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https://api.github.com/repos/huggingface/datasets/issues/171

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https://github.com/huggingface/datasets/pull/171

MDExOlB1bGxSZXF1ZXN0NDIwMjk0ODM0

fix squad metric format

closed

MEMBER

The format of the squad metric was wrong. This should fix #143 I tested with ```python3 predictions = [ {'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'} ] references = [ {'answers': [{'text': 'Denver Broncos'}], 'id': '56be4db0acb8001400a502ec'} ] ```

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https://github.com/huggingface/datasets/pull/170

MDExOlB1bGxSZXF1ZXN0NDIwMjMwMDIx

Rename anli dataset

closed

MEMBER

What we have now as the `anli` dataset is actually the αNLI dataset from the ART challenge dataset. This name is confusing because `anli` is also the name of adversarial NLI (see [https://github.com/facebookresearch/anli](https://github.com/facebookresearch/anli)). I renamed the current `anli` dataset by `art`.

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https://github.com/huggingface/datasets/pull/169

MDExOlB1bGxSZXF1ZXN0NDIwMjE1NDkw

Adding Qanta (Quizbowl) Dataset

closed

CONTRIBUTOR

This PR adds the qanta question answering datasets from [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) and [Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples](https://www.aclweb.org/anthology/Q19-1029/) (adversarial fold) This partially continues a discussion around fixing dummy data from https://github.com/huggingface/nlp/issues/161 I ran the following code to double check that it works and did some sanity checks on the output. The majority of the code itself is from our `allennlp` version of the dataset reader. ```python import nlp # Default is full question data = nlp.load_dataset('./datasets/qanta') # Four configs # Primarily useful for training data = nlp.load_dataset('./datasets/qanta', 'mode=sentences,char_skip=25') # Primarily used in evaluation data = nlp.load_dataset('./datasets/qanta', 'mode=first,char_skip=25') data = nlp.load_dataset('./datasets/qanta', 'mode=full,char_skip=25') # Primarily useful in evaluation and "live" play data = nlp.load_dataset('./datasets/qanta', 'mode=runs,char_skip=25') ```

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https://github.com/huggingface/datasets/issues/168

MDU6SXNzdWU2MjA5NTk4MTk=

Loading 'wikitext' dataset fails

closed

NONE

Loading the 'wikitext' dataset fails with Attribute error: Code to reproduce (From example notebook): import nlp wikitext_dataset = nlp.load_dataset('wikitext') Error: --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-17-d5d9df94b13c> in <module>() 11 12 # Load a dataset and print the first examples in the training set ---> 13 wikitext_dataset = nlp.load_dataset('wikitext') 14 print(wikitext_dataset['train'][0]) 6 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, dl_manager, **download_and_prepare_kwargs) 363 verify_infos = not save_infos and not ignore_verifications 364 self._download_and_prepare( --> 365 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 366 ) 367 # Sync info /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 416 try: 417 # Prepare split will record examples associated to the split --> 418 self._prepare_split(split_generator, **prepare_split_kwargs) 419 except OSError: 420 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) 594 example = self.info.features.encode_example(record) 595 writer.write(example) --> 596 num_examples, num_bytes = writer.finalize() 597 598 assert num_examples == num_examples, f"Expected to write {split_info.num_examples} but wrote {num_examples}" /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in finalize(self, close_stream) 173 def finalize(self, close_stream=True): 174 if self.pa_writer is not None: --> 175 self.write_on_file() 176 self.pa_writer.close() 177 if close_stream: /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_on_file(self) 124 else: 125 # All good --> 126 self._write_array_on_file(pa_array) 127 self.current_rows = [] 128 /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in _write_array_on_file(self, pa_array) 93 def _write_array_on_file(self, pa_array): 94 """Write a PyArrow Array""" ---> 95 pa_batch = pa.RecordBatch.from_struct_array(pa_array) 96 self._num_bytes += pa_array.nbytes 97 self.pa_writer.write_batch(pa_batch) AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'

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https://github.com/huggingface/datasets/pull/167

MDExOlB1bGxSZXF1ZXN0NDIwMDY0NDMw

[Tests] refactor tests

closed

MEMBER

This PR separates AWS and Local tests to remove these ugly statements in the script: ```python if "/" not in dataset_name: logging.info("Skip {} because it is a canonical dataset") return ``` To run a `aws` test, one should now run the following command: ```python pytest -s tests/test_dataset_common.py::AWSDatasetTest::test_builder_class_wmt14 ``` The same `local` test, can be run with: ```python pytest -s tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_wmt14 ```

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MDU6SXNzdWU2MjA4NTAyMTg=

Add a method to shuffle a dataset

closed

MEMBER

Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method. Also, we could maybe have a clear indication of which method modify in-place and which methods return/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think?

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MDU6SXNzdWU2MjA3NTgyMjE=

ANLI

closed

NONE

Can I recommend the following: For ANLI, use https://github.com/facebookresearch/anli. As that paper says, "Our dataset is not to be confused with abductive NLI (Bhagavatula et al., 2019), which calls itself αNLI, or ART.". Indeed, the paper cited under what is currently called anli says in the abstract "We introduce a challenge dataset, ART". The current naming will confuse people :)

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MDU6SXNzdWU2MjA1NDAyNTA=

Add Spanish POR and NER Datasets

closed

NONE

Hi guys, In order to cover multilingual support a little step could be adding standard Datasets used for Spanish NER and POS tasks. I can provide it in raw and preprocessed formats.

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MDU6SXNzdWU2MjA1MzQzMDc=

[Feature request] Add cos-e v1.0

closed

NONE

I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https://www.aclweb.org/anthology/P19-1487/), and v1.11 has noted [annotation](https://github.com/salesforce/cos-e/issues/2) [issues](https://arxiv.org/pdf/2004.14546.pdf).

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https://github.com/huggingface/datasets/pull/162

MDExOlB1bGxSZXF1ZXN0NDE5NzQ4Mzky

fix prev files hash in map

closed

MEMBER

Fix the `.map` issue in #160. This makes sure it takes the previous files when computing the hash.

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MDU6SXNzdWU2MjA0ODc1MzU=

Discussion on version identifier & MockDataLoaderManager for test data

open

CONTRIBUTOR

Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp/utils/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.

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MDU6SXNzdWU2MjA0NDgyMzY=

caching in map causes same result to be returned for train, validation and test

closed

NONE

hello, I am working on a program that uses the `nlp` library with the `SST2` dataset. The rough outline of the program is: ``` import nlp as nlp_datasets ... parser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+') ... dataset = nlp_datasets.load_dataset(*args.dataset) ... # Create feature vocabs vocabs = create_vocabs(dataset.values(), vectorizers) ... # Create a function to vectorize based on vectorizers and vocabs: print('TS', train_set.num_rows) print('VS', valid_set.num_rows) print('ES', test_set.num_rows) # factory method to create a `convert_to_features` function based on vocabs convert_to_features = create_featurizer(vectorizers, vocabs) train_set = train_set.map(convert_to_features, batched=True) train_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths']) train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz) valid_set = valid_set.map(convert_to_features, batched=True) valid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths']) valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz) test_set = test_set.map(convert_to_features, batched=True) test_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths']) test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz) print('TS', train_set.num_rows) print('VS', valid_set.num_rows) print('ES', test_set.num_rows) ``` Im not sure if Im using it incorrectly, but the results are not what I expect. Namely, the `.map()` seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets: ``` TS 67349 VS 872 ES 1821 TS 67349 VS 67349 ES 67349 ``` The behavior changes if I turn off the caching but then the results fail: ``` train_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False) ... valid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False) ... test_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False) ``` Now I get the right set of features back... ``` TS 67349 VS 872 ES 1821 100%|██████████| 68/68 [00:00<00:00, 92.78it/s] 100%|██████████| 1/1 [00:00<00:00, 75.47it/s] 0%| | 0/2 [00:00<?, ?it/s]TS 67349 VS 872 ES 1821 100%|██████████| 2/2 [00:00<00:00, 77.19it/s] ``` but I think its losing track of the original training set: ``` Traceback (most recent call last): File "/home/dpressel/dev/work/baseline/api-examples/layers-classify-hf-datasets.py", line 148, in <module> for x in train_loader: File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 345, in __next__ data = self._next_data() File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 385, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__ output_all_columns=self._output_all_columns, File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 294, in _getitem outputs = self._unnest(self._data.slice(key, 1).to_pydict()) File "pyarrow/table.pxi", line 1211, in pyarrow.lib.Table.slice File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000) Process finished with exit code 1 ``` The full-example program (minus the print stmts) is here: https://github.com/dpressel/mead-baseline/pull/620/files

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MDU6SXNzdWU2MjA0MjA3MDA=

How can we add more datasets to nlp library?

closed

NONE

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https://github.com/huggingface/datasets/pull/158

MDExOlB1bGxSZXF1ZXN0NDE5NjUyNTQy

add Toronto Books Corpus

closed

CONTRIBUTOR

This PR adds the Toronto Books Corpus. . It on consider TMX and plain text files (Moses) defined in the table **Statistics and TMX/Moses Downloads** [here](http://opus.nlpl.eu/Books.php )

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MDU6SXNzdWU2MjAzNTY1NDI=

nlp.load_dataset() gives "TypeError: list_() takes exactly one argument (2 given)"

closed

NONE

I'm trying to load datasets from nlp but there seems to have error saying "TypeError: list_() takes exactly one argument (2 given)" gist can be found here https://gist.github.com/saahiluppal/c4b878f330b10b9ab9762bc0776c0a6a

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MDU6SXNzdWU2MjAyNjM2ODc=

SyntaxError with WMT datasets

closed

NONE

The following snippet produces a syntax error: ``` import nlp dataset = nlp.load_dataset('wmt14') print(dataset['train'][0]) ``` ``` Traceback (most recent call last): File "/home/tom/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-8-3206959998b9>", line 3, in <module> dataset = nlp.load_dataset('wmt14') File "/home/tom/.local/lib/python3.6/site-packages/nlp/load.py", line 505, in load_dataset builder_cls = import_main_class(module_path, dataset=True) File "/home/tom/.local/lib/python3.6/site-packages/nlp/load.py", line 56, in import_main_class module = importlib.import_module(module_path) File "/usr/lib/python3.6/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 994, in _gcd_import File "<frozen importlib._bootstrap>", line 971, in _find_and_load File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 665, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 678, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/tom/.local/lib/python3.6/site-packages/nlp/datasets/wmt14/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2/wmt14.py", line 21, in <module> from .wmt_utils import Wmt, WmtConfig File "/home/tom/.local/lib/python3.6/site-packages/nlp/datasets/wmt14/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2/wmt_utils.py", line 659 <<<<<<< HEAD ^ SyntaxError: invalid syntax ``` Python version: `3.6.9 (default, Apr 18 2020, 01:56:04) [GCC 8.4.0]` Running on Ubuntu 18.04, via a Jupyter notebook

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MDExOlB1bGxSZXF1ZXN0NDE5Mzg1ODM0

Include more links in README, fix typos

closed

CONTRIBUTOR

Include more links and fix typos in README

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MDExOlB1bGxSZXF1ZXN0NDE5Mzc4Mzgw

add Ubuntu Dialogs Corpus datasets

closed

CONTRIBUTOR

This PR adds the Ubuntu Dialog Corpus datasets version 2.0.

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MDU6SXNzdWU2MTk5NzIyNDY=

Meta-datasets (GLUE/XTREME/...) – Special care to attributions and citations

open

MEMBER

Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation/specific homepage/etc are very visible and accessible and not only the generic citation of the meta-dataset itself. Let's take GLUE as an example: The configuration has the citation for each dataset included (e.g. [here](https://github.com/huggingface/nlp/blob/master/datasets/glue/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded.

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https://github.com/huggingface/datasets/pull/152

MDExOlB1bGxSZXF1ZXN0NDE5MzA4OTE2

Add GLUE config name check

closed

CONTRIBUTOR

Fixes #130 by adding a name check to the Glue class

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MDExOlB1bGxSZXF1ZXN0NDE5MzA2MTYz

Fix JSON tests.

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NDE5MTgyODU4

Add WNUT 17 NER dataset

closed

CONTRIBUTOR

Hi, this PR adds the WNUT 17 dataset to `nlp`. > Emerging and Rare entity recognition > This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text. > > The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. More information about the dataset can be found on the [shared task page](https://noisy-text.github.io/2017/emerging-rare-entities.html). Dataset is taken is taken from their [GitHub repository](https://github.com/leondz/emerging_entities_17), because the data provided in this repository contains minor fixes in the dataset format. ## Usage Then the WNUT 17 dataset can be used in `nlp` like this: ```python import nlp wnut_17 = nlp.load_dataset("./datasets/wnut_17/wnut_17.py") print(wnut_17) ``` This outputs: ```txt 'train': Dataset(schema: {'id': 'string', 'tokens': 'list<item: string>', 'labels': 'list<item: string>'}, num_rows: 3394) 'validation': Dataset(schema: {'id': 'string', 'tokens': 'list<item: string>', 'labels': 'list<item: string>'}, num_rows: 1009) 'test': Dataset(schema: {'id': 'string', 'tokens': 'list<item: string>', 'labels': 'list<item: string>'}, num_rows: 1287) ``` Number are identical with the ones in [this paper](https://www.ijcai.org/Proceedings/2019/0702.pdf) and are the same as using the `dataset` reader in Flair. ## Features The following feature format is used to represent a sentence in the WNUT 17 dataset: | Feature | Example | Description | ---- | ---- | ----------------- | `id` | `0` | Number (id) of current sentence | `tokens` | `["AHFA", "extends", "deadline"]` | List of tokens (strings) for a sentence | `labels` | `["B-group", "O", "O"]` | List of labels (outer span) The following labels are used in WNUT 17: ```txt O B-corporation I-corporation B-location I-location B-product I-product B-person I-person B-group I-group B-creative-work I-creative-work ```

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MDU6SXNzdWU2MTk3MzU3Mzk=

[Feature request] Add Ubuntu Dialogue Corpus dataset

closed

NONE

https://github.com/rkadlec/ubuntu-ranking-dataset-creator or http://dataset.cs.mcgill.ca/ubuntu-corpus-1.0/

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MDU6SXNzdWU2MTk1OTA1NTU=

_download_and_prepare() got an unexpected keyword argument 'verify_infos'

closed

CONTRIBUTOR

# Reproduce In Colab, ``` %pip install -q nlp %pip install -q apache_beam mwparserfromhell dataset = nlp.load_dataset('wikipedia') ``` get ``` Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wikipedia/20200501.aa/1.0.0... --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-6-52471d2a0088> in <module>() ----> 1 dataset = nlp.load_dataset('wikipedia') 1 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) 515 download_mode=download_mode, 516 ignore_verifications=ignore_verifications, --> 517 save_infos=save_infos, 518 ) 519 /usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs) 361 verify_infos = not save_infos and not ignore_verifications 362 self._download_and_prepare( --> 363 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 364 ) 365 # Sync info TypeError: _download_and_prepare() got an unexpected keyword argument 'verify_infos' ```

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MDU6SXNzdWU2MTk1ODE5MDc=

Error with sklearn train_test_split

closed

NONE

It would be nice if we could use sklearn `train_test_split` to quickly generate subsets from the dataset objects returned by `nlp.load_dataset`. At the moment the code: ```python data = nlp.load_dataset('imdb', cache_dir=data_cache) f_half, s_half = train_test_split(data['train'], test_size=0.5, random_state=seed) ``` throws: ``` ValueError: Can only get row(s) (int or slice) or columns (string). ``` It's not a big deal, since there are other ways to split the data, but it would be a cool thing to have.

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MDExOlB1bGxSZXF1ZXN0NDE5MDI5MjUx

Add BERTScore to metrics

closed

CONTRIBUTOR

This PR adds [BERTScore](https://arxiv.org/abs/1904.09675) to metrics. Here is an example of how to use it. ```sh import nlp bertscore = nlp.load_metric('metrics/bertscore') # or simply nlp.load_metric('bertscore') after this is added to huggingface's s3 bucket predictions = ['example', 'fruit'] references = [['this is an example.', 'this is one example.'], ['apple']] results = bertscore.compute(predictions, references, lang='en') print(results) ```

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MDExOlB1bGxSZXF1ZXN0NDE4OTcxMjg0

[AWS Tests] Follow-up PR from #144

closed

MEMBER

I forgot to add this line in PR #145 .

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MDExOlB1bGxSZXF1ZXN0NDE4OTY5NjA1

[AWS tests] AWS test should not run for canonical datasets

closed

MEMBER

AWS tests should in general not run for canonical datasets. Only local tests will run in this case. This way a PR is able to pass when adding a new dataset. This PR changes to logic to the following: 1) All datasets that are present in `nlp/datasets` are tested only locally. This way when one adds a canonical dataset, the PR includes his dataset in the tests. 2) All datasets that are only present on AWS, such as `webis/tl_dr` atm are tested only on AWS. I think the testing structure might need a bigger refactoring and better documentation very soon. Merging for now to unblock new PRs @thomwolf @mariamabarham .

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MDU6SXNzdWU2MTk0NTc2NDE=

ArrowTypeError in squad metrics

closed

MEMBER

`squad_metric.compute` is giving following error ``` ArrowTypeError: Could not convert [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}] with type list: was not a dict, tuple, or recognized null value for conversion to struct type ``` This is how my predictions and references look like ``` predictions[0] # {'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'} ``` ``` references[0] # {'answers': [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}], 'id': '56be4db0acb8001400a502ec'} ``` These are structured as per the `squad_metric.compute` help string.

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MDExOlB1bGxSZXF1ZXN0NDE4OTU0OTc1

[WMT] Add all wmt

closed

MEMBER

This PR adds all wmt datasets scripts. At the moment the script is **not** functional for the language pairs "cs-en", "ru-en", "hi-en" because apparently it takes up to a week to get the manual data for these datasets: see http://ufal.mff.cuni.cz/czeng. The datasets are fully functional though for the "big" language pairs "de-en" and "fr-en". Overall I think the scripts are very messy and might need a big refactoring at some point. For now I think there are good to merge (most dataset configs can be used). I will add "cs", "ru" and "hi" when the manual data is available.

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MDExOlB1bGxSZXF1ZXN0NDE4OTUzMzQw

[Clean up] remove bogus folder

closed

MEMBER

@mariamabarham - I think you accidentally placed it there.

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MDExOlB1bGxSZXF1ZXN0NDE4OTUxMzg4

[Tests] run local tests as default

closed

MEMBER

This PR also enables local tests by default I think it's safer for now to enable both local and aws tests for every commit. The problem currently is that when we do a PR to add a dataset, the dataset is not yet on AWS on therefore not tested on the PR itself. Thus the PR will always be green even if the datasets are not correct. This PR aims at fixing this. ## Suggestion on how to commit to the repo from now on: Now since the repo is "online", I think we should adopt a couple of best practices: 1) - No direct committing to the repo anymore. Every change should be opened in a PR and be well documented so that we can find it later 2) - Every PR has to be reviewed by at least x people (I guess @thomwolf you should decide here) because we now have to be much more careful when doing changes to the API for backward compatibility, etc...

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MDExOlB1bGxSZXF1ZXN0NDE4ODc4NzMy

Add GermEval 2014 NER dataset

closed

CONTRIBUTOR

Hi, this PR adds the GermEval 2014 NER dataset 😃 > The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation [1] with the following properties: > - The data was sampled from German Wikipedia and News Corpora as a collection of citations. > - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. > - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]]. Dataset will be downloaded from the [official GermEval 2014 website](https://sites.google.com/site/germeval2014ner/data). ## Dataset format Here's an example of the dataset format from the original dataset: ```tsv # http://de.wikipedia.org/wiki/Manfred_Korfmann [2009-10-17] 1 Aufgrund O O 2 seiner O O 3 Initiative O O 4 fand O O 5 2001/2002 O O 6 in O O 7 Stuttgart B-LOC O 8 , O O 9 Braunschweig B-LOC O 10 und O O 11 Bonn B-LOC O 12 eine O O 13 große O O 14 und O O 15 publizistisch O O 16 vielbeachtete O O 17 Troia-Ausstellung B-LOCpart O 18 statt O O 19 , O O 20 „ O O 21 Troia B-OTH B-LOC 22 - I-OTH O 23 Traum I-OTH O 24 und I-OTH O 25 Wirklichkeit I-OTH O 26 “ O O 27 . O O ``` The sentence is encoded as one token per line (tab separated columns. The first column contains either a `#`, which signals the source the sentence is cited from and the date it was retrieved, or the token number within the sentence. The second column contains the token. Column three and four contain the named entity (in IOB2 scheme). Outer spans are encoded in the third column, embedded/nested spans in the fourth column. ## Features I decided to keep most information from the dataset. That means the so called "source" information (where the sentences come from + date information) is also returned for each sentence in the feature vector. For each sentence in the dataset, one feature vector (`nlp.Features` definition) will be returned: | Feature | Example | Description | ---- | ---- | ----------------- | `id` | `0` | Number (id) of current sentence | `source` | `http://de.wikipedia.org/wiki/Manfred_Korfmann [2009-10-17]` | URL and retrieval date as string | `tokens` | `["Schwartau", "sagte", ":"]` | List of tokens (strings) for a sentence | `labels` | `["B-PER", "O", "O"]` | List of labels (outer span) | `nested-labels` | `["O", "O", "O"]` | List of labels for nested span ## Example The following command downloads the dataset from the official GermEval 2014 page and pre-processed it: ```bash python nlp-cli test datasets/germeval_14 --all_configs ``` It then outputs the number for training, development and testset. The training set consists of 24,000 sentences, the development set of 2,200 and the test of 5,100 sentences. Now it can be imported and used with `nlp`: ```python import nlp germeval = nlp.load_dataset("./datasets/germeval_14/germeval_14.py") assert len(germeval["train"]) == 24000 # Show first sentence of training set: germeval["train"][0] ```

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MDU6SXNzdWU2MTkyMjUxOTE=

Consider renaming to nld

closed

NONE

Hey :) Just making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing. The issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme. If you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere. If people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order. I don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider. I suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.

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MDU6SXNzdWU2MTkyMTQ2NDU=

Tokenized BLEU considered harmful - Discussion on community-based process

open

NONE

https://github.com/huggingface/nlp/blob/7d1526dfeeb29248d832f1073192dbf03ad642da/metrics/bleu/bleu.py#L76 assumes the inputs are tokenized by the user. This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard. Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options. As @mjpost reported in https://www.aclweb.org/anthology/W18-6319.pdf BLEU configurations can vary by 1.8. Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https://arxiv.org/abs/2004.04902 . There are a few use cases for tokenized BLEU like Thai. For Chinese, people seem to use character BLEU for better or worse. The default easy option should be the one that's correct more often. And that is sacrebleu. Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`. Also, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.

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MDExOlB1bGxSZXF1ZXN0NDE4NzgxNzI4

Update README.md

closed

NONE

small typo

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MDExOlB1bGxSZXF1ZXN0NDE4Nzc4MTMw

Fix print statement in READ.md

closed

CONTRIBUTOR

print statement was throwing generator object instead of printing names of available datasets/metrics

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MDExOlB1bGxSZXF1ZXN0NDE4Njk5OTYz

Update README.md

closed

NONE

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MDU6SXNzdWU2MTkwOTQ5NTQ=

[Question] Using/adding a local dataset

closed

NONE

Users may want to either create/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets. It appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this. A notebook/example script demonstrating this would be very helpful.

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MDU6SXNzdWU2MTkwNzc4NTE=

[Feature Request] Add the OpenWebText dataset

closed

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The OpenWebText dataset is an open clone of OpenAI's WebText dataset. It can be used to train ELECTRA as is specified in the [README](https://www.github.com/google-research/electra). More information and the download link are available [here](https://skylion007.github.io/OpenWebTextCorpus/).

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MDU6SXNzdWU2MTkwNzM3MzE=

[Feature request] Add Toronto BookCorpus dataset

closed

CONTRIBUTOR

I know the copyright/distribution of this one is complex, but it would be great to have! That, combined with the existing `wikitext`, would provide a complete dataset for pretraining models like BERT.

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MDU6SXNzdWU2MTkwMzU0NDA=

Loading GLUE dataset loads CoLA by default

closed

NONE

If I run: ```python dataset = nlp.load_dataset('glue') ``` The resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling: ```python metric = nlp.load_metric("glue") ``` which throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets?

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MDU6SXNzdWU2MTg5OTc3MjU=

[Feature request] Add Google Natural Question dataset

closed

NONE

Would be great to have https://github.com/google-research-datasets/natural-questions as an alternative to SQuAD.

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MDU6SXNzdWU2MTg5NTExMTc=

Some error inside nlp.load_dataset()

closed

NONE

First of all, nice work! I am going through [this overview notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb) In simple step `dataset = nlp.load_dataset('squad', split='validation[:10%]')` I get an error, which is connected with some inner code, I think: ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-8-d848d3a99b8c> in <module>() 1 # Downloading and loading a dataset 2 ----> 3 dataset = nlp.load_dataset('squad', split='validation[:10%]') 8 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) 515 download_mode=download_mode, 516 ignore_verifications=ignore_verifications, --> 517 save_infos=save_infos, 518 ) 519 /usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs) 361 verify_infos = not save_infos and not ignore_verifications 362 self._download_and_prepare( --> 363 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 364 ) 365 # Sync info /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 414 try: 415 # Prepare split will record examples associated to the split --> 416 self._prepare_split(split_generator, **prepare_split_kwargs) 417 except OSError: 418 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) 585 fname = "{}-{}.arrow".format(self.name, split_generator.name) 586 fpath = os.path.join(self._cache_dir, fname) --> 587 examples_type = self.info.features.type 588 writer = ArrowWriter(data_type=examples_type, path=fpath, writer_batch_size=self._writer_batch_size) 589 /usr/local/lib/python3.6/dist-packages/nlp/features.py in type(self) 460 @property 461 def type(self): --> 462 return get_nested_type(self) 463 464 @classmethod /usr/local/lib/python3.6/dist-packages/nlp/features.py in get_nested_type(schema) 370 # Nested structures: we allow dict, list/tuples, sequences 371 if isinstance(schema, dict): --> 372 return pa.struct({key: get_nested_type(value) for key, value in schema.items()}) 373 elif isinstance(schema, (list, tuple)): 374 assert len(schema) == 1, "We defining list feature, you should just provide one example of the inner type" /usr/local/lib/python3.6/dist-packages/nlp/features.py in <dictcomp>(.0) 370 # Nested structures: we allow dict, list/tuples, sequences 371 if isinstance(schema, dict): --> 372 return pa.struct({key: get_nested_type(value) for key, value in schema.items()}) 373 elif isinstance(schema, (list, tuple)): 374 assert len(schema) == 1, "We defining list feature, you should just provide one example of the inner type" /usr/local/lib/python3.6/dist-packages/nlp/features.py in get_nested_type(schema) 379 # We allow to reverse list of dict => dict of list for compatiblity with tfds 380 if isinstance(inner_type, pa.StructType): --> 381 return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type)) 382 return pa.list_(inner_type, schema.length) 383 /usr/local/lib/python3.6/dist-packages/nlp/features.py in <genexpr>(.0) 379 # We allow to reverse list of dict => dict of list for compatiblity with tfds 380 if isinstance(inner_type, pa.StructType): --> 381 return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type)) 382 return pa.list_(inner_type, schema.length) 383 TypeError: list_() takes exactly one argument (2 given) ```

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MDExOlB1bGxSZXF1ZXN0NDE4NTQ1MDcy

Update Overview.ipynb

closed

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update notebook

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MDExOlB1bGxSZXF1ZXN0NDE4NTM1Mzc5

remove webis

closed

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Remove webis from dataset folder. Our first dataset script that only lives on AWS :-) https://s3.console.aws.amazon.com/s3/buckets/datasets.huggingface.co/nlp/datasets/webis/tl_dr/?region=us-east-1 @julien-c @jplu

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MDExOlB1bGxSZXF1ZXN0NDE4NTExNDE0

[Newsroom] add newsroom

closed

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I checked it with the data link of the mail you forwarded @thomwolf => works well!

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MDExOlB1bGxSZXF1ZXN0NDE4NTA3NDUx

Xsum, require manual download of some files

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MDExOlB1bGxSZXF1ZXN0NDE4NDcxODU5

[Tests] Local => aws

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## Change default Test from local => aws As a default we set` aws=True`, `Local=False`, `slow=False` ### 1. RUN_AWS=1 (default) This runs 4 tests per dataset script. a) Does the dataset script have a valid etag / Can it be reached on AWS? b) Can we load its `builder_class`? c) Can we load **all** dataset configs? d) _Most importantly_: Can we load the dataset? Important - we currently only test the first config of each dataset to reduce test time. Total test time is around 1min20s. ### 2. RUN_LOCAL=1 RUN_AWS=0 ***This should be done when debugging dataset scripts of the ./datasets folder*** This only runs 1 test per dataset test, which is equivalent to aws d) - Can we load the dataset from the local `datasets` directory? ### 3. RUN_SLOW=1 We should set up to run these tests maybe 1 time per week ? @thomwolf The `slow` tests include two more important tests. e) Can we load the dataset with all possible configs? This test will probably fail at the moment because a lot of dummy data is missing. We should add the dummy data step by step to be sure that all configs work. f) Test that the actual dataset can be loaded. This will take quite some time to run, but is important to make sure that the "real" data can be loaded. It will also test whether the dataset script has the correct checksums file which is currently not tested with `aws=True`. @lhoestq - is there an easy way to check cheaply whether the `dataset_info.json` is correct for each dataset script?

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MDExOlB1bGxSZXF1ZXN0NDE4NDY2Mzc3

Final cleanup of readme and metrics

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MDExOlB1bGxSZXF1ZXN0NDE4NDQ4MTkx

make style

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MDU6SXNzdWU2MTg3Mzc3ODM=

🐛 `map` not working

closed

NONE

I'm trying to run a basic example (mapping function to add a prefix). [Here is the colab notebook I'm using.](https://colab.research.google.com/drive/1YH4JCAy0R1MMSc-k_Vlik_s1LEzP_t1h?usp=sharing) ```python import nlp dataset = nlp.load_dataset('squad', split='validation[:10%]') def test(sample): sample['title'] = "test prefix @@@ " + sample["title"] return sample print(dataset[0]['title']) dataset.map(test) print(dataset[0]['title']) ``` Output : > Super_Bowl_50 Super_Bowl_50 Expected output : > Super_Bowl_50 test prefix @@@ Super_Bowl_50

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MDU6SXNzdWU2MTg2NTIxNDU=

🐛 Colab : type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'

closed

NONE

I'm trying to load CNN/DM dataset on Colab. [Colab notebook](https://colab.research.google.com/drive/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing) But I meet this error : > AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'

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MDU6SXNzdWU2MTg2NDMwODg=

❓ How to apply a map to all subsets ?

closed

NONE

I'm working with CNN/DM dataset, where I have 3 subsets : `train`, `test`, `validation`. Should I apply my map function on the subsets one by one ? ```python import nlp cnn_dm = nlp.load_dataset('cnn_dailymail') for corpus in ['train', 'test', 'validation']: cnn_dm[corpus] = cnn_dm[corpus].map(my_func) ``` Or is there a better way to do this ?

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MDU6SXNzdWU2MTg2MzI1NzM=

❓ How to remove specific rows of a dataset ?

closed

NONE

I saw on the [example notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb#scrollTo=efFhDWhlvSVC) how to remove a specific column : ```python dataset.drop('id') ``` But I didn't find how to remove a specific row. **For example, how can I remove all sample with `id` < 10 ?**

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MDU6SXNzdWU2MTg2MjgyNjQ=

🐛 Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323

closed

NONE

I'm trying to use rouge metric. I have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence. I tried : ```python import nlp rouge = nlp.load_metric('rouge') with open("test.pred.tokenized") as p, open("test.gold.tokenized") as g: for lp, lg in zip(p, g): rouge.add(lp, lg) ``` But I meet following error : > pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323 --- Full stack-trace : ``` Traceback (most recent call last): File "<stdin>", line 3, in <module> File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/metric.py", line 224, in add self.writer.write_batch(batch) File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/arrow_writer.py", line 148, in write_batch pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema) File "pyarrow/table.pxi", line 1550, in pyarrow.lib.Table.from_pydict File "pyarrow/table.pxi", line 1503, in pyarrow.lib.Table.from_arrays File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323 ``` (`nlp` installed from source)

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MDU6SXNzdWU2MTg2MTU4NTU=

AttributeError: 'dict' object has no attribute 'info'

closed

NONE

I'm trying to access the information of CNN/DM dataset : ```python cnn_dm = nlp.load_dataset('cnn_dailymail') print(cnn_dm.info) ``` returns : > AttributeError: 'dict' object has no attribute 'info'

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MDU6SXNzdWU2MTg2MTEzMTA=

Couldn't reach CNN/DM dataset

closed

NONE

I can't get CNN / DailyMail dataset. ```python import nlp assert "cnn_dailymail" in [dataset.id for dataset in nlp.list_datasets()] cnn_dm = nlp.load_dataset('cnn_dailymail') ``` [Colab notebook](https://colab.research.google.com/drive/1zQ3bYAVzm1h0mw0yWPqKAg_4EUlSx5Ex?usp=sharing) gives following error : ``` ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/cnn_dailymail/cnn_dailymail.py ```

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MDExOlB1bGxSZXF1ZXN0NDE4MjkxNjIx

Adding docstrings and some doc

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Some doc

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MDExOlB1bGxSZXF1ZXN0NDE4Mjc0MTU4

Qa4mre - add dataset

closed

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Added dummy data test only for the first config. Will do the rest later. I had to do add some minor hacks to an important function to make it work. There might be a cleaner way to handle it - can you take a look @thomwolf ?

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MDExOlB1bGxSZXF1ZXN0NDE4MjQwMjMy

[Clean-up] remove under construction datastes

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MDExOlB1bGxSZXF1ZXN0NDE4MjMzODIy

fix reddit tifu dummy data

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MDExOlB1bGxSZXF1ZXN0NDE4MjI0MDYw

[Reclor] fix reclor

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- That's probably one me. Could have made the manual data test more flexible. @mariamabarham

https://api.github.com/repos/huggingface/datasets/issues/109/timeline

https://api.github.com/repos/huggingface/datasets/issues/108

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https://github.com/huggingface/datasets/pull/108

MDExOlB1bGxSZXF1ZXN0NDE4MTIzMzc3

convert can use manual dir as second argument

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@mariamabarham

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https://api.github.com/repos/huggingface/datasets/issues/107

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https://github.com/huggingface/datasets/pull/107

MDExOlB1bGxSZXF1ZXN0NDE4MTEyNzcx

add writer_batch_size to GeneratorBasedBuilder

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You can now specify `writer_batch_size` in the builder arguments or directly in `load_dataset`

https://api.github.com/repos/huggingface/datasets/issues/107/timeline

https://api.github.com/repos/huggingface/datasets/issues/106

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https://github.com/huggingface/datasets/pull/106

MDExOlB1bGxSZXF1ZXN0NDE4MTAzMjM3

Add data dir test command

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https://api.github.com/repos/huggingface/datasets/issues/105

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MDExOlB1bGxSZXF1ZXN0NDE4MDg5Njgz

[New structure on AWS] Adapt paths

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Some small changes so that we have the correct paths. @julien-c

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https://api.github.com/repos/huggingface/datasets/issues/104

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MDExOlB1bGxSZXF1ZXN0NDE4MDMzOTY0

Add trivia_q

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Currently tested only for one config to pass tests. Needs to add more dummy data later.

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https://api.github.com/repos/huggingface/datasets/issues/103

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https://github.com/huggingface/datasets/pull/103

MDExOlB1bGxSZXF1ZXN0NDE3OTk5MDIy

[Manual downloads] add logic proposal for manual downloads and add wikihow

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Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset. The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`. The dataset can then be loaded via: ```python import nlp nlp.load_dataset("wikihow", data_dir="~/wikihow/manual_dir") ``` I added/changed so that there are explicit error messages when using manually downloaded files.

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https://github.com/huggingface/datasets/pull/102

MDExOlB1bGxSZXF1ZXN0NDE3OTk3MDQz

Run save infos

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I replaced the old checksum file with the new `dataset_infos.json` by running the script on almost all the datasets we have. The only one that is still running on my side is the cornell dialog

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https://github.com/huggingface/datasets/pull/101

MDExOlB1bGxSZXF1ZXN0NDE3ODk5OTQ2

[Reddit] add reddit

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- Everything worked fine @mariamabarham. Made my computer nearly crash, but all seems to be working :-)

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https://github.com/huggingface/datasets/pull/100

MDExOlB1bGxSZXF1ZXN0NDE3ODc1MjE2

Add per type scores in seqeval metric

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CONTRIBUTOR

This PR add a bit more detail in the seqeval metric. Now the usage and output are: ```python import nlp met = nlp.load_metric('metrics/seqeval') references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] met.compute(predictions, references) #Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.8} ``` It is also possible to compute scores for non IOB notations, POS tagging for example hasn't this kind of notation. Add `suffix` parameter: ```python import nlp met = nlp.load_metric('metrics/seqeval') references = [['O', 'O', 'O', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']] predictions = [['O', 'O', 'MISC', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']] met.compute(predictions, references, metrics_kwargs={"suffix": True}) #Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.9} ```

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MDExOlB1bGxSZXF1ZXN0NDE3ODMxNjky

[Cmrc 2018] fix cmrc2018

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https://github.com/huggingface/datasets/pull/98

MDExOlB1bGxSZXF1ZXN0NDE3Nzc3NDcy

Webis tl-dr

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CONTRIBUTOR

Add the Webid TL:DR dataset.

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https://api.github.com/repos/huggingface/datasets/issues/97

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MDExOlB1bGxSZXF1ZXN0NDE3NjU4MDcy

[Csv] add tests for csv dataset script

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Adds dummy data tests for csv.

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https://api.github.com/repos/huggingface/datasets/issues/96

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https://github.com/huggingface/datasets/pull/96

MDExOlB1bGxSZXF1ZXN0NDE3NjAwMjY4

lm1b

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Add lm1b dataset.

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https://api.github.com/repos/huggingface/datasets/issues/95

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https://github.com/huggingface/datasets/pull/95

MDExOlB1bGxSZXF1ZXN0NDE3NTY5NzA4

Replace checksums files by Dataset infos json

closed