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MDU6SXNzdWU3NTM2NzkwMjA=

Hello

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MDExOlB1bGxSZXF1ZXN0NTI5NzA4MTcy

add inquisitive

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Adding inquisitive qg dataset More info: https://github.com/wjko2/INQUISITIVE

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MDExOlB1bGxSZXF1ZXN0NTI5NzA1MzM4

Add Turku NLP Corpus for Finnish NER

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MDExOlB1bGxSZXF1ZXN0NTI5NjcyMzgw

Add DART

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- **Name:** *DART* - **Description:** *DART is a large dataset for open-domain structured data record to text generation.* - **Paper:** *https://arxiv.org/abs/2007.02871* - **Data:** *https://github.com/Yale-LILY/dart#leaderboard* ### Checkbox - [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template - [x] Fill the `_DESCRIPTION` and `_CITATION` variables - [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()` - [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class. - [x] Generate the metadata file `dataset_infos.json` for all configurations - [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB) - [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs - [x] Both tests for the real data and the dummy data pass.

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MDExOlB1bGxSZXF1ZXN0NTI5NjIyMDQx

Add CC-100 dataset

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Add CC-100. Close #773

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MDExOlB1bGxSZXF1ZXN0NTI5NjEzOTA4

Add XOR QA Dataset

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CONTRIBUTOR

Added XOR Question Answering Dataset. The link to the dataset can be found [here](https://nlp.cs.washington.edu/xorqa/) - [x] Followed the instructions in CONTRIBUTING.md - [x] Ran the tests successfully - [x] Created the dummy data

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MDExOlB1bGxSZXF1ZXN0NTI5NTIzMTgz

add dream dataset

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Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension More details: https://dataset.org/dream/ https://github.com/nlpdata/dream

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MDU6SXNzdWU3NTM0MzQ0NzI=

wrong length with datasets

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CONTRIBUTOR

Hi I have a MRPC dataset which I convert it to seq2seq format, then this is of this format: `Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10) ` I feed it to a dataloader: ``` dataloader = DataLoader( train_dataset, batch_size=self.args.train_batch_size, sampler=train_sampler, collate_fn=self.data_collator, drop_last=self.args.dataloader_drop_last, num_workers=self.args.dataloader_num_workers, ) ``` now if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks

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MDExOlB1bGxSZXF1ZXN0NTI5NDgzOTk4

Add conll2002

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Adding the Conll2002 dataset for NER. More info here : https://www.clips.uantwerpen.be/conll2002/ner/ ### Checkbox - [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template - [x] Fill the `_DESCRIPTION` and `_CITATION` variables - [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()` - [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class. - [x] Generate the metadata file `dataset_infos.json` for all configurations - [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB) - [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs - [x] Both tests for the real data and the dummy data pass.

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https://github.com/huggingface/datasets/pull/917

MDExOlB1bGxSZXF1ZXN0NTI5NDc5MTIy

Addition of Concode Dataset

closed

CONTRIBUTOR

##Overview Concode Dataset contains pairs of Nl Queries and the corresponding Code.(Contextual Code Generation) Reference Links Paper Link = https://arxiv.org/pdf/1904.09086.pdf Github Link =https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/text-to-code

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MDExOlB1bGxSZXF1ZXN0NTI5NDY3MTkx

Add Swedish NER Corpus

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MDU6SXNzdWU3NTMxMTg0ODE=

Shall we change the hashing to encoding to reduce potential replicated cache files?

open

NONE

Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write. If you have interest in this, I'd love to help :).

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MDExOlB1bGxSZXF1ZXN0NTI5MTM2Njk3

Add list_github_datasets api for retrieving dataset name list in github repo

closed

NONE

Thank you for your great effort on unifying data processing for NLP! This pr is trying to add a new api `list_github_datasets` in the `inspect` module. The reason for it is that the current `list_datasets` api need to access https://huggingface.co/api/datasets to get a large json. However, this connection can be really slow... (I was visiting from China) and from my own experience, most of the time `requests.get` failed to download the whole json after a long wait and will trigger fault in `r.json()`. I also noticed that the current implementation will first try to download from github, which makes me be able to smoothly run `load_dataset('squad')` in the example. Therefore, I think it would be better if we can have an api to get the list of datasets that are available on github, and it will also improve newcomers' experience (it is a little frustrating if one cannot successfully run the first function in the README example.) before we have faster source for huggingface.co. As for the implementation, I've added a `dataset_infos.json` file under the `datasets` folder, and it has the following structure: ```json { "id": "aeslc", "folder": "datasets/aeslc", "dataset_infos": "datasets/aeslc/dataset_infos.json" }, ... { "id": "json", "folder": "datasets/json" }, ... ``` The script I used to get this file is: ```python import json import os DATASETS_BASE_DIR = "/root/datasets" DATASET_INFOS_JSON = "dataset_infos.json" datasets = [] for item in os.listdir(os.path.join(DATASETS_BASE_DIR, "datasets")): if os.path.isdir(os.path.join(DATASETS_BASE_DIR, "datasets", item)): datasets.append(item) datasets.sort() total_ds_info = [] for ds in datasets: ds_dir = os.path.join("datasets", ds) ds_info_dir = os.path.join(ds_dir, DATASET_INFOS_JSON) if os.path.isfile(os.path.join(DATASETS_BASE_DIR, ds_info_dir)): total_ds_info.append({"id": ds, "folder": ds_dir, "dataset_infos": ds_info_dir}) else: total_ds_info.append({"id": ds, "folder": ds_dir}) with open(DATASET_INFOS_JSON, "w") as f: json.dump(total_ds_info, f) ``` The new `dataset_infos.json` was saved as a formated json so that it will be easy to add new dataset. When calling `list_github_datasets`, the user will get the list of dataset names in this github repo and if `with_details` is set to be `True`, they can get the url of specific dataset info. Thank you for your time on reviewing this pr :).

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MDExOlB1bGxSZXF1ZXN0NTI5MDkyOTc3

My new dataset PEC

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CONTRIBUTOR

A new dataset PEC published in EMNLP 2020.

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MDU6SXNzdWU3NTI4MDYyMTU=

datasets module not found

closed

NONE

Currently, running `from datasets import load_dataset` will throw a `ModuleNotFoundError: No module named 'datasets'` error.

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MDU6SXNzdWU3NTI3NzI3MjM=

Grindr meeting app web.Grindr

closed

NONE

## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

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MDExOlB1bGxSZXF1ZXN0NTI4ODE1NDYz

Add FiNER dataset

closed

CONTRIBUTOR

Hi, this PR adds "A Finnish News Corpus for Named Entity Recognition" as new `finer` dataset. The dataset is described in [this paper](https://arxiv.org/abs/1908.04212). The data is publicly available in [this GitHub](https://github.com/mpsilfve/finer-data). Notice: they provide two testsets. The additional test dataset taken from Wikipedia is named as "test_wikipedia" split.

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MDExOlB1bGxSZXF1ZXN0NTI4NzUzMjcz

Add dependency on black for tests

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Add package 'black' as an installation requirement for tests.

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MDExOlB1bGxSZXF1ZXN0NTI4NzQ4ODMx

Remove os.path.join from all URLs

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Remove `os.path.join` from all URLs in dataset scripts.

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MDExOlB1bGxSZXF1ZXN0NTI4NzM0MDY0

Fix url with backslash in windows for blimp and pg19

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Following #903 I also fixed blimp and pg19 which were using the `os.path.join` to create urls cc @albertvillanova

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MDExOlB1bGxSZXF1ZXN0NTI4NzI3OTEy

Disallow backslash in urls

closed

MEMBER

Following #903 @albertvillanova noticed that there are sometimes bad usage of `os.path.join` in datasets scripts to create URLS. However this should be avoided since it doesn't work on windows. I'm suggesting a test to make sure we that all the urls don't have backslashes in them in the datasets scripts. The tests works by adding a callback feature to the MockDownloadManager used to test the dataset scripts. In a download callback I just make sure that the url is valid.

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MDExOlB1bGxSZXF1ZXN0NTI4NzA5NTUx

Very detailed step-by-step on how to add a dataset

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Add very detailed step-by-step instructions to add a new dataset to the library.

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MDExOlB1bGxSZXF1ZXN0NTI4Njk5NDQ3

Fix URL with backslash in Windows

closed

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In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash. In general, `os.path.join` should be avoided to generate URLs.

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MDExOlB1bGxSZXF1ZXN0NTI4Njg3NTYw

Follow cache_dir parameter to gcs downloader

closed

MEMBER

As noticed in #900 the cache_dir parameter was not followed to the downloader in the case of an already processed dataset hosted on our google storage (one of them is natural questions). Fix #900

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MDExOlB1bGxSZXF1ZXN0NTI4NTk3NDU5

Addition of Nl2Bash Dataset

closed

CONTRIBUTOR

## Overview The NL2Bash data contains over 10,000 instances of linux shell commands and their corresponding natural language descriptions provided by experts, from the Tellina system. The dataset features 100+ commonly used shell utilities. ## Footnotes The following dataset marks the first ML on source code related Dataset in datasets module. It'll be really useful as a lot of the research direction involves Transformer Based Model. Thanks. ### Reference Links > Paper Link = https://arxiv.org/pdf/1802.08979.pdf > Github Link = https://github.com/TellinaTool/nl2bash

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MDU6SXNzdWU3NTIyMTQwNjY=

datasets.load_dataset() custom chaching directory bug

closed

NONE

Hello, I'm having issue with loading a dataset with a custom `cache_dir`. Despite specifying the output dir, it is still downloaded to `~/.cache`. ## Environment info - `datasets` version: 1.1.3 - Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1 - Python version: 3.7.3 ## The code I'm running: ```python import datasets from pathlib import Path validation_dataset = datasets.load_dataset("natural_questions", split="validation[:5%]", cache_dir=Path("./data")) ``` ## The output: * The dataset is downloaded to my home directory's `.cache` * A new empty directory named "`natural_questions` is created in the specified directory `.data` * `tree data` in the shell outputs: ``` data └── natural_questions └── default └── 0.0.2 3 directories, 0 files ``` The output: ``` Downloading: 8.61kB [00:00, 5.11MB/s] Downloading: 13.6kB [00:00, 7.89MB/s] Using custom data configuration default Downloading and preparing dataset natural_questions/default (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size, total: 134.92 GiB) to ./data/natural_questions/default/0.0.2/867dbbaf9137c1b8 3ecb19f5eb80559e1002ea26e702c6b919cfa81a17a8c531... Downloading: 100%|██████████████████████████████████████████████████| 13.6k/13.6k [00:00<00:00, 1.51MB/s] Downloading: 7%|███▎ | 6.70G/97.4G [03:46<1:37:05, 15.6MB/s] ``` ## Expected behaviour: The dataset "Natural Questions" should be downloaded to the directory "./data"

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MDExOlB1bGxSZXF1ZXN0NTI4NTYzNzYz

Allow arrow based builder in auto dummy data generation

closed

MEMBER

Following #898 I added support for arrow based builder for the auto dummy data generator

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MDExOlB1bGxSZXF1ZXN0NTI4NTI4MDY1

Adding SQA dataset

closed

MEMBER

As discussed in #880 Seems like automatic dummy-data generation doesn't work if the builder is a `ArrowBasedBuilder`, do you think you could take a look @lhoestq ?

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MDU6SXNzdWU3NTIxMDAyNTY=

Dataset viewer issues

closed

CONTRIBUTOR

I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues/bugs though: - the URL is still under `nlp`, perhaps an alias for `datasets` can be made - when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user ```bash IndexError: list index out of range Traceback: File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script exec(code, module.__dict__) File "/home/sasha/nlp-viewer/run.py", line 316, in <module> st.table(style) File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 122, in wrapped_method return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index) File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 367, in _enqueue_new_element_delta rv = marshall_element(msg.delta.new_element) File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element return method(dg, element, *args, **kwargs) File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 2944, in table data_frame_proto.marshall_data_frame(data, element.table) File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 54, in marshall_data_frame _marshall_styles(proto_df.style, df, styler) File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 73, in _marshall_styles translated_style = styler._translate() File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/pandas/io/formats/style.py", line 351, in _translate * (len(clabels[0]) - len(hidden_columns)) ``` - there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https://huggingface.co/nlp/viewer/?dataset=wmt19&config=cs-en). This problem goes away when you enable "List view", because then some syntax highlighteris used, and the special characters are coded correctly.

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MDExOlB1bGxSZXF1ZXN0NTI4MjcyMjc0

Add template and documentation for dataset card

closed

MEMBER

This PR adds a template for dataset cards, as well as a guide to filling out the template and a completed example for the ELI5 dataset, building on the work of @mcmillanmajora New pull requests adding datasets should now have a README.md file which serves both to hold the tags we will have to index the datasets and as a data statement. The template is designed to be pretty extensive. The idea is that the person who uploads the dataset should put in all the basic information (at least the Dataset Description section) and whatever else they feel comfortable adding and leave the `[More Information Needed]` annotation everywhere else as a placeholder. We will then work with @mcmillanmajora to involve the data authors more directly in filling out the remaining information. Direct links to: - [Documentation](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/templates/README_guide.md) - [Empty template](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/templates/README.md) - [ELI5 example](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/datasets/eli5/README.md)

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MDExOlB1bGxSZXF1ZXN0NTI4MjMyMjU3

Better messages regarding split naming

closed

MEMBER

I made explicit the error message when a bad split name is used. Also I wanted to allow the `-` symbol for split names but actually this symbol is used to name the arrow files `{dataset_name}-{dataset_split}.arrow` so we should probably keep it this way, i.e. not allowing the `-` symbol in split names. Moreover in the future we might want to use `{dataset_name}-{dataset_split}-{shard_id}_of_{n_shards}.arrow` and reuse the `-` symbol.

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MDExOlB1bGxSZXF1ZXN0NTI4MTkzNzQy

Allow several tags sets

closed

MEMBER

Hi ! Currently we have three dataset cards : snli, cnn_dailymail and allocine. For each one of those datasets a set of tag is defined. The set of tags contains fields like `multilinguality`, `task_ids`, `licenses` etc. For certain datasets like `glue` for example, there exist several configurations: `sst2`, `mnli` etc. Therefore we should define one set of tags per configuration. However the current format used for tags only supports one set of tags per dataset. In this PR I propose a simple change in the yaml format used for tags to allow for several sets of tags. Let me know what you think, especially @julien-c let me know if it's good for you since it's going to be parsed by moon-landing

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MDExOlB1bGxSZXF1ZXN0NTI4MTY4NDgx

add metrec: arabic poetry dataset

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NTI4MTMxNTE1

Add a few datasets of reference in the documentation

closed

MEMBER

I started making a small list of various datasets of reference in the documentation. Since many datasets share a lot in common I think it's good to have a list of datasets scripts to get some inspiration from. Let me know what you think, and if you have ideas of other datasets that we may add to this list, please let me know.

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MDExOlB1bGxSZXF1ZXN0NTI4MDY1MTQ3

gitignore .python-version

closed

MEMBER

ignore `.python-version` added by `pyenv`

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MDExOlB1bGxSZXF1ZXN0NTI4MDI5NjA3

Add LER

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NTI3NjkwODE2

Optional per-dataset default config name

closed

CONTRIBUTOR

This PR adds a `DEFAULT_CONFIG_NAME` class attribute to `DatasetBuilder`. This allows a dataset to have a specified default config name when a dataset has more than one config but the user does not specify it. For example, after defining `DEFAULT_CONFIG_NAME = "combined"` in PolyglotNER, a user can now do the following: ```python ds = load_dataset("polyglot_ner") ``` which is equivalent to, ```python ds = load_dataset("polyglot_ner", "combined") ``` In effect (for this particular dataset configuration), this means that if the user doesn't specify a language, they are given the combined dataset including all languages. Since it doesn't always make sense to have a default config, this feature is opt-in. If `DEFAULT_CONFIG_NAME` is not defined and a user does not pass a config for a dataset with multiple configs available, a ValueError is raised like usual. Let me know what you think about this approach @lhoestq @thomwolf and I'll add some documentation and define a default for some of our existing datasets.

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MDU6SXNzdWU3NTA5NDQ0MjI=

Nested lists are zipped unexpectedly

closed

CONTRIBUTOR

I might misunderstand something, but I expect that if I define: ```python "top": datasets.features.Sequence({ "middle": datasets.features.Sequence({ "bottom": datasets.Value("int32") }) }) ``` And I then create an example: ```python yield 1, { "top": [{ "middle": [ {"bottom": 1}, {"bottom": 2} ] }] } ``` I then load my dataset: ```python train = load_dataset("my dataset")["train"] ``` and expect to be able to access `data[0]["top"][0]["middle"][0]`. That is not the case. Here is `data[0]` as JSON: ```json {"top": {"middle": [{"bottom": [1, 2]}]}} ``` Clearly different than the thing I inputted. ```json {"top": [{"middle": [{"bottom": 1},{"bottom": 2}]}]} ```

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MDU6SXNzdWU3NTA4Njg4MzE=

pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>

open

CONTRIBUTOR

I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) ```python def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=datasets.Features( { "pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32")) } ), homepage=_HOMEPAGE, citation=_CITATION, ) def _generate_examples(self): """ Yields examples. """ yield 1, { "pose": [np.zeros(shape=(137, 2), dtype=np.float32)] } ``` But this doesn't work - > pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>

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MDExOlB1bGxSZXF1ZXN0NTI3NDU1MDU5

Fix wikipedia custom config

closed

MEMBER

It should be possible to use the wikipedia dataset with any `language` and `date`. However it was not working as noticed in #784 . Indeed the custom wikipedia configurations were not enabled for some reason. I fixed that and was able to run ```python from datasets import load_dataset load_dataset("./datasets/wikipedia", language="zh", date="20201120", beam_runner='DirectRunner') ``` cc @stvhuang @SamuelCahyawijaya Fix #784

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MDU6SXNzdWU3NTA3ODkwNTI=

Very slow cold-start

closed

CONTRIBUTOR

Hi, I expect when importing `datasets` that nothing major happens in the background, and so the import should be insignificant. When I load a metric, or a dataset, its fine that it takes time. The following ranges from 3 to 9 seconds: ``` python -m timeit -n 1 -r 1 'from datasets import load_dataset' ``` edit: sorry for the mis-tag, not sure how I added it.

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MDExOlB1bGxSZXF1ZXN0NTI2NjA5MDc1

Auto generate dummy data

closed

MEMBER

When adding a new dataset to the library, dummy data creation can take some time. To make things easier I added a command line tool that automatically generates dummy data when possible. The tool only supports certain data files types: txt, csv, tsv, jsonl, json and xml. Here are some examples: ``` python datasets-cli dummy_data ./datasets/snli --auto_generate python datasets-cli dummy_data ./datasets/squad --auto_generate --json_field data python datasets-cli dummy_data ./datasets/iwslt2017 --auto_generate --xml_tag seg --match_text_files "train*" --n_lines 15 # --xml_tag seg => each sample corresponds to a "seg" tag in the xml tree # --match_text_files "train*" => also match text files that don't have a proper text file extension (no suffix like ".txt" for example) # --n_lines 15 => some text files have headers so we have to use at least 15 lines ``` and here is the command usage: ``` usage: datasets-cli <command> [<args>] dummy_data [-h] [--auto_generate] [--n_lines N_LINES] [--json_field JSON_FIELD] [--xml_tag XML_TAG] [--match_text_files MATCH_TEXT_FILES] [--keep_uncompressed] [--cache_dir CACHE_DIR] path_to_dataset positional arguments: path_to_dataset Path to the dataset (example: ./datasets/squad) optional arguments: -h, --help show this help message and exit --auto_generate Try to automatically generate dummy data --n_lines N_LINES Number of lines or samples to keep when auto- generating dummy data --json_field JSON_FIELD Optional, json field to read the data from when auto- generating dummy data. In the json data files, this field must point to a list of samples as json objects (ex: the 'data' field for squad-like files) --xml_tag XML_TAG Optional, xml tag name of the samples inside the xml files when auto-generating dummy data. --match_text_files MATCH_TEXT_FILES Optional, a comma separated list of file patterns that looks for line-by-line text files other than *.txt or *.csv. Example: --match_text_files *.label --keep_uncompressed Don't compress the dummy data folders when auto- generating dummy data. Useful for debugging for to do manual adjustements before compressing. --cache_dir CACHE_DIR Cache directory to download and cache files when auto- generating dummy data ``` The command generates all the necessary `dummy_data.zip` files (one per config). How it works: - it runs the split_generators() method of the dataset script to download the original data files - when downloading it records a mapping between the downloaded files and the corresponding expected dummy data files paths - then for each data file it creates the dummy data file keeping only the first samples (the strategy depends on the type of file) - finally it compresses the dummy data folders into dummy_zip files ready for dataset tests Let me know if that makes sense or if you have ideas to improve this tool ! I also added a unit test.

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MDU6SXNzdWU3NDk3NTA4MDE=

Downloading/caching only a part of a datasets' dataset.

open

NONE

Hi, I want to use the validation data *only* (of natural question). I don't want to have the whole dataset cached in my machine, just the dev set. Is this possible? I can't find a way to do it in the docs. Thank you, Sapir

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MDExOlB1bGxSZXF1ZXN0NTI2NDQyMjA2

Update README.md

closed

CONTRIBUTOR

"no label" is "-" in the original dataset but "-1" in Huggingface distribution.

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MDExOlB1bGxSZXF1ZXN0NTI2MzQ5MDM2

Use GCP download url instead of tensorflow custom download for boolq

closed

MEMBER

BoolQ is a dataset that used tf.io.gfile.copy to download the file from a GCP bucket. It prevented the dataset to be downloaded twice because of a FileAlreadyExistsError. Even though the error could be fixed by providing `overwrite=True` to the tf.io.gfile.copy call, I changed the script to use GCP download urls and use regular downloads instead and remove the tensorflow dependency. Fix #875

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MDU6SXNzdWU3NDg5NDk2MDY=

Add SQA

closed

CONTRIBUTOR

## Adding a Dataset - **Name:** SQA (Sequential Question Answering) by Microsoft. - **Description:** The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total. - **Paper:** https://www.microsoft.com/en-us/research/publication/search-based-neural-structured-learning-sequential-question-answering/ - **Data:** https://www.microsoft.com/en-us/download/details.aspx?id=54253 - **Motivation:** currently, the [Tapas](https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html) algorithm by Google AI is being added to the Transformers library (see https://github.com/huggingface/transformers/pull/8113). It would be great to use that model in combination with this dataset, on which it achieves SOTA results (average question accuracy of 0.71). Note 1: this dataset actually consists of 2 types of files: 1) TSV files, containing the questions, answer coordinates and answer texts (for training, dev and test) 2) a folder of csv files, which contain the actual tabular data Note 2: if you download the dataset straight from the download link above, then you will see that the `answer_coordinates` and `answer_text` columns are string lists of string tuples and strings respectively, which is not ideal. It would be better to make them true Python lists of tuples and strings respectively (using `ast.literal_eval`), before uploading them to the HuggingFace hub. Adding this would be great! Then we could possibly also add [WTQ (WikiTable Questions)](https://github.com/ppasupat/WikiTableQuestions) and [TabFact (Tabular Fact Checking)](https://github.com/wenhuchen/Table-Fact-Checking) on which TAPAS also achieves state-of-the-art results. Note that the TAPAS algorithm requires these datasets to first be converted into the SQA format. Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).

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MDU6SXNzdWU3NDg4NDg4NDc=

boolq does not load

closed

CONTRIBUTOR

Hi I am getting these errors trying to load boolq thanks Traceback (most recent call last): File "test.py", line 5, in <module> data = AutoTask().get("boolq").get_dataset("train", n_obs=10) File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 42, in get_dataset dataset = self.load_dataset(split=split) File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 38, in load_dataset return datasets.load_dataset(self.task.name, split=split) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 150, in download_custom get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 472, in get_from_cache f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been" FileNotFoundError: Cannot find the requested files in the cached path at /idiap/home/rkarimi/.cache/huggingface/datasets/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.

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MDU6SXNzdWU3NDg2MjE5ODE=

Loading Data From S3 Path in Sagemaker

open

NONE

In Sagemaker Im tring to load the data set from S3 path as follows `train_path = 's3://xxxxxxxxxx/xxxxxxxxxx/train.csv' valid_path = 's3://xxxxxxxxxx/xxxxxxxxxx/validation.csv' test_path = 's3://xxxxxxxxxx/xxxxxxxxxx/test.csv' data_files = {} data_files["train"] = train_path data_files["validation"] = valid_path data_files["test"] = test_path extension = train_path.split(".")[-1] datasets = load_dataset(extension, data_files=data_files, s3_enabled=True) print(datasets)` I getting an error of `algo-1-7plil_1 | File "main.py", line 21, in <module> algo-1-7plil_1 | datasets = load_dataset(extension, data_files=data_files) algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/load.py", line 603, in load_dataset algo-1-7plil_1 | **config_kwargs, algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 155, in __init__ algo-1-7plil_1 | **config_kwargs, algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 305, in _create_builder_config algo-1-7plil_1 | m.update(str(os.path.getmtime(data_file))) algo-1-7plil_1 | File "/opt/conda/lib/python3.6/genericpath.py", line 55, in getmtime algo-1-7plil_1 | return os.stat(filename).st_mtime algo-1-7plil_1 | FileNotFoundError: [Errno 2] No such file or directory: 's3://lsmv-sagemaker/pubmedbert/test.csv` But when im trying with pandas , it is able to load from S3 Does the datasets library support S3 path to load

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MDU6SXNzdWU3NDgyMzQ0Mzg=

DataLoader(datasets) become more and more slowly within iterations

closed

NONE

Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly! ``` dataset = load_from_disk(dataset_path) # around 21,000,000 lines lineloader = tqdm(DataLoader(dataset, batch_size=1)) for idx, line in enumerate(lineloader): # do some thing for each line ``` In the begining, the loading speed is around 2000it/s, but after 1 minutes later, the speed is much slower, just around 800it/s. And when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it/s. Could you please help me with this problem? Thanks a lot!

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MDU6SXNzdWU3NDgxOTUxMDQ=

imdb dataset cannot be loaded

closed

CONTRIBUTOR

Hi I am trying to load the imdb train dataset `dataset = datasets.load_dataset("imdb", split="train")` getting following errors, thanks for your help ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 558, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 73, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=32660064, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=26476338, num_examples=20316, dataset_name='imdb')}, {'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}] >>> dataset = datasets.load_dataset("imdb", split="train") ```

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MDU6SXNzdWU3NDgxOTQzMTE=

bug in boolq dataset loading

closed

CONTRIBUTOR

Hi I am trying to load boolq dataset: ``` import datasets datasets.load_dataset("boolq") ``` I am getting the following errors, thanks for your help ``` >>> import datasets 2020-11-22 09:16:30.070332: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2020-11-22 09:16:30.070389: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. >>> datasets.load_dataset("boolq") cahce dir /idiap/temp/rkarimi/cache_home/datasets cahce dir /idiap/temp/rkarimi/cache_home/datasets Using custom data configuration default Downloading and preparing dataset boolq/default (download: 8.36 MiB, generated: 7.47 MiB, post-processed: Unknown size, total: 15.83 MiB) to /idiap/temp/rkarimi/cache_home/datasets/boolq/default/0.1.0/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11... cahce dir /idiap/temp/rkarimi/cache_home/datasets cahce dir /idiap/temp/rkarimi/cache_home/datasets/downloads Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 149, in download_custom custom_download(url, path) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/tensorflow/python/lib/io/file_io.py", line 516, in copy_v2 compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite) tensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists ```

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MDU6SXNzdWU3NDgxOTMxNDA=

trec dataset unavailable

closed

CONTRIBUTOR

Hi when I try to load the trec dataset I am getting these errors, thanks for your help `datasets.load_dataset("trec", split="train") ` ``` File "<stdin>", line 1, in <module> File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 477, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ```

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MDU6SXNzdWU3NDc5NTk1MjM=

load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error

closed

NONE

``` from datasets import load_dataset dataset = load_dataset('cnn_dailymail', '3.0.0') ``` Stack trace: ``` --------------------------------------------------------------------------- NotADirectoryError Traceback (most recent call last) <ipython-input-6-2e06a8332652> in <module>() 1 from datasets import load_dataset ----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0') 5 frames /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 608 download_config=download_config, 609 download_mode=download_mode, --> 610 ignore_verifications=ignore_verifications, 611 ) 612 /usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 513 if not downloaded_from_gcs: 514 self._download_and_prepare( --> 515 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 516 ) 517 # Sync info /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 568 split_dict = SplitDict(dataset_name=self.name) 569 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 570 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 571 572 # Checksums verification /root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _split_generators(self, dl_manager) 252 def _split_generators(self, dl_manager): 253 dl_paths = dl_manager.download_and_extract(_DL_URLS) --> 254 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN) 255 # Generate shared vocabulary 256 /root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _subset_filenames(dl_paths, split) 153 else: 154 logging.fatal("Unsupported split: %s", split) --> 155 cnn = _find_files(dl_paths, "cnn", urls) 156 dm = _find_files(dl_paths, "dm", urls) 157 return cnn + dm /root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict) 132 else: 133 logging.fatal("Unsupported publisher: %s", publisher) --> 134 files = sorted(os.listdir(top_dir)) 135 136 ret_files = [] NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' ``` I have ran the code on Google Colab

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MDExOlB1bGxSZXF1ZXN0NTI0ODM4NjEx

Add IndicGLUE dataset and Metrics

closed

CONTRIBUTOR

Added IndicGLUE benchmark for evaluating models on 11 Indian Languages. The descriptions of the tasks and the corresponding paper can be found [here](https://indicnlp.ai4bharat.org/indic-glue/) - [x] Followed the instructions in CONTRIBUTING.md - [x] Ran the tests successfully - [x] Created the dummy data

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MDU6SXNzdWU3NDc0NzAxMzY=

terminate called after throwing an instance of 'google::protobuf::FatalException'

closed

CONTRIBUTOR

Hi I am using the dataset "iwslt2017-en-nl", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [02:47<00:00, 2.18s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0): terminate called after throwing an instance of 'google::protobuf::FatalException' what(): CHECK failed: (index) >= (0): run_t5_base_eval.sh: line 19: 5795 Aborted

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MDU6SXNzdWU3NDcwMjE5OTY=

[Feature Request] Add optional parameter in text loading script to preserve linebreaks

closed

NONE

I'm working on a project about rhyming verse using phonetic poetry and song lyrics, and line breaks are a vital part of the data. I recently switched over to use the datasets library when my various corpora grew larger than my computer's memory. And so far, it is SO great. But the first time I processed all of my data into a dataset, I hadn't realized the text loader script was processing the source files line-by-line and stripping off the newlines. Once I caught the issue, I made my own data loader by modifying one line in the default text loader (changing `batch = batch.splitlines()` to `batch = batch.splitlines(True)` inside `_generate_tables`). And so I'm all set as far as my project is concerned. But if my use case is more general, it seems like it'd be pretty trivial to add a kwarg to the default text loader called keeplinebreaks or something, which would default to False and get passed to `splitlines()`.

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MDExOlB1bGxSZXF1ZXN0NTIzODc3OTkw

Update ner datasets infos

closed

MEMBER

Update the dataset_infos.json files for changes made in #850 regarding the ner datasets feature types (and the change to ClassLabel) I also fixed the ner types of conll2003

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MDExOlB1bGxSZXF1ZXN0NTIzMzc2MzQ3

Consistent metric outputs

closed

MEMBER

To automate the use of metrics, they should return consistent outputs. In particular I'm working on adding a conversion of metrics to keras metrics. To achieve this we need two things: - have each metric return dictionaries of string -> floats since each keras metrics should return one float - define in the metric info the different fields of the output dictionary In this PR I'm adding these two features. I also fixed a few bugs in some metrics #867 needs to be merged first

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MDExOlB1bGxSZXF1ZXN0NTIzMjc4MjI4

Fix some metrics feature types

closed

MEMBER

Replace `int` feature type to `int32` since `int` is not a pyarrow dtype in those metrics: - accuracy - precision - recall - f1 I also added the sklearn citation and used keyword arguments to remove future warnings

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MDU6SXNzdWU3NDU3MTkyMjI=

OSCAR from Inria group

closed

NONE

## Adding a Dataset - **Name:** *OSCAR* (Open Super-large Crawled ALMAnaCH coRpus), multilingual parsing of Common Crawl (separate crawls for many different languages), [here](https://oscar-corpus.com/). - **Description:** *OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.* - **Paper:** *[here](https://hal.inria.fr/hal-02148693)* - **Data:** *[here](https://oscar-corpus.com/)* - **Motivation:** *useful for unsupervised tasks in separate languages. In an ideal world, your team would be able to obtain the unshuffled version, that could be used to train GPT-2-like models (the shuffled version, I suppose, could be used for translation).* I am aware that you do offer the "colossal" Common Crawl dataset already, but this has the advantage to be available in many subcorpora for different languages.

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MDU6SXNzdWU3NDU0MzA0OTc=

Have Trouble importing `datasets`

closed

CONTRIBUTOR

I'm failing to import transformers (v4.0.0-dev), and tracing the cause seems to be failing to import datasets. I cloned the newest version of datasets (master branch), and do `pip install -e .`. Then, `import datasets` causes the error below. ``` ~/workspace/Clone/datasets/src/datasets/utils/file_utils.py in <module> 116 sys.path.append(str(HF_MODULES_CACHE)) 117 --> 118 os.makedirs(HF_MODULES_CACHE, exist_ok=True) 119 if not os.path.exists(os.path.join(HF_MODULES_CACHE, "__init__.py")): 120 with open(os.path.join(HF_MODULES_CACHE, "__init__.py"), "w"): ~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/os.py in makedirs(name, mode, exist_ok) 221 return 222 try: --> 223 mkdir(name, mode) 224 except OSError: 225 # Cannot rely on checking for EEXIST, since the operating system FileNotFoundError: [Errno 2] No such file or directory: '<MY_HOME_DIRECTORY>/.cache/huggingface/modules' ``` The error occurs in `os.makedirs` in `file_utils.py`, even though `exist_ok = True` option is set. (I use Python 3.8, so `exist_ok` is expected to work.) I've checked some environment variables, and they are set as below. ``` *** NameError: name 'HF_MODULES_CACHE' is not defined *** NameError: name 'hf_cache_home' is not defined *** NameError: name 'XDG_CACHE_HOME' is not defined ``` Should I set some environment variables before using this library? And, do you have any idea why "No such file or directory" occurs even though the `exist_ok = True` option is set? Thank you in advance.

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MDU6SXNzdWU3NDUzMjIzNTc=

Unable to download cnn_dailymail dataset

closed

NONE

### Script to reproduce the error ``` from datasets import load_dataset train_dataset = load_dataset("cnn_dailymail", "3.0.0", split= 'train[:10%') valid_dataset = load_dataset("cnn_dailymail","3.0.0", split="validation[:5%]") ``` ### Error ``` --------------------------------------------------------------------------- NotADirectoryError Traceback (most recent call last) <ipython-input-8-47c39c228935> in <module>() 1 from datasets import load_dataset 2 ----> 3 train_dataset = load_dataset("cnn_dailymail", "3.0.0", split= 'train[:10%') 4 valid_dataset = load_dataset("cnn_dailymail","3.0.0", split="validation[:5%]") 5 frames /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 609 download_config=download_config, 610 download_mode=download_mode, --> 611 ignore_verifications=ignore_verifications, 612 ) 613 /usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 469 if not downloaded_from_gcs: 470 self._download_and_prepare( --> 471 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 472 ) 473 # Sync info /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 524 split_dict = SplitDict(dataset_name=self.name) 525 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 526 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 527 528 # Checksums verification /root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _split_generators(self, dl_manager) 252 def _split_generators(self, dl_manager): 253 dl_paths = dl_manager.download_and_extract(_DL_URLS) --> 254 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN) 255 # Generate shared vocabulary 256 /root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _subset_filenames(dl_paths, split) 153 else: 154 logging.fatal("Unsupported split: %s", split) --> 155 cnn = _find_files(dl_paths, "cnn", urls) 156 dm = _find_files(dl_paths, "dm", urls) 157 return cnn + dm /root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict) 132 else: 133 logging.fatal("Unsupported publisher: %s", publisher) --> 134 files = sorted(os.listdir(top_dir)) 135 136 ret_files = [] NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' ``` Thanks for any suggestions.

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MDExOlB1bGxSZXF1ZXN0NTIyNTk0Mjg1

Add clear_cache parameter in the test command

closed

MEMBER

For certain datasets like OSCAR #348 there are lots of different configurations and each one of them can take a lot of disk space. I added a `--clear_cache` flag to the `datasets-cli test` command to be able to clear the cache after each configuration test to avoid filling up the disk. It should enable an easier generation for the `dataset_infos.json` file for OSCAR.

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MDExOlB1bGxSZXF1ZXN0NTIyNTUzMzY1

Update head requests

closed

MEMBER

Get requests and Head requests didn't have the same parameters.

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MDU6SXNzdWU3NDQ3NTM0NTg=

Possible Bug: Small training/dataset file creates gigantic output

closed

NONE

Hey guys, I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely. I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug? I've used the following CMD: `python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`

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MDU6SXNzdWU3NDQ3NTA2OTE=

wmt16 cs-en does not donwload

closed

CONTRIBUTOR

Hi I am trying with wmt16, cs-en pair, thanks for the help, perhaps similar to the ro-en issue. thanks split="train", n_obs=data_args.n_train) for task in data_args.task} File "finetune_t5_trainer.py", line 109, in <dictcomp> split="train", n_obs=data_args.n_train) for task in data_args.task} File "/home/rabeeh/internship/seq2seq/tasks/tasks.py", line 82, in get_dataset dataset = load_dataset("wmt16", self.pair, split=split) File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/rabeeh/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz

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MDExOlB1bGxSZXF1ZXN0NTIxNzI4MDM4

Integrate file_lock inside the lib for better logging control

closed

MEMBER

Previously the locking system of the lib was based on the file_lock package. However as noticed in #812 there were too many logs printed even when the datasets logging was set to warnings or errors. For example ```python import logging logging.basicConfig(level=logging.INFO) import datasets datasets.set_verbosity_warning() datasets.load_dataset("squad") ``` would still log the file lock events: ``` INFO:filelock:Lock 5737989232 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/44801f118d500eff6114bfc56ab4e6def941f1eb14b70ac1ecc052e15cdac49d.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py.lock INFO:filelock:Lock 5737989232 released on /Users/quentinlhoest/.cache/huggingface/datasets/44801f118d500eff6114bfc56ab4e6def941f1eb14b70ac1ecc052e15cdac49d.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py.lock INFO:filelock:Lock 4393489968 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock INFO:filelock:Lock 4393489968 released on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock INFO:filelock:Lock 4393490808 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock Reusing dataset squad (/Users/quentinlhoest/.cache/huggingface/datasets/squad/plain_text/1.0.0/1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41) INFO:filelock:Lock 4393490808 released on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock ``` With the integration of file_lock in the library, the ouput is much cleaner: ``` Reusing dataset squad (/Users/quentinlhoest/.cache/huggingface/datasets/squad/plain_text/1.0.0/1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41) ``` Since the file_lock package is only a 450 lines file I think it's fine to have it inside the lib. Fix #812

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MDExOlB1bGxSZXF1ZXN0NTIxNzE3ODQ4

Add SemEval-2010 task 8

closed

CONTRIBUTOR

Hi, I don't know how to add dummy data, since I create the validation set out of the last 1000 examples of the train set. If you have a suggestion, I am happy to implement it. Cheers, Joel

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MDExOlB1bGxSZXF1ZXN0NTIxNjg0ODIx

Use pandas reader in csv

closed

MEMBER

The pyarrow CSV reader has issues that the pandas one doesn't (see #836 ). To fix that I switched to the pandas csv reader. The new reader is compatible with all the pandas parameters to read csv files. Moreover it reads csv by chunk in order to save RAM, while the pyarrow one loads everything in memory. Fix #836 Fix #794 Breaking: now all the parameters to read to csv file can be used in the `load_dataset` kwargs when loading csv, and the previous pyarrow objects `pyarrow.csv.ReadOptions`, `pyarrow.csv.ParseOptions` and `pyarrow.csv.ConvertOptions` are not used anymore.

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MDExOlB1bGxSZXF1ZXN0NTIxNjMzNTYz

Add open book corpus

closed

CONTRIBUTOR

Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed. It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves. @lhoestq and others please review this PR thoroughly. cc @shawwn

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