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MDExOlB1bGxSZXF1ZXN0NjgxODg0OTgy

Add C4

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The old code for the C4 dataset was to generate the C4 with Apache Beam, as in Tensorflow Datasets. However AllenAI is now hosting the processed C4 dataset in this repo: https://huggingface.co/datasets/allenai/c4 Thanks a lot to them for their amazing work ! In this PR I changed the script to download and prepare the data directly from this repo. It has 4 variants: en, en.noblocklist, en.noclean, realnewslike You can load it with ```python from datasets import load_dataset c4 = load_dataset("c4", "en") ``` It also supports streaming, if you don't want to download hundreds of GB of data: ```python c4 = load_dataset("c4", "en", streaming=True) ``` Regarding the dataset_infos.json, I haven't added the infos for en.noclean. I will add them once I have them. Also we can work on the dataset card at https://huggingface.co/datasets/c4 For now I just added a link to https://huggingface.co/datasets/allenai/c4 as well as a few sections

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MDExOlB1bGxSZXF1ZXN0NjgxNjczMzYy

Add streaming in load a dataset docs

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Mention dataset streaming on the "loading a dataset" page of the documentation

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MDU6SXNzdWU5MzQ1ODQ3NDU=

Finding right block-size with JSON loading difficult for user

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As reported by @thomwolf, while loading a JSON Lines file with "json" loading script, he gets > json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)

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MDU6SXNzdWU5MzQ1NzM3Njc=

Support Zstandard compressed files

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Add support for Zstandard compressed files: https://facebook.github.io/zstd/

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MDExOlB1bGxSZXF1ZXN0NjgwOTQ2NzQ1

Filter expected warning log from transformers

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Close #2569.

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MDExOlB1bGxSZXF1ZXN0NjgwNjEzNzc0

Minor fix docs format for bertscore

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Minor fix docs format for bertscore: - link to README - format of KWARGS_DESCRIPTION

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MDU6SXNzdWU5MzMwMTU3OTc=

Weights of model checkpoint not initialized for RobertaModel for Bertscore

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NONE

When applying bertscore out of the box, ```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']``` Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html ``` from datasets import load_metric metric = load_metric('bertscore') # Example of typical usage for batch in dataset: inputs, references = batch predictions = model(inputs) metric.add_batch(predictions=predictions, references=references) score = metric.compute(lang="en") #score = metric.compute(model_type="roberta-large") # gives the same error ``` I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo.... ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27 - Python version: 3.9.5 - PyArrow version: 3.0.0

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MDExOlB1bGxSZXF1ZXN0NjgwMjE5MDU2

Add interleave_datasets for map-style datasets

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### Add interleave_datasets for map-style datasets Add support for map-style datasets (i.e. `Dataset` objects) in `interleave_datasets`. It was only supporting iterable datasets (i.e. `IterableDataset` objects). ### Implementation details It works by concatenating the datasets and then re-order the indices to make the new dataset. ### TODO - [x] tests - [x] docs Close #2563

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MDExOlB1bGxSZXF1ZXN0NjgwMjE3OTY3

Add ASR task and new languages to resources

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This PR adds a new `automatic-speech-recognition` task to the list of supported tasks in `tasks.json` and also includes a few new languages missing from `common_voice`. Note: I used the [Papers with Code list](https://www.paperswithcode.com/area/speech/speech-recognition) as inspiration for the ASR subtasks

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MDExOlB1bGxSZXF1ZXN0NjgwMTA2NzM0

fix Dataset.map when num_procs > num rows

closed

CONTRIBUTOR

closes #2470 ## Testing notes To run updated tests: ```sh pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s ``` With Python code (to view warning): ```python from datasets import Dataset dataset = Dataset.from_dict({"x": ["sample"]}) print(len(dataset)) dataset.map(lambda x: x, num_proc=10) ```

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MDExOlB1bGxSZXF1ZXN0Njc5Nzg3NTI4

Inject templates for ASR datasets

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This PR adds ASR templates for 5 of the most common speech datasets on the Hub, where "common" is defined by the number of models trained on them. I also fixed a bunch of the tags in the READMEs 😎

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MDU6SXNzdWU5MzIzODk2Mzk=

concatenate_datasets for iterable datasets

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Currently `concatenate_datasets` only works for map-style `Dataset`. It would be nice to have it work for `IterableDataset` objects as well. It would simply chain the iterables of the iterable datasets.

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MDU6SXNzdWU5MzIzODc2Mzk=

interleave_datasets for map-style datasets

closed

MEMBER

Currently the `interleave_datasets` functions only works for `IterableDataset`. Let's make it work for map-style `Dataset` objects as well. It would work the same way: either alternate between the datasets in order or randomly given probabilities specified by the user.

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MDExOlB1bGxSZXF1ZXN0Njc5NjkyMjQ2

Minor fix in loading metrics docs

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Make some minor fixes in "Loading metrics" docs.

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MDU6SXNzdWU5MzIzMjE3MjU=

Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`

open

NONE

## Describe the bug If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets. ## Steps to reproduce the bug - Create a local dataset builder class - load the local builder class file using `load_dataset` and let the cache build - update the file's content - The cache should rebuilt. ## Expected results With `ignore_verifications=True`, `load_dataset` should pick up existing cache. ## Actual results Creates new cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.7 - PyArrow version: 3.0.0

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MDExOlB1bGxSZXF1ZXN0Njc5NTMyODk4

fix Dataset.map when num_procs > num rows

closed

CONTRIBUTOR

closes #2470 ## Testing notes To run updated tests: ```sh pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s ``` With Python code (to view warning): ```python from datasets import Dataset dataset = Dataset.from_dict({"x": ["sample"]}) print(len(dataset)) dataset.map(lambda x: x, num_proc=10) ```

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MDU6SXNzdWU5MzE4NDk3MjQ=

Memory usage consistently increases when processing a dataset with `.map`

open

NONE

## Describe the bug I have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help. ## Steps to reproduce the bug Providing code as it is would be hard. I can provide a MVP if that helps. ## Expected results Memory usage should become consistent after some time following the launch of processing. ## Actual results Memory usage keeps on increasing. ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.7 - PyArrow version: 3.0.0

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MDExOlB1bGxSZXF1ZXN0Njc5MTg0Njk1

Update: WebNLG - update checksums

closed

MEMBER

The master branch changed so I computed the new checksums. I also pinned a specific revision so that it doesn't happen again in the future. Fix https://github.com/huggingface/datasets/issues/2553

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MDExOlB1bGxSZXF1ZXN0Njc5MDk4ODg3

Fix `fever` keys

closed

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The keys has duplicates since they were reset to 0 after each file. I fixed it by taking into account the file index as well.

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MDU6SXNzdWU5MzE1OTU4NzI=

Better DuplicateKeysError error to help the user debug the issue

open

MEMBER

As mentioned in https://github.com/huggingface/datasets/issues/2552 it would be nice to improve the error message when a dataset fails to build because there are duplicate example keys. The current one is ```python datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 48 Keys should be unique and deterministic in nature ``` and we could have something that guides the user to debugging the issue: ```python DuplicateKeysError: both 42th and 1337th examples have the same keys `48`. Please fix the dataset script at <path/to/the/dataset/script> ```

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MDExOlB1bGxSZXF1ZXN0Njc5MDU4ODM3

Fix code_search_net keys

closed

MEMBER

There were duplicate keys in the `code_search_net` dataset, as reported in https://github.com/huggingface/datasets/issues/2552 I fixed the keys (it was an addition of the file and row indices, which was causing collisions) Fix #2552.

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MDU6SXNzdWU5MzE0NTM4NTU=

Multilabel metrics not supported

closed

NONE

When I try to use a metric like F1 macro I get the following error: ``` TypeError: int() argument must be a string, a bytes-like object or a number, not 'list' ``` There is an explicit casting here: https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/src/datasets/features.py#L274 And looks like this is because here https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/metrics/f1/f1.py#L88 the features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work: ```python class F1(datasets.Metric): def _info(self): return datasets.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features( { "predictions": datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")), } ), reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"], ) def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None): return { "f1": f1_score( references, predictions, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight, ), } ```

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MDU6SXNzdWU5MzEzNjU5MjY=

load_dataset("web_nlg") NonMatchingChecksumError

closed

NONE

Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('web_nlg', name="release_v3.0_en", split="dev") ``` Gives ``` NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip'] ``` ## Environment info - `datasets` version: 1.8.0 - Platform: macOS-11.3.1-x86_64-i386-64bit - Python version: 3.9.4 - PyArrow version: 3.0.0 Also tested on Linux, with python 3.6.8

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MDU6SXNzdWU5MzEzNTQ2ODc=

Keys should be unique error on code_search_net

closed

MEMBER

## Describe the bug Loading `code_search_net` seems not possible at the moment. ## Steps to reproduce the bug ```python >>> load_dataset('code_search_net') Downloading: 8.50kB [00:00, 3.09MB/s] Downloading: 19.1kB [00:00, 10.1MB/s] No config specified, defaulting to: code_search_net/all Downloading and preparing dataset code_search_net/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to /Users/thomwolf/.cache/huggingface/datasets/code_search_net/all/1.0.0/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a... Traceback (most recent call last): File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/builder.py", line 1067, in _prepare_split writer.write(example, key) File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 343, in write self.check_duplicate_keys() File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 354, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 48 Keys should be unique and deterministic in nature ``` ## Environment info - `datasets` version: 1.8.1.dev0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 2.0.0

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MDExOlB1bGxSZXF1ZXN0Njc4NTQzMjg1

Fix FileSystems documentation

closed

CONTRIBUTOR

### What this fixes: This PR resolves several issues I discovered in the documentation on the `datasets.filesystems` module ([this page](https://huggingface.co/docs/datasets/filesystems.html)). ### What were the issues? When I originally tried implementing the code examples I faced several bugs attributed to: - out of date [botocore](https://github.com/boto/botocore) call signatures - capitalization errors in the `S3FileSystem` class name (written as `S3Filesystem` in one place) - call signature errors for the `S3FileSystem` class constructor (uses parameter `sessions` instead of `session` in some places) (see [`s3fs`](https://s3fs.readthedocs.io/en/latest/api.html#s3fs.core.S3FileSystem) for where this constructor signature is defined) ### Testing/reviewing notes Instructions for generating the documentation locally: [here](https://github.com/huggingface/datasets/tree/master/docs#generating-the-documentation).

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MDU6SXNzdWU5MzA5NTEyODc=

Allow for incremental cumulative metric updates in a distributed setup

closed

CONTRIBUTOR

Currently, using a metric allows for one of the following: - Per example/batch metrics - Cumulative metrics over the whole data What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation. Since most metrics are just an average of per-example metrics (which aren't?), an efficient calculation can be done as follows: `((score_cumulative * n_cumulative) + (score_new * n_new)) / (n_cumulative+ n_new)` where `n` and `score` refer to number of examples and metric score, `cumulative` refers to the cumulative metric and `new` refers to the addition of new examples. If you don't want to add this capability in the library, a simple solution exists so users can do it themselves: It is easy to implement for a single process setup, but in a distributed one there is no way to get the correct `n_new`. The solution for this is to return the number of examples that was used to compute the metrics in `.compute()` by adding the following line here: https://github.com/huggingface/datasets/blob/5a3221785311d0ce86c2785b765e86bd6997d516/src/datasets/metric.py#L402-L403 ``` output["number_of_examples"] = len(predictions) ``` and also remove the log message here so it won't spam: https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/src/datasets/metric.py#L411 If this change is ok with you, I'll open a pull request.

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MDU6SXNzdWU5Mjk4MTkwOTM=

Handling unlabeled datasets

closed

NONE

Hi! Is there a way for datasets to produce unlabeled instances (e.g., the `ClassLabel` can be nullable). For example, I want to use the MNLI dataset reader ( https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi_nli.py ) on a file that doesn't have the `gold_label` field. I tried setting `"label": data.get("gold_label")`, but got the following error: ``` File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset use_auth_token=use_auth_token, File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 989, in _prepare_split example = self.info.features.encode_example(record) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 953, in encode_example return encode_nested_example(self, example) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in encode_nested_example k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in <dictcomp> k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 875, in encode_nested_example return schema.encode_example(obj) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 653, in encode_example if not -1 <= example_data < self.num_classes: TypeError: '<=' not supported between instances of 'int' and 'NoneType' ``` What's the proper way to handle reading unlabeled datasets, especially for downstream usage with Transformers?

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MDU6SXNzdWU5MjkyMzI4MzE=

Field order issue in loading json

closed

NONE

## Describe the bug The `load_dataset` function expects columns in alphabetical order when loading json files. Similar bug was previously reported for csv in #623 and fixed in #684. ## Steps to reproduce the bug For a json file `j.json`, ``` {"c":321, "a": 1, "b": 2} ``` Running the following, ``` f= datasets.Features({'a': Value('int32'), 'b': Value('int32'), 'c': Value('int32')}) json_data = datasets.load_dataset('json', data_files='j.json', features=f) ``` ## Expected results A successful load. ## Actual results ``` File "pyarrow/table.pxi", line 1409, in pyarrow.lib.Table.cast ValueError: Target schema's field names are not matching the table's field names: ['c', 'a', 'b'], ['a', 'b', 'c'] ``` ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyArrow version: 3.0.0

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MDU6SXNzdWU5MjkxOTIzMjk=

Dataset load_from_disk is too slow

open

NONE

@lhoestq ## Describe the bug It's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example). ## Steps to reproduce the bug Just get the oscar in spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset. ## Expected results I expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Ubuntu 18 - Python version: 3.8 I've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model.

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MDExOlB1bGxSZXF1ZXN0Njc2OTk2MjQ0

Add license to the Cambridge English Write & Improve + LOCNESS dataset card

closed

MEMBER

As noticed in https://github.com/huggingface/datasets/pull/2539, the licensing information was missing for this dataset. I added it and I also filled a few other empty sections.

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MDExOlB1bGxSZXF1ZXN0Njc2OTMxOTYw

Fix DuplicatedKeysError in drop dataset

closed

MEMBER

Close #2542. cc: @VictorSanh.

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MDExOlB1bGxSZXF1ZXN0Njc2ODM1MjYz

Fix logging levels

closed

MEMBER

Sometimes default `datasets` logging can be too verbose. One approach could be reducing some logging levels, from info to debug, or from warning to info. Close #2543. cc: @stas00

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MDU6SXNzdWU5Mjg1NzE5MTU=

switching some low-level log.info's to log.debug?

closed

CONTRIBUTOR

In https://github.com/huggingface/transformers/pull/12276 we are now changing the examples to have `datasets` on the same log level as `transformers`, so that one setting can do a consistent logging across all involved components. The trouble is that now we get a ton of these: ``` 06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 acquired on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock 06/23/2021 12:15:31 - INFO - datasets.arrow_writer - Done writing 50 examples in 12280 bytes /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow. 06/23/2021 12:15:31 - INFO - datasets.arrow_dataset - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns. 06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 released on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock ``` May I suggest that these can be `log.debug` as it's no informative to the user. More examples: these are not informative - too much information: ``` 06/23/2021 12:14:26 - INFO - datasets.load - Checking /home/stas/.cache/huggingface/datasets/downloads/459933f1fe47711fad2f6ff8110014ff189120b45ad159ef5b8e90ea43a174fa.e23e7d1259a8c6274a82a42a8936dd1b87225302c6dc9b7261beb3bc2daac640.py for additional imports. 06/23/2021 12:14:27 - INFO - datasets.builder - Constructing Dataset for split train, validation, test, from /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a ``` While these are: ``` 06/23/2021 12:14:27 - INFO - datasets.info - Loading Dataset Infos from /home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt16/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a 06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a) ``` I also realize that `transformers` examples don't have do use `info` for `datasets` to let the default `warning` keep logging to less noisy. But I think currently the log levels are slightly misused and skewed by 1 level. Many `warnings` will better be `info`s and most `info`s be `debug`. e.g.: ``` 06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a) ``` why is this a warning? it is informing me that the cache is used, there is nothing to be worried about. I'd have it as `info`. Warnings are typically something that's bordering error or the first thing to check when things don't work as expected. infrequent info is there to inform of the different stages or important events. Everything else is debug. At least the way I understand things.

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MDU6SXNzdWU5Mjg1NDAzODI=

`datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa/adversarialQA`

closed

MEMBER

## Describe the bug Failure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("drop") load_dataset("adversarial_qa", "adversarialQA") ``` ## Expected results The examples keys should be unique. ## Actual results ```bash >>> load_dataset("drop") Using custom data configuration default Downloading and preparing dataset drop/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to /home/hf/.cache/huggingface/datasets/drop/default/0.1.0/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset use_auth_token=use_auth_token, File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 992, in _prepare_split num_examples, num_bytes = writer.finalize() File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 409, in finalize self.check_duplicate_keys() File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 28553293-d719-441b-8f00-ce3dc6df5398 Keys should be unique and deterministic in nature ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.7.0 - Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.10 - PyArrow version: 3.0.0

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MDExOlB1bGxSZXF1ZXN0Njc2NTIwNDgx

update discofuse link cc @ekQ

closed

MEMBER

Updating the discofuse link: https://github.com/google-research-datasets/discofuse/commit/fd4b120cb3dd19a417e7f3b5432010b574b5eeee

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MDExOlB1bGxSZXF1ZXN0Njc2NDM5NTM1

Remove task templates if required features are removed during `Dataset.map`

closed

MEMBER

This PR fixes a bug reported by @craffel where removing a dataset's columns during `Dataset.map` triggered a `KeyError` because the `TextClassification` template tried to access the removed columns during `DatasetInfo.__post_init__`: ```python from datasets import load_dataset # `yelp_polarity` comes with a `TextClassification` template ds = load_dataset("yelp_polarity", split="test") ds # Dataset({ # features: ['text', 'label'], # num_rows: 38000 # }) # Triggers KeyError: 'label' - oh noes! ds.map(lambda x: {"inputs": 0}, remove_columns=ds.column_names) ``` I wrote a unit test to make sure I could reproduce the error and then patched a fix.

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MDExOlB1bGxSZXF1ZXN0Njc2MDI5MDY5

remove wi_locness dataset due to licensing issues

closed

CONTRIBUTOR

It was brought to my attention that this dataset's license is not only missing, but also prohibits redistribution. I contacted the original author to apologize for this oversight and asked if we could still use it, but unfortunately we can't and the author kindly asked to take down this dataset.

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MDU6SXNzdWU5Mjc5NDA2OTE=

Loading partial dataset when debugging

open

NONE

I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits). Every time I execute load_dataset for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues. Is there a way to only load part of the dataset on load_dataset? This would really speed up my workflow. Something like a debug mode would really help. Thanks!

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MDExOlB1bGxSZXF1ZXN0Njc1NjI1OTY3

Add Parquet loader + from_parquet and to_parquet

closed

MEMBER

Continuation of #2247 I added a "parquet" dataset builder, as well as the methods `Dataset.from_parquet` and `Dataset.to_parquet`. As usual, the data are converted to arrow in a batched way to avoid loading everything in memory.

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MDU6SXNzdWU5MjczMzg2Mzk=

Use `Audio` features for `AutomaticSpeechRecognition` task template

open

MEMBER

In #2533 we added a task template for speech recognition that relies on the file paths to the audio files. As pointed out by @SBrandeis this is brittle as it doesn't port easily across different OS'. The solution is to use dedicated `Audio` features when casting the dataset. These features are not yet available in `datasets`, but should be included in the `AutomaticSpeechRecognition` template once they are.

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MDExOlB1bGxSZXF1ZXN0Njc1NTA3MTAw

Improve Features docs

closed

MEMBER

- Fix rendering and cross-references in Features docs - Add docstrings to Features methods

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MDExOlB1bGxSZXF1ZXN0Njc1MzkzODg0

Sync with transformers disabling NOTSET

closed

MEMBER

Close #2528.

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MDExOlB1bGxSZXF1ZXN0Njc1Mzg2OTMw

Add task template for automatic speech recognition

closed

MEMBER

This PR adds a task template for automatic speech recognition. In this task, the input is a path to an audio file which the model consumes to produce a transcription. Usage: ```python from datasets import load_dataset from datasets.tasks import AutomaticSpeechRecognition ds = load_dataset("timit_asr", split="train[:10]") # Dataset({ # features: ['file', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'], # num_rows: 10 # }) task = AutomaticSpeechRecognition(audio_file_column="file", transcription_column="text") ds.prepare_for_task(task) # Dataset({ # features: ['audio_file', 'transcription'], # num_rows: 10 # }) ```

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MDU6SXNzdWU5MjcwNjMxOTY=

Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task

closed

CONTRIBUTOR

[This colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https://huggingface.co/transformers/custom_datasets.html#tok-ner). The pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https://en.wikipedia.org/wiki/Kana_ligature) break the alignment of `return_offsets_mapping`:  Without the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https://colab.research.google.com/drive/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing) It is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('ヿ')` return 'コト'. One workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`. I guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this. p.s. **I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https://github.com/huggingface/datasets/pull/2466)** `get_dataset `is just a simple wrapping for `load_dataset` and the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained("xlm-roberta-large")`

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MDExOlB1bGxSZXF1ZXN0Njc1MjM2MDYz

Fix dev version

closed

MEMBER

The dev version that ends in `.dev0` should be greater than the current version. However it happens that `1.8.0 > 1.8.0.dev0` for example. Therefore we need to use `1.8.1.dev0` for example in this case. I updated the dev version to use `1.8.1.dev0`, and I also added a comment in the setup.py in the release steps about this.

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MDExOlB1bGxSZXF1ZXN0Njc1MjMyNDk0

Fixed label parsing in the ProductReviews dataset

closed

CONTRIBUTOR

Fixed issue with parsing dataset labels.

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MDExOlB1bGxSZXF1ZXN0Njc0NjkxNjA5

Add summarization template

closed

MEMBER

This PR adds a task template for text summarization. As far as I can tell, we do not need to distinguish between "extractive" or "abstractive" summarization - both can be handled with this template. Usage: ```python from datasets import load_dataset from datasets.tasks import Summarization ds = load_dataset("xsum", split="train") # Dataset({ # features: ['document', 'summary', 'id'], # num_rows: 204045 # }) summarization = Summarization(text_column="document", summary_column="summary") ds.prepare_for_task(summarization) # Dataset({ # features: ['text', 'summary'], # num_rows: 204045 # }) ```

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MDU6SXNzdWU5MjYzMTQ2NTY=

Logging cannot be set to NOTSET similar to transformers

closed

NONE

## Describe the bug In the transformers library you can set the verbosity level to logging.NOTSET to work around the usage of tqdm and IPywidgets, however in Datasets this is no longer possible. This is because transformers set the verbosity level of tqdm with [this](https://github.com/huggingface/transformers/blob/b53bc55ba9bb10d5ee279eab51a2f0acc5af2a6b/src/transformers/file_utils.py#L1449) `disable=bool(logging.get_verbosity() == logging.NOTSET)` and datasets accomplishes this like [so](https://github.com/huggingface/datasets/blob/83554e410e1ab8c6f705cfbb2df7953638ad3ac1/src/datasets/utils/file_utils.py#L493) `not_verbose = bool(logger.getEffectiveLevel() > WARNING)` ## Steps to reproduce the bug ```python import datasets import logging datasets.logging.get_verbosity = lambda : logging.NOTSET datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy") ``` ## Expected results The code should download and load the dataset as normal without displaying progress bars ## Actual results ```ImportError Traceback (most recent call last) <ipython-input-4-aec65c0509c6> in <module> ----> 1 datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy") ~/venv/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs) 713 dataset=True, 714 return_resolved_file_path=True, --> 715 use_auth_token=use_auth_token, 716 ) 717 # Set the base path for downloads as the parent of the script location ~/venv/lib/python3.7/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs) 350 file_path = hf_bucket_url(path, filename=name, dataset=False) 351 try: --> 352 local_path = cached_path(file_path, download_config=download_config) 353 except FileNotFoundError: 354 raise FileNotFoundError( ~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 289 use_etag=download_config.use_etag, 290 max_retries=download_config.max_retries, --> 291 use_auth_token=download_config.use_auth_token, 292 ) 293 elif os.path.exists(url_or_filename): ~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) 668 headers=headers, 669 cookies=cookies, --> 670 max_retries=max_retries, 671 ) 672 ~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries) 493 initial=resume_size, 494 desc="Downloading", --> 495 disable=not_verbose, 496 ) 497 for chunk in response.iter_content(chunk_size=1024): ~/venv/lib/python3.7/site-packages/tqdm/notebook.py in __init__(self, *args, **kwargs) 217 total = self.total * unit_scale if self.total else self.total 218 self.container = self.status_printer( --> 219 self.fp, total, self.desc, self.ncols) 220 self.sp = self.display 221 ~/venv/lib/python3.7/site-packages/tqdm/notebook.py in status_printer(_, total, desc, ncols) 95 if IProgress is None: # #187 #451 #558 #872 96 raise ImportError( ---> 97 "IProgress not found. Please update jupyter and ipywidgets." 98 " See https://ipywidgets.readthedocs.io/en/stable" 99 "/user_install.html") ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Linux-5.4.95-42.163.amzn2.x86_64-x86_64-with-debian-10.8 - Python version: 3.7.10 - PyArrow version: 3.0.0 I am running this code on Deepnote and which important to this issue **does not** support IPywidgets

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MDExOlB1bGxSZXF1ZXN0Njc0MzkzNjQ5

Replace bad `n>1M` size tag

closed

MEMBER

Some datasets were still using the old `n>1M` tag which has been replaced with tags `1M<n<10M`, etc. This resulted in unexpected results when searching for datasets bigger than 1M on the hub, since it was only showing the ones with the tag `n>1M`.

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MDU6SXNzdWU5MjU5MjkyMjg=

Add COCO datasets

open

NONE

## Adding a Dataset - **Name:** COCO - **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset. - **Paper + website:** https://cocodataset.org/#home - **Data:** https://cocodataset.org/#download - **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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MDExOlB1bGxSZXF1ZXN0Njc0Mjc5MTgy

Use scikit-learn package rather than sklearn in setup.py

closed

CONTRIBUTOR

The sklearn package is an historical thing and should probably not be used by anyone, see https://github.com/scikit-learn/scikit-learn/issues/8215#issuecomment-344679114 for some caveats. Note: this affects only TESTS_REQUIRE so I guess only developers not end users.

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MDExOlB1bGxSZXF1ZXN0Njc0MDQzNzk1

Raise FileNotFoundError in WindowsFileLock

closed

CONTRIBUTOR

Closes #2443

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MDU6SXNzdWU5MjU0MjEwMDg=

Fr

closed

NONE

__Originally posted by @lewtun in https://github.com/huggingface/datasets/pull/2469__

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MDU6SXNzdWU5MjUzMzQzNzk=

Documentation Mistakes in Dataset: emotion

open

NONE

As per documentation, Dataset: emotion Homepage: https://github.com/dair-ai/emotion_dataset Dataset: https://github.com/huggingface/datasets/blob/master/datasets/emotion/emotion.py Permalink: https://huggingface.co/datasets/viewer/?dataset=emotion Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper. But when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust.

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MDExOlB1bGxSZXF1ZXN0NjczNTgxNzQ4

Insert text classification template for Emotion dataset

closed

MEMBER

This PR includes a template and updated `dataset_infos.json` for the `emotion` dataset.

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MDU6SXNzdWU5MjUwMTUwMDQ=

Datasets with tricky task templates

open

MEMBER

I'm collecting a list of datasets here that don't follow the "standard" taxonomy and require further investigation to implement task templates for. ## Text classification * [hatexplain](https://huggingface.co/datasets/hatexplain): ostensibly a form of text classification, but not in the standard `(text, target)` format and each sample appears to be tokenized. * [muchocine](https://huggingface.co/datasets/muchocine): contains two candidate text columns (long-form and summary) which in principle requires two `TextClassification` templates which is not currently supported

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MDExOlB1bGxSZXF1ZXN0NjczNDcyMzYy

Improve performance of pandas arrow extractor

closed

MEMBER

While reviewing PR #2505, I noticed that pandas arrow extractor could be refactored to be faster.

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MDExOlB1bGxSZXF1ZXN0NjczMjU5Nzg1

Add task templates for tydiqa and xquad

closed

MEMBER

This PR adds question-answering templates to the remaining datasets that are linked to a model on the Hub. Notes: * I could not test the tydiqa implementation since I don't have enough disk space 😢 . But I am confident the template works :) * there exist other datasets like `fquad` and `mlqa` which are candidates for question-answering templates, but some work is needed to handle the ordering of nested column described in #2434

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MDExOlB1bGxSZXF1ZXN0NjczMjUwODk1

Fix typo in MatthewsCorrelation class name

closed

MEMBER

Close #2513.

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MDU6SXNzdWU5MjQ1OTc0NzA=

datasets.map pickle issue resulting in invalid mapping function

open

NONE

I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` / `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts. The following reproduces the issue - most likely I'm missing something A simulated tokeniser which can be pickled ``` class CustomTokenizer: def __init__(self): self.state = "init" def __getstate__(self): print("__getstate__ called") out = self.__dict__.copy() self.state = "pickled" return out def __setstate__(self, d): print("__setstate__ called") self.__dict__ = d self.state = "restored" tokenizer = CustomTokenizer() ``` Test that it actually works - prints "__getstate__ called" and "__setstate__ called" ``` import pickle serialized = pickle.dumps(tokenizer) restored = pickle.loads(serialized) assert restored.state == "restored" ``` Simulate a function that tokenises examples, when dataset.map is called, this function ``` def tokenize_function(examples): assert tokenizer.state == "restored" # this shouldn't fail but it does output = tokenizer(examples) # this will fail as tokenizer isn't really a tokenizer return output ``` Use map to simulate tokenization ``` import glob from datasets import load_dataset assert tokenizer.state == "restored" train_files = glob.glob('train*.csv') validation_files = glob.glob('validation*.csv') datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files)) tokenized_datasets = datasets.map( tokenize_function, batched=True, ) ``` What's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ / __setstate__. I'm not sure if there's another hook I'm supposed to implement as well? --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-22-a2aef4f74aaa> in <module> 8 tokenized_datasets = datasets.map( 9 tokenize_function, ---> 10 batched=True, 11 ) ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc) 487 desc=desc, 488 ) --> 489 for k, dataset in self.items() 490 } 491 ) ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0) 487 desc=desc, 488 ) --> 489 for k, dataset in self.items() 490 } 491 ) ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 1633 fn_kwargs=fn_kwargs, 1634 new_fingerprint=new_fingerprint, -> 1635 desc=desc, 1636 ) 1637 else: ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 184 } 185 # apply actual function --> 186 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 187 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 188 # re-apply format to the output ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 395 # Call actual function 396 --> 397 out = func(self, *args, **kwargs) 398 399 # Update fingerprint of in-place transforms + update in-place history of transforms ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc) 1961 indices, 1962 check_same_num_examples=len(input_dataset.list_indexes()) > 0, -> 1963 offset=offset, 1964 ) 1965 except NumExamplesMismatch: ~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 1853 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset 1854 processed_inputs = ( -> 1855 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) 1856 ) 1857 if update_data is None: <ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples) 1 def tokenize_function(examples): ----> 2 assert tokenizer.state == "restored" 3 tokenizer(examples) 4 return examples

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MDExOlB1bGxSZXF1ZXN0NjczMDc3NTIx

CRD3 dataset card

closed

CONTRIBUTOR

This PR adds additional information to the CRD3 dataset card.

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MDU6SXNzdWU5MjQ0MTcxNzI=

Can datasets remove duplicated rows?

open

NONE

**Is your feature request related to a problem? Please describe.** i find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that.. **Describe the solution you'd like** have a functionality of " remove duplicated rows" **Describe alternatives you've considered** convert dataset to pandas, remove duplicate, and convert back... **Additional context** no

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MDU6SXNzdWU5MjQxNzQ0MTM=

Corelation should be Correlation

closed

NONE

https://github.com/huggingface/datasets/blob/0e87e1d053220e8ecddfa679bcd89a4c7bc5af62/metrics/matthews_correlation/matthews_correlation.py#L66

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MDU6SXNzdWU5MjQwNjkzNTM=

seqeval metric does not work with a recent version of sklearn: classification_report() got an unexpected keyword argument 'output_dict'

closed

NONE

## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric seqeval = load_metric("seqeval") seqeval.compute(predictions=[['A']], references=[['A']]) ``` ## Expected results The function computes a dict with metrics ## Actual results ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-39-69a57f5cf06f> in <module> 1 from datasets import load_dataset, load_metric 2 seqeval = load_metric("seqeval") ----> 3 seqeval.compute(predictions=[['A']], references=[['A']]) ~/p3/lib/python3.7/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/81eda1ff004361d4fa48754a446ec69bb7aa9cf4d14c7215f407d1475941c5ff/seqeval.py in _compute(self, predictions, references, suffix) 95 96 def _compute(self, predictions, references, suffix=False): ---> 97 report = classification_report(y_true=references, y_pred=predictions, suffix=suffix, output_dict=True) 98 report.pop("macro avg") 99 report.pop("weighted avg") TypeError: classification_report() got an unexpected keyword argument 'output_dict' ``` ## Environment info sklearn=0.24 datasets=1.1.3

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MDU6SXNzdWU5MjM3NjIxMzM=

Add C4

closed

MEMBER

## Adding a Dataset - **Name:** *C4* - **Description:** *https://github.com/allenai/allennlp/discussions/5056* - **Paper:** *https://arxiv.org/abs/1910.10683* - **Data:** *https://huggingface.co/datasets/allenai/c4* - **Motivation:** *Used a lot for pretraining* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Should fix https://github.com/huggingface/datasets/issues/1710

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MDExOlB1bGxSZXF1ZXN0NjcyNDY3MzY3

Add align_labels_with_mapping to DatasetDict

closed

MEMBER

https://github.com/huggingface/datasets/pull/2457 added the `Dataset.align_labels_with_mapping` method. In this PR I also added `DatasetDict.align_labels_with_mapping`

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MDExOlB1bGxSZXF1ZXN0NjcxNjcyMzU5

Fix fingerprint when moving cache dir

closed

MEMBER

The fingerprint of a dataset changes if the cache directory is moved. I fixed that by setting the fingerprint to be the hash of: - the relative cache dir (dataset_name/version/config_id) - the requested split Close #2496 I had to fix an issue with the filelock filename that was too long (>255). It prevented the tests to run on my machine. I just added `hash_filename_if_too_long` in case this happens, to not get filenames longer than 255. We usually have long filenames for filelocks because they are named after the path that is being locked. In case the path is a cache directory that has long directory names, then the filelock filename could en up being very long.

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MDU6SXNzdWU5MjE4NjMxNzM=

Load Image Classification Dataset from Local

open

NONE

**Is your feature request related to a problem? Please describe.** Yes - we would like to load an image classification dataset with datasets without having to write a custom data loader. **Describe the solution you'd like** Given a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like "cifar10". **Describe alternatives you've considered** Implement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path) Write custom data loader logic **Additional context** We're training ViT on custom dataset

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MDExOlB1bGxSZXF1ZXN0NjcwNDQ0MDgz

Rearrange JSON field names to match passed features schema field names

closed

MEMBER

This PR depends on PR #2453 (which must be merged first). Close #2366.

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MDExOlB1bGxSZXF1ZXN0NjcwNDM4NTgx

Add course banner

closed

MEMBER

This PR adds a course banner similar to the one you can now see in the [Transformers repo](https://github.com/huggingface/transformers) that links to the course. Let me know if placement seems right to you or not, I can move it just below the badges too.

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MDExOlB1bGxSZXF1ZXN0NjcwMjY2NjQy

Make numpy arrow extractor faster

closed

MEMBER

I changed the NumpyArrowExtractor to call directly to_numpy and see if it can lead to speed-ups as discussed in https://github.com/huggingface/datasets/issues/2498 This could make the numpy/torch/tf/jax formatting faster

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MDU6SXNzdWU5MjA2MzYxODY=

SubjQA wrong boolean values in entries

open

NONE

## Describe the bug SubjQA seems to have a boolean that's consistently wrong. It defines: - question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective). - is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective) However, `is_ques_subjective` seems to have wrong values in the entire dataset. For instance, in the example in the dataset card, we have: - "question_subj_level": 2 - "is_ques_subjective": false However, according to the description, the question should be subjective since the `question_subj_level` is below 4

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MDExOlB1bGxSZXF1ZXN0NjY5NzQ1MDA5

JAX integration

closed

MEMBER

Hi ! I just added the "jax" formatting, as we already have for pytorch, tensorflow, numpy (and also pandas and arrow). It does pretty much the same thing as the pytorch formatter except it creates jax.numpy.ndarray objects. ```python from datasets import Dataset d = Dataset.from_dict({"foo": [[0., 1., 2.]]}) d = d.with_format("jax") d[0] # {'foo': DeviceArray([0., 1., 2.], dtype=float32)} ``` A few details: - The default integer precision for jax depends on the jax configuration `jax_enable_x64` (see [here](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision)), I took that into account. Unless `jax_enable_x64` is specified, it is int32 by default - AFAIK it's not possible to do a full conversion from arrow data to jax data. We are doing arrow -> numpy -> jax but the numpy -> jax part doesn't do zero copy unfortutanely (see [here](https://github.com/google/jax/issues/4486)) - the env var for disabling JAX is `USE_JAX`. However I noticed that in `transformers` it is `USE_FLAX`. This is not an issue though IMO I also updated `convert_to_python_objects` to allow users to pass jax.numpy.ndarray objects to build a dataset. Since the `convert_to_python_objects` method became slow because it's the time when pytorch, tf (and now jax) are imported, I fixed it by checking the `sys.modules` to avoid unecessary import of pytorch, tf or jax. Close #2495

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