesn't seem to be something for arxiv. Are these two datasets merged? Or have I misunderstood something? Thanks!

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https://api.github.com/repos/huggingface/datasets/issues/196

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https://github.com/huggingface/datasets/pull/196

MDExOlB1bGxSZXF1ZXN0NDIzMjIwMjIw

Check invalid config name

closed

MEMBER

As said in #194, we should raise an error if the config name has bad characters. Bad characters are those that are not allowed for directory names on windows.

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https://github.com/huggingface/datasets/pull/195

MDExOlB1bGxSZXF1ZXN0NDIzMTg1NTAy

[Dummy data command] add new case to command

closed