Datasets:

selfishark

/

hf-issues-dataset-with-comments

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MDU6SXNzdWU2NzE2MjI0NDE=

UnicodeDecodeError while loading PAN-X task of XTREME dataset

closed

MEMBER

Hi 🤗 team! ## Description of the problem I'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset: ``` --------------------------------------------------------------------------- UnicodeDecodeError Traceback (most recent call last) <ipython-input-5-1d61f439b843> in <module> ----> 1 dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data') /usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 528 ignore_verifications = ignore_verifications or save_infos 529 # Download/copy dataset processing script --> 530 module_path, hash = prepare_module(path, download_config=download_config, dataset=True) 531 532 # Get dataset builder class from the processing script /usr/local/lib/python3.6/dist-packages/nlp/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs) 265 266 # Download external imports if needed --> 267 imports = get_imports(local_path) 268 local_imports = [] 269 library_imports = [] /usr/local/lib/python3.6/dist-packages/nlp/load.py in get_imports(file_path) 156 lines = [] 157 with open(file_path, mode="r") as f: --> 158 lines.extend(f.readlines()) 159 160 logger.info("Checking %s for additional imports.", file_path) /usr/lib/python3.6/encodings/ascii.py in decode(self, input, final) 24 class IncrementalDecoder(codecs.IncrementalDecoder): 25 def decode(self, input, final=False): ---> 26 return codecs.ascii_decode(input, self.errors)[0] 27 28 class StreamWriter(Codec,codecs.StreamWriter): UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128) ``` ## Steps to reproduce Install from nlp's master branch ```python pip install git+https://github.com/huggingface/nlp.git ``` then run ```python from nlp import load_dataset # AmazonPhotos.zip is located in data/ dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data') ``` ## OS / platform details - `nlp` version: latest from master - Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyTorch version (GPU?): 1.4.0 (True) - Tensorflow version (GPU?): 2.1.0 (True) - Using GPU in script?: True - Using distributed or parallel set-up in script?: False ## Proposed solution Either change [line 762](https://github.com/huggingface/nlp/blob/7ada00b1d62f94eee22a7df38c6b01e3f27194b7/datasets/xtreme/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding: ``` # old with open(filepath) as f # new with open(filepath, encoding='utf-8') as f ``` or raise a warning that suggests setting the locale explicitly, e.g. ```python import locale locale.setlocale(locale.LC_ALL, 'C.UTF-8') ``` I have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix!

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MDExOlB1bGxSZXF1ZXN0NDYxNzgwMzUy

DOCS: Fix typo

closed

CONTRIBUTOR

Fix typo from dictionnary -> dictionary

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MDExOlB1bGxSZXF1ZXN0NDYxMDEzOTM0

[METRICS] Various improvements on metrics

closed

MEMBER

- Disallow the use of positional arguments to avoid `predictions` vs `references` mistakes - Allow to directly feed numpy/pytorch/tensorflow/pandas objects in metrics

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MDExOlB1bGxSZXF1ZXN0NDYwMjEwODYw

Keep features after transform

closed

MEMBER

When applying a transform like `map`, some features were lost (and inferred features were used). It was the case for ClassLabel, Translation, etc. To fix that, I did some modifications in the `ArrowWriter`: - added the `update_features` parameter. When it's `True`, then the features specified by the user (if any) can be updated with inferred features if their type don't match. `map` transform sets `update_features=True` when writing to cache file or buffer. Features won't change by default in `map`. - added the `with_metadata` parameter. If `True`, the `features` (after update) will be written inside the metadata of the schema in this format: ``` { "huggingface": {"features" : <serialized Features exactly like dataset_info.json>} } ``` Then, once a dataset is instantiated without info/features, these metadata are used to set the features of the dataset.

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MDExOlB1bGxSZXF1ZXN0NDYwMTAxNDYz

Add rename, remove and cast in-place operations

closed

MEMBER

Add a bunch of in-place operation leveraging the Arrow back-end to rename and remove columns and cast to new features without using the more expensive `map` method. These methods are added to `Dataset` as well as `DatasetDict`. Added tests for these new methods and add the methods to the doc. Naming follows the new pattern with a trailing underscore indicating in-place methods.

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MDExOlB1bGxSZXF1ZXN0NDYwMDcyNjQ1

Add dataset/mlsum

closed

CONTRIBUTOR

New pull request that should correct the previous errors. The load_real_data stills fails because it is looking for a default dataset URL that does not exists, this does not happen when loading the dataset with load_dataset

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MDExOlB1bGxSZXF1ZXN0NDYwMDU0NDgz

add DoQA (ACL 2020) dataset

closed

CONTRIBUTOR

adds DoQA (ACL 2020) dataset

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MDExOlB1bGxSZXF1ZXN0NDYwMDQzNDY5

Doqa

closed

CONTRIBUTOR

add DoQA (ACL 2020) dataset

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MDExOlB1bGxSZXF1ZXN0NDU5OTM2OTU2

Fix KeyboardInterrupt in map and bad indices in select

closed

MEMBER

If you interrupted a map function while it was writing, the cached file was not discarded. Therefore the next time you called map, it was loading an incomplete arrow file. We had the same issue with select if there was a bad indice at one point. To fix that I used temporary files that are renamed once everything is finished.

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MDExOlB1bGxSZXF1ZXN0NDU5OTAxMjEy

[Breaking] Update Dataset and DatasetDict API

closed

MEMBER

This PR contains a few breaking changes so it's probably good to keep it for the next (major) release: - rename the `flatten`, `drop` and `dictionary_encode_column` methods in `flatten_`, `drop_` and `dictionary_encode_column_` to indicate that these methods have in-place effects as discussed in #166. From now on we should keep the convention of having a trailing underscore for methods which have an in-place effet. I also adopt the conversion of not returning the (self) dataset for these methods. This is different than what PyTorch does for instance (`model.to()` is in-place but return the self model) but I feel like it's a safer approach in terms of UX. - remove the `dataset.columns` property which returns a low-level Apache Arrow object and should not be used by users. Similarly, remove `dataset. nbytes` which we don't really want to expose in this bare-bone format. - add a few more properties and methods to `DatasetDict`

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MDExOlB1bGxSZXF1ZXN0NDU5Mzk5ODg2

Install CoVal metric from github

closed

MEMBER

Changed the import statements in `coval.py` to direct the user to install the original package from github if it's not already installed (the warning will only display properly after merging [PR455](https://github.com/huggingface/nlp/pull/455)) Also changed the function call to use named rather than positional arguments.

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MDExOlB1bGxSZXF1ZXN0NDU5MzMyOTM1

add set_format to DatasetDict + tests

closed

MEMBER

Add the `set_format` and `formated_as` and `reset_format` to `DatasetDict`. Add tests to these for `Dataset` and `DatasetDict`. Fix some bugs uncovered by the tests for `pandas` formating.

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MDExOlB1bGxSZXF1ZXN0NDU5MTc1MTY0

add crd3(ACL 2020) dataset

closed

CONTRIBUTOR

This PR adds the **Critical Role Dungeons and Dragons Dataset** published at ACL 2020

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MDExOlB1bGxSZXF1ZXN0NDU4NTk4NTUw

Add bleurt

closed

MEMBER

This PR adds the BLEURT metric to the library. The BLEURT `Metric` downloads a TF checkpoint corresponding to its `config_name` at creation (in the `_info` function). Default is set to `bleurt-base-128`. Note that the default in the original package is `bleurt-tiny-128`, but they throw a warning and recommend using `bleurt-base-128` instead. I think it's safer to have our users have a functioning metric when they call the default behavior, we'll address discrepancies in the issues/discussions if it comes up. In addition to the BLEURT file, `load.py` was changed so we can ask users to pip install the required packages from git when they have a `setup.py` but are not on PyPL cc @ankparikh @tsellam

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MDExOlB1bGxSZXF1ZXN0NDU4NTc3MzA3

Create SECURITY.md

closed

NONE

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MDExOlB1bGxSZXF1ZXN0NDU4MzQwNzky

add builder tests

closed

MEMBER

I added `as_dataset` and `download_and_prepare` to the tests

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MDExOlB1bGxSZXF1ZXN0NDU4MTUzNjQy

Guardian authorship dataset

closed

CONTRIBUTOR

A new dataset: Guardian news articles for authorship attribution **tests passed:** python nlp-cli dummy_data datasets/guardian_authorship --save_infos --all_configs RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_guardian_authorship **Tests failed:** Real data: RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_guardian_authorship output: __init__() missing 3 required positional arguments: 'train_folder', 'valid_folder', and 'tes...' Remarks: This is the init function of my class. I am not sure why it passes in both my tests and with nlp-cli, but fails here. By the way, I ran this command with another 2 datasets and they failed: * _glue - OSError: Cannot find data file. *_newsgroup - FileNotFoundError: Local file datasets/newsgroup/dummy/18828_comp.graphics/3.0.0/dummy_data.zip doesn't exist Thank you for letting us contribute to such a huge and important library! EDIT: I was able to fix the dummy_data issue. This dataset has around 14 configurations. I was testing with only 2, but their versions were not in a sequence, they were V1.0.0 and V.12.0.0. It seems that the testing code generates testes for all the versions from 0 to MAX, and was testing for versions (and dummy_data.zip files) that do not exist. I fixed that by changing the versions to 1 and 2.

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MDExOlB1bGxSZXF1ZXN0NDU3OTIxNDMx

Fix csv/json/txt cache dir

closed

MEMBER

The cache dir for csv/json/txt datasets was always the same. This is an issue because it should be different depending on the data files provided by the user. To fix that, I added a line that use the hash of the data files provided by the user to define the cache dir. This should fix #444

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MDExOlB1bGxSZXF1ZXN0NDU3ODA5ODA2

add sogou_news

closed

CONTRIBUTOR

This PR adds the sogou news dataset #353

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MDExOlB1bGxSZXF1ZXN0NDU3NjY0NjYx

add reuters21578 dataset

closed

CONTRIBUTOR

This PR adds the `Reuters_21578` dataset https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html #353 The datasets is a lit of `.sgm` files which are a bit different from xml file indeed `xml.etree` couldn't be used to read files. I consider them as text file (to avoid using external library) and read line by line (maybe there is a better way to do, happy to get your opinion on it) In the Readme file 3 ways to split the dataset are given.: - The Modified Lewis ("ModLewis") Split: train, test and unused-set - The Modified Apte ("ModApte") Split : train, test and unused-set - The Modified Hayes ("ModHayes") Split: train and test Here I consider the last one as the readme file highlight that this split provides the ability to compare results with those of the 2 first splits.

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MDExOlB1bGxSZXF1ZXN0NDU3NjYwMDU2

add aws load metric test

closed

CONTRIBUTOR

Following issue #445 Added a test to recognize import errors of all metrics

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MDExOlB1bGxSZXF1ZXN0NDU3NjE2NDA0

[BugFix] fix wrong import of DEFAULT_TOKENIZER

closed

CONTRIBUTOR

Fixed the path to `DEFAULT_TOKENIZER` #445

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MDExOlB1bGxSZXF1ZXN0NDU3NjEyNTg5

[BugFix] fix wrong import of DEFAULT_TOKENIZER

closed

CONTRIBUTOR

Fixed the path to `DEFAULT_TOKENIZER` #445

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MDU6SXNzdWU2NjY4MzY2NTg=

DEFAULT_TOKENIZER import error in sacrebleu

closed

CONTRIBUTOR

Latest Version 0.3.0 When loading the metric "sacrebleu" there is an import error due to the wrong path 

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MDU6SXNzdWU2NjYyODA4NDI=

Keep loading old file even I specify a new file in load_dataset

closed

NONE

I used load a file called 'a.csv' by ``` dataset = load_dataset('csv', data_file='./a.csv') ``` And after a while, I tried to load another csv called 'b.csv' ``` dataset = load_dataset('csv', data_file='./b.csv') ``` However, the new dataset seems to remain the old 'a.csv' and not loading new csv file. Even worse, after I load a.csv, the load_dataset function keeps loading the 'a.csv' afterward. Is this a cache problem?

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MDU6SXNzdWU2NjYyNDY3MTY=

Cannot unpickle saved .pt dataset with torch.save()/load()

closed

CONTRIBUTOR

Saving a formatted torch dataset to file using `torch.save()`. Loading the same file fails during unpickling: ```python >>> import torch >>> import nlp >>> squad = nlp.load_dataset("squad.py", split="train") >>> squad Dataset(features: {'source_text': Value(dtype='string', id=None), 'target_text': Value(dtype='string', id=None)}, num_rows: 87599) >>> squad = squad.map(create_features, batched=True) >>> squad.set_format(type="torch", columns=["source_ids", "target_ids", "attention_mask"]) >>> torch.save(squad, "squad.pt") >>> squad_pt = torch.load("squad.pt") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 593, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 773, in _legacy_load result = unpickler.load() File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/splits.py", line 493, in __setitem__ raise ValueError("Cannot add elem. Use .add() instead.") ValueError: Cannot add elem. Use .add() instead. ``` where `create_features` is a function that tokenizes the data using `batch_encode_plus` and returns a Dict with `input_ids`, `target_ids` and `attention_mask`. ```python def create_features(batch): source_text_encoding = tokenizer.batch_encode_plus( batch["source_text"], max_length=max_source_length, pad_to_max_length=True, truncation=True) target_text_encoding = tokenizer.batch_encode_plus( batch["target_text"], max_length=max_target_length, pad_to_max_length=True, truncation=True) features = { "source_ids": source_text_encoding["input_ids"], "target_ids": target_text_encoding["input_ids"], "attention_mask": source_text_encoding["attention_mask"] } return features ``` I found a similar issue in [issue 5267 in the huggingface/transformers repo](https://github.com/huggingface/transformers/issues/5267) which was solved by downgrading to `nlp==0.2.0`. That did not solve this problem, however.

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MDU6SXNzdWU2NjYyMDE4MTA=

[Suggestion] Glue Diagnostic Data with Labels

open

NONE

Hello! First of all, thanks for setting up this useful project! I've just realised you provide the the [Glue Diagnostics Data](https://huggingface.co/nlp/viewer/?dataset=glue&config=ax) without labels, indicating in the `GlueConfig` that you've only a test set. Yet, the data with labels is available, too (see also [here](https://gluebenchmark.com/diagnostics#introduction)): https://www.dropbox.com/s/ju7d95ifb072q9f/diagnostic-full.tsv?dl=1 Have you considered incorporating it?

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MDExOlB1bGxSZXF1ZXN0NDU3MDQyMjY3

Add features parameter in load dataset

closed

MEMBER

Added `features` argument in `nlp.load_dataset`. If they don't match the data type, it raises a `ValueError`. It's a draft PR because #440 needs to be merged first.

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MDExOlB1bGxSZXF1ZXN0NDU3MDE2MjQy

Fix user specified features in map

closed

MEMBER

`.map` didn't keep the user specified features because of an issue in the writer. The writer used to overwrite the user specified features with inferred features. I also added tests to make sure it doesn't happen again.

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MDU6SXNzdWU2NjU5NjQ2NzM=

Issues: Adding a FAISS or Elastic Search index to a Dataset

closed

NONE

It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https://huggingface.co/nlp/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab. Is there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ?

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MDU6SXNzdWU2NjU4NjU0OTA=

New Datasets: IWSLT15+, ITTB

open

CONTRIBUTOR

**Links:** [iwslt](https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/datasets/iwslt.html) Don't know if that link is up to date. [ittb](http://www.cfilt.iitb.ac.in/iitb_parallel/) **Motivation**: replicate mbart finetuning results (table below)  For future readers, we already have the following language pairs in the wmt namespaces: ``` wmt14: ['cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en'] wmt15: ['cs-en', 'de-en', 'fi-en', 'fr-en', 'ru-en'] wmt16: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en'] wmt17: ['cs-en', 'de-en', 'fi-en', 'lv-en', 'ru-en', 'tr-en', 'zh-en'] wmt18: ['cs-en', 'de-en', 'et-en', 'fi-en', 'kk-en', 'ru-en', 'tr-en', 'zh-en'] wmt19: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de'] ```

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MDExOlB1bGxSZXF1ZXN0NDU2NjIzNjc3

Fix XTREME PAN-X loading

closed

MEMBER

Hi 🤗 In response to the discussion in #425 @lewtun and I made some fixes to the repo. In the original XTREME implementation the PAN-X dataset for named entity recognition loaded each word/tag pair as a single row and the sentence relation was lost. With the fix each row contains the list of all words in a single sentence and their NER tags. This is also in agreement with the [NER example](https://github.com/huggingface/transformers/tree/master/examples/token-classification) in the transformers repo. With the fix the output of the dataset should look as follows: ```python >>> dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data') >>> dataset['train'][0] {'words': ['R.H.', 'Saunders', '(', 'St.', 'Lawrence', 'River', ')', '(', '968', 'MW', ')'], 'ner_tags': ['B-ORG', 'I-ORG', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O'], 'langs': ['en', 'en', 'en', 'en', 'en', 'en', 'en', 'en', 'en', 'en', 'en']} ```

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MDU6SXNzdWU2NjU1ODIxNjc=

Google Colab - load_dataset - PyArrow exception

closed

NONE

With latest PyArrow 1.0.0 installed, I get the following exception . Restarting colab has the same issue ImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`. The error goes only when I install version 0.16.0 i.e. !pip install pyarrow==0.16.0

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MDU6SXNzdWU2NjU1MDcxNDE=

ImportWarning for pyarrow 1.0.0

closed

NONE

The following PR raised ImportWarning at `pyarrow ==1.0.0` https://github.com/huggingface/nlp/pull/265/files

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MDExOlB1bGxSZXF1ZXN0NDU2NTM3Njgz

Fixed check for pyarrow

closed

CONTRIBUTOR

Fix check for pyarrow in __init__.py. Previously would raise an error for pyarrow >= 1.0.0

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MDU6SXNzdWU2NjUzMTEwMjU=

How to reuse functionality of a (generic) dataset?

open

NONE

I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https://brat.nlplab.org/standoff.html), [dataset code](https://github.com/ArneBinder/nlp/blob/brat/datasets/brat/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to reuse formats and loading functionality for datasets with a common format? In my case, it took a bit of time to create the Brat dataset and I think others would appreciate to not have to think about that again. Also, I assume there are other formats (e.g. conll) that are widely used, so having this would really ease dataset onboarding and adoption of the library.

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MDExOlB1bGxSZXF1ZXN0NDU2MzQxNDk3

Fix handling of config files while loading datasets from multiple processes

closed

CONTRIBUTOR

When loading shards on several processes, each process upon loading the dataset will overwrite dataset_infos.json in <package path>/datasets/<dataset name>/<hash>/dataset_infos.json. It does so every time, even when the target file already exists and is identical. Because multiple processes rewrite the same file in parallel, it creates a race condition when a process tries to load the file, often resulting in a JSON decoding exception because the file is only partially written. This pull requests partially address this by comparing if the files are already identical before copying over the downloaded copy to the cached destination. There's still a race condition, but now it's less likely to occur if some basic precautions are taken by the library user, e.g., download all datasets to cache before spawning multiple processes.

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MDExOlB1bGxSZXF1ZXN0NDU2MTgyNDE2

Specify split post processing + Add post processing resources downloading

closed

MEMBER

Previously if you tried to do ```python from nlp import load_dataset wiki = load_dataset("wiki_dpr", "psgs_w100_with_nq_embeddings", split="train[:100]", with_index=True) ``` Then you'd get an error `Index size should match Dataset size...` This was because it was trying to use the full index (21M elements). To fix that I made it so post processing resources can be named according to the split. I'm going to add tests on post processing too. Note that the CI will fail as I added a new argument in `_post_processing_resources`: the AWS version of wiki_dpr fails, and there's also an error telling that it is not synced (it'll be synced once it's merged): ``` =========================== short test summary info ============================ FAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr FAILED tests/test_hf_gcp.py::TestDatasetSynced::test_script_synced_with_s3_wiki_dpr ``` EDIT: I did a change to ignore the script hash to locate the arrow files on GCS, so I removed the sync test. It was there just because of the hash logic for files on GCS

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MDExOlB1bGxSZXF1ZXN0NDU1ODAxOTI2

add DatasetDict

closed

MEMBER

## Add DatasetDict ### Overview When you call `load_dataset` it can return a dictionary of datasets if there are several splits (train/test for example). If you wanted to apply dataset transforms you had to iterate over each split and apply the transform. Instead of returning a dict, it now returns a `nlp.DatasetDict` object which inherits from dict and contains the same data as before, except that now users can call dataset transforms directly from the output, and they'll be applied on each split. Before: ```python from nlp import load_dataset squad = load_dataset("squad") print(squad.keys()) # dict_keys(['train', 'validation']) squad = { split_name: dataset.map(my_func) for split_name, dataset in squad.items() } print(squad.keys()) # dict_keys(['train', 'validation']) ``` Now: ```python from nlp import load_dataset squad = load_dataset("squad") print(squad.keys()) # dict_keys(['train', 'validation']) squad = squad.map(my_func) print(squad.keys()) # dict_keys(['train', 'validation']) ``` ### Dataset transforms `nlp.DatasetDict` implements the following dataset transforms: - map - filter - sort - shuffle ### Arguments The arguments of the methods are the same except for split-specific arguments like `cache_file_name`. For such arguments, the expected input is a dictionary `{split_name: argument_value}` It concerns: - `cache_file_name` in map, filter, sort, shuffle - `seed` and `generator` in shuffle

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MDExOlB1bGxSZXF1ZXN0NDU1NjU2MDk5

mlsum

closed

CONTRIBUTOR

Hello, The tests for the load_real_data fail, as there is no default language subset to download it looks for a file that does not exist. This bug does not happen when using the load_dataset function, as it asks you to specify a language if you do not, so I submit this PR anyway. The dataset is avalaible on : https://gitlab.lip6.fr/scialom/mlsum_data

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MDExOlB1bGxSZXF1ZXN0NDU1NjE3Nzcy

fix concatenate_datasets

closed

MEMBER

`concatenate_datatsets` used to test that the different`nlp.Dataset.schema` match, but this attribute was removed in #423

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MDExOlB1bGxSZXF1ZXN0NDU1NTk1Nzc3

Allow sequence features for beam + add processed Natural Questions

closed

MEMBER

## Allow Sequence features for Beam Datasets + add Natural Questions ### The issue The steps of beam datasets processing is the following: - download the source files and send them in a remote storage (gcs) - process the files using a beam runner (dataflow) - save output in remote storage (gcs) - convert output to arrow in remote storage (gcs) However it wasn't possible to process `natural_questions` because apache beam's processing outputs parquet files, and it's not yet possible to read parquet files with list features. ### The proposed solution To allow sequence features for beam I added a workaround that serializes the values using `json.dumps`, so that we end up with strings instead of the original features. Then when the arrow file is created, the serialized objects are transformed back to normal with `json.loads`. Not sure if there's a better way to do it. ### Natural Questions I was able to process NQ with it, and so I added the json infos file in this PR too. The processed arrow files are also stored in gcs. It allows you to load NQ with ```python from nlp import load_dataset nq = load_dataset("natural_questions") # download the 90GB arrow files from gcs and return the dataset ``` ### Tests I added a test case to make sure it works as expected. Note that the CI will fail because I am updating `natural_questions.py`: it's not synced with the script on S3. It will be synced as soon as this PR is merged. ``` =========================== short test summary info ============================ FAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_natural_questions/default ```

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MDU6SXNzdWU2NjQyMDM4OTc=

[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter

closed

NONE

It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process/thread/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together?

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MDU6SXNzdWU2NjQwMjk4NDg=

Correct data structure for PAN-X task in XTREME dataset?

closed

MEMBER

Hi 🤗 team! ## Description of the problem Thanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows: ```python from nlp import load_dataset # AmazonPhotos.zip is located in data/ dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data') dataset_train = dataset['train'] ``` However, I am not sure that `load_dataset()` is returning the correct data structure for NER. Currently, every row in `dataset_train` is of the form ```python {'word': str, 'ner_tag': str, 'lang': str} ``` but I think we actually want something like ```python {'words': List[str], 'ner_tags': List[str], 'langs': List[str]} ``` so that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples. Indeed, [this line](https://github.com/google-research/xtreme/blob/522434d1aece34131d997a97ce7e9242a51a688a/third_party/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages. ## Proposed solution Replace ```python with open(filepath) as f: data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE) for id_, row in enumerate(data): if row: lang, word = row[0].split(":")[0], row[0].split(":")[1] tag = row[1] yield id_, {"word": word, "ner_tag": tag, "lang": lang} ``` from [these lines](https://github.com/huggingface/nlp/blob/ce7d3a1d630b78fe27188d1706f3ea980e8eec43/datasets/xtreme/xtreme.py#L881-L887) of the `_generate_examples()` function with something like ```python guid_index = 1 with open(filepath, encoding="utf-8") as f: words = [] ner_tags = [] langs = [] for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": if words: yield guid_index, {"words": words, "ner_tags": ner_tags, "langs": langs} guid_index += 1 words = [] ner_tags = [] else: # pan-x data is tab separated splits = line.split("\t") # strip out en: prefix langs.append(splits[0][:2]) words.append(splits[0][3:]) if len(splits) > 1: labels.append(splits[-1].replace("\n", "")) else: # examples have no label in test set labels.append("O") ``` If you agree, me or @lvwerra would be happy to implement this and create a PR.

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MDExOlB1bGxSZXF1ZXN0NDU1MTk4MTY0

Web of science

closed

CONTRIBUTOR

this PR adds the WebofScience dataset #353

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MDExOlB1bGxSZXF1ZXN0NDU0NTU4OTA0

Change features vs schema logic

closed

MEMBER

## New logic for `nlp.Features` in datasets Previously, it was confusing to have `features` and pyarrow's `schema` in `nlp.Dataset`. However `features` is supposed to be the front-facing object to define the different fields of a dataset, while `schema` is only used to write arrow files. Changes: - Remove `schema` field in `nlp.Dataset` - Make `features` the source of truth to read/write examples - `features` can no longer be `None` in `nlp.Dataset` - Update `features` after each dataset transform such as `nlp.Dataset.map` Todo: change the tests to take these changes into account

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MDExOlB1bGxSZXF1ZXN0NDU0NTE3MDU2

- Corrected encoding for IMDB.

closed

CONTRIBUTOR

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

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MDExOlB1bGxSZXF1ZXN0NDUzNzkzMzQ1

Style change

closed

CONTRIBUTOR

make quality and make style ran on scripts

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MDExOlB1bGxSZXF1ZXN0NDUzNjI5OTk2

Better handle nested features

closed

MEMBER

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

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MDExOlB1bGxSZXF1ZXN0NDUzNTgxNzQz

EmoContext dataset add

closed

CONTRIBUTOR

EmoContext Dataset add Signed-off-by: lordtt13 <thakurtanmay72@yahoo.com>

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MDU6SXNzdWU2NjE5MTQ4NzM=

Addition of google drive links to dl_manager

closed

CONTRIBUTOR

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

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MDExOlB1bGxSZXF1ZXN0NDUzNDMyODE5

Fix docstrins multiple metrics instances

closed

MEMBER

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

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MDExOlB1bGxSZXF1ZXN0NDUzMjg1NTM4

Fix xtreme panx directory

closed

MEMBER

Fix #412

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MDU6SXNzdWU2NjA2ODcwNzY=

Something is wrong with WMT 19 kk-en dataset

open

NONE

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

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MDU6SXNzdWU2NjA2NTQwMTM=

from_dict delete?

closed

NONE

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

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MDU6SXNzdWU2NjAwNjM2NTU=

Is there a way to download only NQ dev?

closed

NONE

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

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MDU6SXNzdWU2NjAwNDcxMzk=

Unable to load XTREME dataset from disk

closed

MEMBER

Hi 🤗 team! ## Description of the problem Following the [docs](https://huggingface.co/nlp/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https://github.com/google-research/xtreme) benchmark. I have manually downloaded the `AmazonPhotos.zip` file from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset. As far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path: ``` # path where load_dataset is looking for fr.tar.gz /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/ # path where it actually exists /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/panx_dataset/ ``` ## Steps to reproduce the problem 1. Manually download the XTREME benchmark from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) 2. Run the following code snippet ```python from nlp import load_dataset # AmazonPhotos.zip is in the root of the folder dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./') ``` 3. Here is the stack trace ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-4-26786bb5fa93> in <module> ----> 1 dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./') /usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 522 download_mode=download_mode, 523 ignore_verifications=ignore_verifications, --> 524 save_infos=save_infos, 525 ) 526 /usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 430 verify_infos = not save_infos and not ignore_verifications 431 self._download_and_prepare( --> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 433 ) 434 # Sync info /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 464 split_dict = SplitDict(dataset_name=self.name) 465 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 466 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 467 # Checksums verification 468 if verify_infos: /usr/local/lib/python3.6/dist-packages/nlp/datasets/xtreme/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746/xtreme.py in _split_generators(self, dl_manager) 725 panx_dl_dir = dl_manager.extract(panx_path) 726 lang = self.config.name.split(".")[1] --> 727 lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + ".tar.gz")) 728 return [ 729 nlp.SplitGenerator( /usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in extract(self, path_or_paths) 196 """ 197 return map_nested( --> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths, 199 ) 200 /usr/local/lib/python3.6/dist-packages/nlp/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple) 170 return tuple(mapped) 171 # Singleton --> 172 return function(data_struct) 173 174 /usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in <lambda>(path) 196 """ 197 return map_nested( --> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths, 199 ) 200 /usr/local/lib/python3.6/dist-packages/nlp/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 203 elif urlparse(url_or_filename).scheme == "": 204 # File, but it doesn't exist. --> 205 raise FileNotFoundError("Local file {} doesn't exist".format(url_or_filename)) 206 else: 207 # Something unknown FileNotFoundError: Local file /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/fr.tar.gz doesn't exist ``` ## OS and hardware ``` - `nlp` version: 0.3.0 - Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyTorch version (GPU?): 1.4.0 (True) - Tensorflow version (GPU?): 2.1.0 (True) - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ```

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MDExOlB1bGxSZXF1ZXN0NDUxMjQxOTQy

Sbf

closed

CONTRIBUTOR

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

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MDExOlB1bGxSZXF1ZXN0NDUxMTEzMTI3

20newsgroup

closed

CONTRIBUTOR

Add 20Newsgroup dataset. #353

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MDU6SXNzdWU2NTkxMjg2MTE=

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

closed

NONE

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

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MDExOlB1bGxSZXF1ZXN0NDUwOTU1MTE0

Add tests datasets gcp

closed

MEMBER

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

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MDU6SXNzdWU2NTg2NzI3MzY=

MissingBeamOptions for Wikipedia 20200501.en

closed

CONTRIBUTOR

There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available): ``` nlp.load_dataset('wikipedia', "20200501.en", split='train') ``` And now, having pulled master, I get: ``` Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to /home/hltcoe/mgordon/.cache/huggingface/datasets/wikipedia/20200501.en/1.0.0/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd... Traceback (most recent call last): File "scripts/download.py", line 11, in <module> fire.Fire(download_pretrain) File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 138, in Fire component_trace = _Fire(component, args, parsed_flag_args, context, name) File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 468, in _Fire target=component.__name__) File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 672, in _CallAndUpdateTrace component = fn(*varargs, **kwargs) File "scripts/download.py", line 6, in download_pretrain nlp.load_dataset('wikipedia', "20200501.en", split='train') File "/exp/mgordon/nlp/src/nlp/load.py", line 534, in load_dataset save_infos=save_infos, File "/exp/mgordon/nlp/src/nlp/builder.py", line 460, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/exp/mgordon/nlp/src/nlp/builder.py", line 870, in _download_and_prepare "\n\t`{}`".format(usage_example) nlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S park, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/ If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). Example of usage: `load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')` ```

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MDU6SXNzdWU2NTg1ODE3NjQ=

Faster Shuffling?

closed

CONTRIBUTOR

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

https://api.github.com/repos/huggingface/datasets/issues/406/timeline

https://api.github.com/repos/huggingface/datasets/issues/405

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https://github.com/huggingface/datasets/pull/405

MDExOlB1bGxSZXF1ZXN0NDUwNTI1MTc3

Make select() faster by batching reads

closed

CONTRIBUTOR

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

https://api.github.com/repos/huggingface/datasets/issues/405/timeline

https://api.github.com/repos/huggingface/datasets/issues/404

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

MDExOlB1bGxSZXF1ZXN0NDUwMzY4Mjg4

Add seed in metrics

closed

MEMBER

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

https://api.github.com/repos/huggingface/datasets/issues/404/timeline

https://api.github.com/repos/huggingface/datasets/issues/403

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https://github.com/huggingface/datasets/pull/403

MDExOlB1bGxSZXF1ZXN0NDUwMzAzNjI2

return python objects instead of arrays by default

closed

MEMBER

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

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https://api.github.com/repos/huggingface/datasets/issues/402

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https://github.com/huggingface/datasets/pull/402

MDExOlB1bGxSZXF1ZXN0NDUwMDI2NTE0

Search qa

closed

CONTRIBUTOR

add SearchQA dataset #336

https://api.github.com/repos/huggingface/datasets/issues/402/timeline

https://api.github.com/repos/huggingface/datasets/issues/401

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https://github.com/huggingface/datasets/pull/401

MDExOlB1bGxSZXF1ZXN0NDUwMDIyNTc0

add web_questions

closed

CONTRIBUTOR

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

https://api.github.com/repos/huggingface/datasets/issues/401/timeline

https://api.github.com/repos/huggingface/datasets/issues/400

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https://github.com/huggingface/datasets/pull/400

MDExOlB1bGxSZXF1ZXN0NDUwMDA1MDU5

Web questions

closed

CONTRIBUTOR

add the WebQuestion dataset #336

https://api.github.com/repos/huggingface/datasets/issues/400/timeline

https://api.github.com/repos/huggingface/datasets/issues/399

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https://github.com/huggingface/datasets/pull/399

MDExOlB1bGxSZXF1ZXN0NDQ5ODkxNTEy

Spelling mistake

closed

CONTRIBUTOR

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

https://api.github.com/repos/huggingface/datasets/issues/399/timeline

https://api.github.com/repos/huggingface/datasets/issues/398

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https://github.com/huggingface/datasets/pull/398

MDExOlB1bGxSZXF1ZXN0NDQ5NjE1OTk1

Add inline links

closed

CONTRIBUTOR

Add inline links to `Contributing.md`

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https://api.github.com/repos/huggingface/datasets/issues/397

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https://github.com/huggingface/datasets/pull/397

MDExOlB1bGxSZXF1ZXN0NDQ5NjE1MDA4

Add contiguous sharding

closed

CONTRIBUTOR

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

https://api.github.com/repos/huggingface/datasets/issues/397/timeline

https://api.github.com/repos/huggingface/datasets/issues/396

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https://github.com/huggingface/datasets/pull/396

MDExOlB1bGxSZXF1ZXN0NDQ5NTg3MDQ4

Fix memory issue when doing select

closed

MEMBER

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

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MDU6SXNzdWU2NTc0NTQ5ODM=