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https://github.com/huggingface/datasets/pull/4844

PR_kwDODunzps49IFLa

Add 'val' to VALIDATION_KEYWORDS.

closed

CONTRIBUTOR

This PR fixes #4839 by adding the word `"val"` to the `VALIDATION_KEYWORDS` so that the `load_dataset()` method with `imagefolder` (and probably, some other directives as well) reads folders named `"val"` as well. I think the supported keywords have to be mentioned in the documentation as well, but I couldn't think of a proper place to add that.

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PR_kwDODunzps49HaWT

Fix typo in streaming docs

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https://github.com/huggingface/datasets/pull/4842

PR_kwDODunzps49G8CC

Update stackexchange license

closed

CONTRIBUTOR

The correct license of the stackexchange subset of the Pile is `cc-by-sa-4.0`, as can for example be seen here: https://stackoverflow.com/help/licensing

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https://github.com/huggingface/datasets/pull/4841

PR_kwDODunzps49Gf0I

Update ted_talks_iwslt license to include ND

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CONTRIBUTOR

Excerpt from the paper's abstract: "Aside from its cultural and social relevance, this content, which is published under the Creative Commons BY-NC-ND license, also represents a precious language resource for the machine translation research community"

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I_kwDODunzps5PtjrQ

Dataset Viewer issue for darragh/demo_data_raw3

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CONTRIBUTOR

### Link https://huggingface.co./datasets/darragh/demo_data_raw3 ### Description ``` Exception: ValueError Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent. ``` reported by @NielsRogge ### Owner No

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I_kwDODunzps5PtCZp

ImageFolder dataset builder does not read the validation data set if it is named as "val"

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CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Currently, the `'imagefolder'` data set builder in [`load_dataset()`](https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/load.py#L1541] ) only [supports](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) the following names as the validation data set directory name: `["validation", "valid", "dev"]`. When the validation directory is named as `'val'`, the Data set will not have a validation split. I expected this to be a trivial task but ended up spending a lot of time before knowing that only the above names are supported. Here's a minimal example of `val` not being recognized: ```python import os import numpy as np import cv2 from datasets import load_dataset # creating a dummy data set with the following structure: # ROOT # | -- train # | ---- class_1 # | ---- class_2 # | -- val # | ---- class_1 # | ---- class_2 ROOT = "data" for which in ["train", "val"]: for class_name in ["class_1", "class_2"]: dir_name = os.path.join(ROOT, which, class_name) if not os.path.exists(dir_name): os.makedirs(dir_name) for i in range(10): cv2.imwrite( os.path.join(dir_name, f"{i}.png"), np.random.random((224, 224)) ) # trying to create a data set dataset = load_dataset( "imagefolder", data_dir=ROOT ) >> dataset DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 20 }) }) # ^ note how the dataset only has a 'train' subset ``` **Describe the solution you'd like** The suggestion is to include `"val"` to [that list ](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) as that's a commonly used phrase to name the validation directory. Also, In the documentation, explicitly mention that only such directory names are supported as train/val/test directories to avoid confusion. **Describe alternatives you've considered** In the documentation, explicitly mention that only such directory names are supported as train/val/test directories without adding `val` to the above list. **Additional context** A question asked in the forum: [ Loading an imagenet-style image dataset with train/val directories](https://discuss.huggingface.co/t/loading-an-imagenet-style-image-dataset-with-train-val-directories/21554)

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PR_kwDODunzps49F08R

Fix documentation card of adv_glue dataset

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Fix documentation card of adv_glue dataset.

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PR_kwDODunzps49Fb6l

Add support for CSV metadata files to ImageFolder

closed

CONTRIBUTOR

Fix #4814

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I_kwDODunzps5Psghw

Is it possible to pass multiple links to a split in load script?

open

NONE

**Is your feature request related to a problem? Please describe.** I wanted to use a python loading script in hugging face datasets that use different sources of text (it's somehow a compilation of multiple datasets + my own dataset) based on how `load_dataset` [works](https://huggingface.co./docs/datasets/loading) I assumed I could do something like bellow in my loading script: ```python ... _URL = "MY_DATASET_URL/resolve/main/data/" _URLS = { "train": [ "FIRST_URL_TO.txt", _URL + "train-00000-of-00001-676bfebbc8742592.parquet" ] } ... ``` but when loading the dataset it raises the following error: ```python File ~/.local/lib/python3.8/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs ... 668 if isinstance(a, str): 669 # Force-cast str subclasses to str (issue #21127) 670 parts.append(str(a)) TypeError: expected str, bytes or os.PathLike object, not list ``` **Describe the solution you'd like** I believe since it's possible for `load_dataset` to get list of URLs instead of just a URL for `train` split it can be possible here too. **Describe alternatives you've considered** An alternative solution would be to download all needed datasets locally and `push_to_hub` them all, but since the datasets I'm talking about are huge it's not among my options. **Additional context** I think loading `text` beside the `parquet` is completely a different issue but I believe I can figure it out by proposing a config for my dataset to load each entry of `_URLS['train']` separately either by `load_dataset("text", ...` or `load_dataset("parquet", ...`.

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PR_kwDODunzps49FJg9

Fix documentation card of ethos dataset

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Fix documentation card of ethos dataset.

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PR_kwDODunzps49FJOu

Fix documentation card of recipe_nlg dataset

closed

MEMBER

Fix documentation card of recipe_nlg dataset

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PR_kwDODunzps49E_Nk

Fix missing tags in dataset cards

closed

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Fix missing tags in dataset cards: - boolq - break_data - definite_pronoun_resolution - emo - kor_nli - pg19 - quartz - sciq - squad_es - wmt14 - wmt15 - wmt16 - wmt17 - wmt18 - wmt19 - wmt_t2t This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.

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PR_kwDODunzps49EQav

Fix tags in dataset cards

closed

MEMBER

Fix wrong tags in dataset cards.

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https://api.github.com/repos/huggingface/datasets/issues/4831

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PR_kwDODunzps49Cibf

Add oversampling strategies to interleave datasets

closed

CONTRIBUTOR

Hello everyone, Here is a proposal to improve `interleave_datasets` function. Following Issue #3064, and @lhoestq [comment](https://github.com/huggingface/datasets/issues/3064#issuecomment-1022333385), I propose here a code that performs oversampling when interleaving a `Dataset` list. I have myself encountered this problem while trying to implement training on a multilingual dataset following a training strategy similar to that of [XLSUM paper](https://arxiv.org/pdf/2106.13822.pdf), a multilingual abstract summary dataset where the multilingual training dataset is created by sampling from the languages following a smoothing strategy. The main idea is to sample languages that have a low number of samples more frequently than other languages. As in Issue #3064, the current default strategy is a undersampling strategy, which stops as soon as a dataset runs out of samples. The new `all_exhausted` strategy stops building the new dataset as soon as all samples in each dataset have been added at least once. How does it work in practice: - if ``probabilities`` is `None` and the strategy is `all_exhausted`, it simply performs a round robin interleaving that stops when the longest dataset is out of samples. Here the new dataset length will be $maxLengthDataset*nbDataset$. - if ``probabilities`` is not `None` and the strategy is `all_exhausted`, it keeps trace of the datasets which were out of samples but continues to add them to the new dataset, and stops as soons as every dataset runs out of samples at least once. - In the other cases, it is supposed to keep the same behaviour as before. Except that this time, when probabilities are precised, it really stops AS SOON AS a dataset is out of samples. More on the last sentence: The previous example of `interleave_datasets` was: >>> from datasets import Dataset, interleave_datasets >>> d1 = Dataset.from_dict({"a": [0, 1, 2]}) >>> d2 = Dataset.from_dict({"a": [10, 11, 12]}) >>> d3 = Dataset.from_dict({"a": [20, 21, 22]}) >>> dataset = interleave_datasets([d1, d2, d3]) >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42) >>> dataset["a"] [10, 0, 11, 1, 2, 20, 12] With my implementation, `dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)` gives: >>> dataset["a"] [10, 0, 11, 1, 2] because `d1` is already out of samples just after `2` is added. Example of the results of applying the different strategies: >>> from datasets import Dataset, interleave_datasets >>> d1 = Dataset.from_dict({"a": [0, 1, 2]}) >>> d2 = Dataset.from_dict({"a": [10, 11, 12]}) >>> d3 = Dataset.from_dict({"a": [20, 21, 22]}) >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted") >>> dataset["a"] [10, 0, 11, 1, 2, 20, 12, 10, 0, 1, 2, 21, 0, 11, 1, 2, 0, 1, 12, 2, 10, 0, 22] >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42) >>> dataset["a"] [10, 0, 11, 1, 2] >>> dataset = interleave_datasets([d1, d2, d3]) >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> d1 = Dataset.from_dict({"a": [0, 1, 2]}) >>> d2 = Dataset.from_dict({"a": [10, 11, 12, 13]}) >>> d3 = Dataset.from_dict({"a": [20, 21, 22, 23, 24]}) >>> dataset = interleave_datasets([d1, d2, d3]) >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") >>> dataset["a"] [0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24] >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42) >>> dataset["a"] [10, 0, 11, 1, 2] >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted") >>> dataset["a"] [10, 0, 11, 1, 2, 20, 12, 13, ..., 0, 1, 2, 0, 24] **Final note:** I've been using that code for a research project involving a large-scale multilingual dataset. One should be careful when using oversampling to avoid to avoid exploding the size of the dataset. For example, if a very large data set has a low probability of being sampled, the final dataset may be several times the size of that large data set.

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PR_kwDODunzps49Cdro

Fix task tags in dataset cards

closed

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https://github.com/huggingface/datasets/issues/4829

I_kwDODunzps5Posfk

Misalignment between card tag validation and docs

open

MEMBER

## Describe the bug As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284 the validation of the dataset card tags is not aligned with its documentation: e.g. - implementation: `license: List[str]` - docs: `license: Union[str, List[str]]` They should be aligned. CC: @julien-c

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PR_kwDODunzps49B_vb

Support PIL Image objects in `add_item`/`add_column`

open

CONTRIBUTOR

Fix #4796 PS: We should also improve the type inference in `OptimizedTypeSequence` to make it possible to also infer the complex types (only `Image` currently) in nested arrays (e.g. `[[pil_image], [pil_image, pil_image]]` or `[{"img": pil_image}`]), but I plan to address this in a separate PR.

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PR_kwDODunzps49B1zi

Add license metadata to pg19

closed

MEMBER

As reported over email by Roy Rijkers

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PR_kwDODunzps49B0V3

Fix language tags in dataset cards

closed

MEMBER

Fix language tags in all dataset cards, so that they are validated (aligned with our `languages.json` resource).

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https://github.com/huggingface/datasets/pull/4825

PR_kwDODunzps49BYWL

[Windows] Fix Access Denied when using os.rename()

closed

CONTRIBUTOR

In this PR, we are including an additional step when `os.rename()` raises a PermissionError. Basically, we will use `shutil.move()` on the temp files. Fix #2937

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https://github.com/huggingface/datasets/pull/4824

PR_kwDODunzps49BR5H

Fix titles in dataset cards

closed

MEMBER

Fix all the titles in the dataset cards, so that they conform to the required format.

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PR_kwDODunzps49A0O_

Update data URL in mkqa dataset

closed

MEMBER

Update data URL in mkqa dataset. Fix #4817.

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I_kwDODunzps5PnMnY

Moving dataset between namespaces breaks dataset viewer

open

CONTRIBUTOR

## Describe the bug I moved a dataset from my own namespace to an org and that broke the dataset viewer. To fix it I had to manually edit the `dataset_info.json` file and change the first key in the json from `username--datasetname` to `orgname--datasetname` ## Steps to reproduce the bug What I did was: 1- Upload a dataset to my own namespace using `push_to_hub` 2- Move the dataset from my namespace to an org using the web interface. ## Expected results For the file to be changed accordingly. ## Actual results Broken dataset viewer. ## Environment info - `datasets` version: 2.3.3.dev0 - Platform: Linux-4.15.0-189-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.5 - PyArrow version: 7.0.0 - Pandas version: 1.3.5

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PR_kwDODunzps49AvaE

Fix train_test_split docs

closed

CONTRIBUTOR

I saw that `stratify` is added to the `train_test_split` method as per #4322, hence the docs can be updated.

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I_kwDODunzps5PlEVM

Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.

closed

NONE

Hi, when i try to run prepare_dataset function in [fine tuning ASR tutorial 4](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) , i got this error. I got this error Terminating: fork() called from a process already using GNU OpenMP, this is unsafe. There is no other logs available, so i have no clue what is the cause of it. ``` def prepare_dataset(batch): audio = batch["path"] # batched output is "un-batched" batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] batch["input_length"] = len(batch["input_values"]) with processor.as_target_processor(): batch["labels"] = processor(batch["text"]).input_ids return batch data = data.map(prepare_dataset, remove_columns=data.column_names["train"], num_proc=4) ``` Specify the actual results or traceback. There is no traceback except `Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-43-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps48-xc6

Add missing language tags to resources

closed

MEMBER

Add missing language tags to resources, required by existing datasets on GitHub.

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PR_kwDODunzps48-W7a

Add add cc-by-sa-2.5 license tag

closed

CONTRIBUTOR

- [ ] add it to moon-landing - [ ] add it to hub-docs

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I_kwDODunzps5Pi_SD

Outdated Link for mkqa Dataset

closed

NONE

## Describe the bug The URL used to download the mkqa dataset is outdated. It seems the URL to download the dataset is currently https://github.com/apple/ml-mkqa/blob/main/dataset/mkqa.jsonl.gz instead of https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz (master branch has been renamed to main). ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mkqa") ``` ## Expected results downloads the dataset ## Actual results ```python Downloading builder script: 4.79k/? [00:00<00:00, 201kB/s] Downloading metadata: 13.2k/? [00:00<00:00, 504kB/s] Downloading and preparing dataset mkqa/mkqa (download: 11.35 MiB, generated: 34.29 MiB, post-processed: Unknown size, total: 45.65 MiB) to /home/lhr/.cache/huggingface/datasets/mkqa/mkqa/1.0.0/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d... Downloading data files: 0% 0/1 [00:00<?, ?it/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Input In [3], in <cell line: 3>() 1 from datasets import load_dataset ----> 3 dataset = load_dataset("mkqa") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/load.py:1746, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1745 # Download and prepare data -> 1746 builder_instance.download_and_prepare( 1747 download_config=download_config, 1748 download_mode=download_mode, 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, 1751 use_auth_token=use_auth_token, 1752 ) 1754 # Build dataset for splits 1755 keep_in_memory = ( 1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1757 ) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info 708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos) 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/mkqa/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d/mkqa.py:130, in Mkqa._split_generators(self, dl_manager) 128 # download and extract URLs 129 urls_to_download = _URLS --> 130 downloaded_files = dl_manager.download_and_extract(urls_to_download) 132 return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})] File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls) 415 def download_and_extract(self, url_or_urls): 416 """Download and extract given url_or_urls. 417 418 Is roughly equivalent to: (...) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:309, in DownloadManager.download(self, url_or_urls) 306 download_func = partial(self._download, download_config=download_config) 308 start_time = datetime.now() --> 309 downloaded_path_or_paths = map_nested( 310 download_func, 311 url_or_urls, 312 map_tuple=True, 313 num_proc=download_config.num_proc, 314 disable_tqdm=not is_progress_bar_enabled(), 315 desc="Downloading data files", 316 ) 317 duration = datetime.now() - start_time 318 logger.info(f"Downloading took {duration.total_seconds() // 60} min") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:393, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: --> 393 mapped = [ 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:394, in <listcomp>(.0) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: 393 mapped = [ --> 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:330, in _single_map_nested(args) 328 # Singleton first to spare some computation 329 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 330 return function(data_struct) 332 # Reduce logging to keep things readable in multiprocessing with tqdm 333 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:335, in DownloadManager._download(self, url_or_filename, download_config) 332 if is_relative_path(url_or_filename): 333 # append the relative path to the base_path 334 url_or_filename = url_or_path_join(self._base_path, url_or_filename) --> 335 return cached_path(url_or_filename, download_config=download_config) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:185, in cached_path(url_or_filename, download_config, **download_kwargs) 181 url_or_filename = str(url_or_filename) 183 if is_remote_url(url_or_filename): 184 # URL, so get it from the cache (downloading if necessary) --> 185 output_path = get_from_cache( 186 url_or_filename, 187 cache_dir=cache_dir, 188 force_download=download_config.force_download, 189 proxies=download_config.proxies, 190 resume_download=download_config.resume_download, 191 user_agent=download_config.user_agent, 192 local_files_only=download_config.local_files_only, 193 use_etag=download_config.use_etag, 194 max_retries=download_config.max_retries, 195 use_auth_token=download_config.use_auth_token, 196 ignore_url_params=download_config.ignore_url_params, 197 download_desc=download_config.download_desc, 198 ) 199 elif os.path.exists(url_or_filename): 200 # File, and it exists. 201 output_path = url_or_filename File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:530, 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, ignore_url_params, download_desc) 525 raise FileNotFoundError( 526 f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been" 527 " disabled. To enable file online look-ups, set 'local_files_only' to False." 528 ) 529 elif response is not None and response.status_code == 404: --> 530 raise FileNotFoundError(f"Couldn't find file at {url}") 531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 532 if head_error is not None: FileNotFoundError: Couldn't find file at https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps487kpq

Update version of opus_paracrawl dataset

closed

MEMBER

This PR updates OPUS ParaCrawl from 7.1 to 9 version. Fix #4815.

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I_kwDODunzps5PhGtf

Outdated loading script for OPUS ParaCrawl dataset

closed

MEMBER

## Describe the bug Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.

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I_kwDODunzps5PeWbG

Support CSV as metadata file format in AudioFolder/ImageFolder

closed

CONTRIBUTOR

Requested here: https://discuss.huggingface.co/t/how-to-structure-an-image-dataset-repo-using-the-image-folder-approach/21004. CSV is also used in AutoTrain for specifying metadata in image datasets.

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PR_kwDODunzps48446r

Fix loading example in opus dataset cards

closed

MEMBER

This PR: - fixes the examples to load the datasets, with the corrected dataset name, in their dataset cards for: - opus_dgt - opus_paracrawl - opus_wikipedia - fixes their dataset cards with the missing required information: title, data instances/fields/splits - enumerates the supported languages - adds a missing citation reference for opus_wikipedia Related to: - #4806

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PR_kwDODunzps484Fzq

Fix bug in function validate_type for Python >= 3.9

closed

MEMBER

Fix `validate_type` function, so that it uses `get_origin` instead. This makes the function forward compatible. This fixes #4811 because: ```python In [4]: typing.Optional[str] Out[4]: typing.Optional[str] In [5]: get_origin(typing.Optional[str]) Out[5]: typing.Union ``` Fix #4811.

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I_kwDODunzps5PdKDd

Bug in function validate_type for Python >= 3.9

closed

MEMBER

## Describe the bug The function `validate_type` assumes that the type `typing.Optional[str]` is automatically transformed to `typing.Union[str, NoneType]`. ```python In [4]: typing.Optional[str] Out[4]: typing.Union[str, NoneType] ``` However, this is not the case for Python 3.9: ```python In [3]: typing.Optional[str] Out[3]: typing.Optional[str] ```

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PR_kwDODunzps484C9l

Add description to hellaswag dataset

closed

MEMBER

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PR_kwDODunzps483Y4h

Complete the mlqa dataset card

closed

CONTRIBUTOR

I fixed the issue #4808 Details of PR: - Added languages included in the dataset. - Added task id and task category. - Updated the citation information. Fix #4808.

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I_kwDODunzps5PcYcZ

Add more information to the dataset card of mlqa dataset

closed

CONTRIBUTOR

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PR_kwDODunzps483MSH

document fix in opus_gnome dataset

closed

CONTRIBUTOR

I fixed a issue #4805. I changed `"gnome"` to `"opus_gnome"` in[ README.md](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary).

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PR_kwDODunzps482yiS

Fix opus_gnome dataset card

closed

CONTRIBUTOR

I fixed a issue #4805. I changed `"gnome"` to `"opus_gnome"` in[ README.md](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary). Fix #4805

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I_kwDODunzps5Pbq3b

Wrong example in opus_gnome dataset card

closed

CONTRIBUTOR

## Describe the bug I found that [the example on opus_gone dataset ](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary) doesn't work. ## Steps to reproduce the bug ```python load_dataset("gnome", lang1="it", lang2="pl") ``` `"gnome"` should be `"opus_gnome"` ## Expected results ```bash 100% 1/1 [00:00<00:00, 42.09it/s] DatasetDict({ train: Dataset({ features: ['id', 'translation'], num_rows: 8368 }) }) ``` ## Actual results ```bash Couldn't find 'gnome' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/gnome/gnome.py ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.27 - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5PblNW

streaming dataset with concatenating splits raises an error

open

NONE

## Describe the bug streaming dataset with concatenating splits raises an error ## Steps to reproduce the bug ```python from datasets import load_dataset # no error repo = "nateraw/ade20k-tiny" dataset = load_dataset(repo, split="train+validation") ``` ```python from datasets import load_dataset # error repo = "nateraw/ade20k-tiny" dataset = load_dataset(repo, split="train+validation", streaming=True) ``` ```sh --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>() 3 # error 4 repo = "nateraw/ade20k-tiny" ----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True) 1 frames [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path) 1030 splits_generator = splits_generators[split] 1031 else: -> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}") 1033 1034 # Create a dataset for each of the given splits ValueError: Bad split: train+validation. Available splits: ['validation', 'train'] ``` [Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing) ## Expected results load successfully or throws an error saying it is not supported. ## Actual results above ## Environment info - `datasets` version: 2.4.0 - Platform: Windows-10-10.0.22000-SP0 (windows11 x64) - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5PZevK

Support `pipeline` argument in inspect.py functions

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** The `wikipedia` dataset requires a `pipeline` argument to build the list of splits: https://huggingface.co./datasets/wikipedia/blob/main/wikipedia.py#L937 But this is currently not supported in `get_dataset_config_info`: https://github.com/huggingface/datasets/blob/main/src/datasets/inspect.py#L373-L375 which is called by other functions, e.g. `get_dataset_split_names`. **Additional context** The dataset viewer is not working out-of-the-box on `wikipedia` for this reason: https://huggingface.co./datasets/wikipedia/viewer <img width="637" alt="Capture d’écran 2022-08-08 à 12 01 16" src="https://user-images.githubusercontent.com/1676121/183461838-5330783b-0269-4ba7-a999-314cde2023d8.png">

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I_kwDODunzps5PX8YT

`with_format` behavior is inconsistent on different datasets

open

CONTRIBUTOR

## Describe the bug I found a case where `with_format` does not transform the dataset to the requested format. ## Steps to reproduce the bug Run: ```python from transformers import AutoTokenizer, AutoFeatureExtractor from datasets import load_dataset raw = load_dataset("glue", "sst2", split="train") raw = raw.select(range(100)) tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled") def preprocess_func(examples): return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True) data = raw.map(preprocess_func, batched=True) print(type(data[0]["input_ids"])) data = data.with_format("torch", columns=["input_ids"]) print(type(data[0]["input_ids"])) ``` printing as expected: ```python <class 'list'> <class 'torch.Tensor'> ``` Then run: ```python raw = load_dataset("beans", split="train") raw = raw.select(range(100)) preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans") def preprocess_func(examples): imgs = [img.convert("RGB") for img in examples["image"]] return preprocessor(imgs) data = raw.map(preprocess_func, batched=True) print(type(data[0]["pixel_values"])) data = data.with_format("torch", columns=["pixel_values"]) print(type(data[0]["pixel_values"])) ``` Printing, unexpectedly ```python <class 'list'> <class 'list'> ``` ## Expected results `with_format` should transform into the requested format; it's not the case. ## Actual results `type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979 - Platform: Linux - Python version: 3.9.12 - PyArrow version: 7.0.0

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PR_kwDODunzps48yTYu

Fix fine classes in trec dataset

closed

MEMBER

This PR: - replaces the fine labels, so that there are 50 instead of 47 - once more labels are added, all they (fine and coarse) have been re-ordered, so that they align with the order in: https://cogcomp.seas.upenn.edu/Data/QA/QC/definition.html - the feature names have been fixed: `fine_label` instead of `label-fine` - to sneak-case (underscores instead of hyphens) - words have been reordered Fix #4790.

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PR_kwDODunzps48yIss

support LargeListArray in pyarrow

open

CONTRIBUTOR

```python import numpy as np import datasets a = np.zeros((5000000, 768)) res = datasets.Dataset.from_dict({'embedding': a}) ''' File '/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/arrow_writer.py', line 178, in __arrow_array__ out = numpy_to_pyarrow_listarray(data) File "/home/wenjiaxin/anaconda3/envs/data/lib/python3.8/site-packages/datasets/features/features.py", line 1173, in numpy_to_pyarrow_listarray offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32()) File "pyarrow/array.pxi", line 312, in pyarrow.lib.array File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Integer value 2147483904 not in range: -2147483648 to 2147483647 ''' ``` Loading a large numpy array currently raises the error above as the type of offsets is `int32`. And pyarrow has supported [LargeListArray](https://arrow.apache.org/docs/python/generated/pyarrow.LargeListArray.html) for this case.

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I_kwDODunzps5PU8R-

video dataset loader/parser

closed

CONTRIBUTOR

you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co./docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co./docs/datasets/image_load#local-files)? could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem.

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PR_kwDODunzps48wVEG

Shard generator

closed

NONE

Hi everyone! I was using Hugging Face datasets to process some very large datasets and found that it would be quite handy to have a feature that will allow to "split" these large datasets into chunks with equal size. Even better - be able to run through these chunks one by one in simple and convenient way. So I decided to add the method called shard_generator() to the main Dataset class. It works similar to shard method but it returns a generator of datasets with equal size (defined by shard_size attribute). Example: ```python >>> from datasets import load_dataset >>> ds = load_dataset("rotten_tomatoes", split="validation") >>> ds Dataset({ features: ['text', 'label'], num_rows: 1066 }) >>> next(ds.shard_generator(300)) Dataset({ features: ['text', 'label'], num_rows: 300 }) ``` I hope it can be helpful to someone. Thanks!

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PR_kwDODunzps48uL-t

Torgo dataset creation

closed

NONE

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I_kwDODunzps5PRHpC

ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset

open

CONTRIBUTOR

## Describe the bug When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature. ## Steps to reproduce the bug ```python from datasets import load_dataset from PIL import Image dataset = load_dataset("hf-internal-testing/example-documents") # load any random Pillow image image = Image.open("/content/cord_example.png").convert("RGB") new_image = {'image': image} dataset['test'] = dataset['test'].add_item(new_image) ``` ## Expected results The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`: ``` import datasets feature = datasets.Image(decode=False) new_image = {'image': feature.encode_example(image)} dataset['test'] = dataset['test'].add_item(new_image) ``` ## Actual results ``` ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type ```

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I_kwDODunzps5PPvPk

Missing MBPP splits

closed

NONE

(@albertvillanova) The [MBPP dataset on the Hub](https://huggingface.co./datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split: > In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**. If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected. The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples: > We specify a train and test split to use for evaluation. Specifically: > > * Task IDs 11-510 are used for evaluation. > * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples. I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974. Regarding the "sanitized" split the paper states the following: > For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning. The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split.

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I_kwDODunzps5PMLwJ

Add DocVQA

open

CONTRIBUTOR

## Adding a Dataset - **Name:** DocVQA - **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this information. - **Paper:** https://arxiv.org/abs/2007.00398 - **Data:** https://www.docvqa.org/datasets/docvqa - **Motivation:** Models like LayoutLM and Donut in the Transformers library are fine-tuned on DocVQA. Would be very handy to directly load this dataset from the hub. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).

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I_kwDODunzps5PMGK4

Dataset Viewer issue for Team-PIXEL/rendered-wikipedia-english

closed

NONE

### Link https://huggingface.co./datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train ### Description The dataset can be loaded fine but the viewer shows this error: ``` Server Error Status code: 400 Exception: Status400Error Message: The dataset does not exist. ``` I'm guessing this is because I recently renamed the dataset. Based on related issues (e.g. https://github.com/huggingface/datasets/issues/4759) , is there something server-side that needs to be refreshed? ### Owner Yes

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I_kwDODunzps5PMARY

Issue with fine classes in trec dataset

closed

MEMBER

## Describe the bug According to their paper, the TREC dataset contains 2 kinds of classes: - 6 coarse classes: TREC-6 - 50 fine classes: TREC-50 However, our implementation only has 47 (instead of 50) fine classes. The reason for this is that we only considered the last segment of the label, which is repeated for several coarse classes: - We have one `desc` fine label instead of 2: - `DESC:desc` - `HUM:desc` - We have one `other` fine label instead of 3: - `ENTY:other` - `LOC:other` - `NUM:other` From their paper: > We define a two-layered taxonomy, which represents a natural semantic classification for typical answers in the TREC task. The hierarchy contains 6 coarse classes and 50 fine classes, > Each coarse class contains a non-overlapping set of fine classes.

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PR_kwDODunzps48o3Kk

Update doc upload_dataset.mdx

closed

CONTRIBUTOR

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PR_kwDODunzps48oUNx

Fix NonMatchingChecksumError in mbpp dataset

closed

MEMBER

Fix issue reported on the Hub: https://huggingface.co./datasets/mbpp/discussions/1 Fix #4787.

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I_kwDODunzps5PK2TH

NonMatchingChecksumError in mbpp dataset

closed

MEMBER

## Describe the bug As reported on the Hub [Fix Checksum Mismatch](https://huggingface.co./datasets/mbpp/discussions/1), there is a `NonMatchingChecksumError` when loading mbpp dataset ## Steps to reproduce the bug ```python ds = load_dataset("mbpp", "full") ``` ## Expected results Loading of the dataset without any exception raised. ## Actual results ``` NonMatchingChecksumError Traceback (most recent call last) <ipython-input-1-a3fbdd3ed82e> in <module> ----> 1 ds = load_dataset("mbpp", "full") .../huggingface/datasets/src/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, revision, use_auth_token, task, streaming, **config_kwargs) 1791 1792 # Download and prepare data -> 1793 builder_instance.download_and_prepare( 1794 download_config=download_config, 1795 download_mode=download_mode, .../huggingface/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) .../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1225 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1228 1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: .../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: --> 775 verify_checksums( 776 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" 777 ) .../huggingface/datasets/src/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://raw.githubusercontent.com/google-research/google-research/master/mbpp/mbpp.jsonl'] ```

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I_kwDODunzps5PHZ0c

.save_to_disk('path', fs=s3) TypeError

closed

NONE

The following code: ```python import datasets train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"]) s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key) train_dataset.save_to_disk("s3://datasets/", fs=s3) ``` produces following traceback: ```shell File "C:\Users\Hong Knop\AppData\Local\Programs\Python\Python310\lib\site-packages\botocore\auth.py", line 374, in scope return '/'.join(scope) ``` I invoke print(scope) in <auth.py> (line 373) and find this: ```python [('4VA08VLL3VTKQJKCAI8M',), '20220803', 'us-east-1', 's3', 'aws4_request'] ```

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PR_kwDODunzps48k8y4

Require torchaudio<0.12.0 in docs

closed

MEMBER

This PR adds to docs the requirement of torchaudio<0.12.0 to avoid RuntimeError. Subsequent to PR: - #4777

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I_kwDODunzps5PDy-Q

Add Multiface dataset

open

MEMBER

## Adding a Dataset - **Name:** Multiface dataset - **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps - **Data:** https://github.com/facebookresearch/multiface The whole dataset is 65TB though, so I'm not sure Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).

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PR_kwDODunzps48iHey

Docs for creating a loading script for image datasets

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This PR is a first draft of creating a loading script for image datasets. Feel free to let me know if there are any specificities I'm missing for this. 🙂 To do: - [x] Document how to create different configurations.

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I_kwDODunzps5PDOz2

pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648

closed

NONE

## Describe the bug Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets. ## Steps to reproduce the bug ```python dataset_name = "the_pile" ds = load_dataset(dataset_name, split="train") ds = ds.map(preprocess, num_proc=num_workers) uniques = set(ds.unique("hash")) ``` Gists for minimum reproducible example: https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2 ## Expected results Chunking and writing out a deduplicated dataset. ## Actual results ``` return dataset._data.column(column).unique().to_pylist() File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 ```

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PR_kwDODunzps48hOie

Fix label renaming and add a battery of tests

closed

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This PR makes some changes to label renaming in `to_tf_dataset()`, both to fix some issues when users input something we weren't expecting, and also to make it easier to deprecate label renaming in future, if/when we want to move this special-casing logic to a function in `transformers`. The main changes are: - Label renaming now only happens when the `auto_rename_labels` argument is set. For backward compatibility, this defaults to `True` for now. - If the user requests "label" but the data collator renames that column to "labels", the label renaming logic will now handle that case correctly. - Added a battery of tests to make this more reliable in future. - Adds an optimization to loading in `to_tf_dataset()` for unshuffled datasets (uses slicing instead of a list of indices) Fixes #4772

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PR_kwDODunzps48g9oA

Remove apache_beam import from module level in natural_questions dataset

closed

MEMBER

Instead of importing `apache_beam` at the module level, import it in the method `_build_pcollection`. Fix #4779.

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I_kwDODunzps5PCRyp

Loading natural_questions requires apache_beam even with existing preprocessed data

closed

MEMBER

## Describe the bug When loading "natural_questions", the package "apache_beam" is required: ``` ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam. Please install it using 'pip install apache_beam' for instance' ``` This requirement is unnecessary, once there exists preprocessed data and the script just needs to download it. ## Steps to reproduce the bug ```python load_dataset("natural_questions", "dev", split="validation", revision="main") ``` ## Expected results No ImportError raised. ## Actual results ``` ImportError Traceback (most recent call last) [<ipython-input-3-c938e7c05d02>](https://localhost:8080/#) in <module>() ----> 1 from datasets import load_dataset; ds = load_dataset("natural_questions", "dev", split="validation", revision="main") [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1732 revision=revision, 1733 use_auth_token=use_auth_token, -> 1734 **config_kwargs, 1735 ) 1736 [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1504 download_mode=download_mode, 1505 data_dir=data_dir, -> 1506 data_files=data_files, 1507 ) 1508 [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1245 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" 1246 ) from None -> 1247 raise e1 from None 1248 else: 1249 raise FileNotFoundError( [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1180 download_config=download_config, 1181 download_mode=download_mode, -> 1182 dynamic_modules_path=dynamic_modules_path, 1183 ).get_module() 1184 elif path.count("/") == 1: # community dataset on the Hub [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 490 base_path=hf_github_url(path=self.name, name="", revision=revision), 491 imports=imports, --> 492 download_config=self.download_config, 493 ) 494 additional_files = [(config.DATASETDICT_INFOS_FILENAME, dataset_infos_path)] if dataset_infos_path else [] [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in _download_additional_modules(name, base_path, imports, download_config) 214 _them_str = "them" if len(needs_to_be_installed) > 1 else "it" 215 raise ImportError( --> 216 f"To be able to use {name}, you need to install the following {_depencencies_str}: " 217 f"{', '.join(needs_to_be_installed)}.\nPlease install {_them_str} using 'pip install " 218 f"{' '.join(needs_to_be_installed.values())}' for instance'" ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam. Please install it using 'pip install apache_beam' for instance' ``` ## Environment info Colab notebook.

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PR_kwDODunzps48dRPh

Update local loading script docs

closed

MEMBER

This PR clarifies the local loading script section to include how to load a dataset after you've modified the local loading script (closes #4732).

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PR_kwDODunzps48cByL

Require torchaudio<0.12.0 to avoid RuntimeError

closed

MEMBER

Related to: - https://github.com/huggingface/transformers/issues/18379 Fix partially #4776.

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I_kwDODunzps5O8iwk

RuntimeError when using torchaudio 0.12.0 to load MP3 audio file

closed

MEMBER

Current version of `torchaudio` (0.12.0) raises a RuntimeError when trying to use `sox_io` backend but non-Python dependency `sox` is not installed: https://github.com/pytorch/audio/blob/2e1388401c434011e9f044b40bc8374f2ddfc414/torchaudio/backend/sox_io_backend.py#L21-L29 ```python def _fail_load( filepath: str, frame_offset: int = 0, num_frames: int = -1, normalize: bool = True, channels_first: bool = True, format: Optional[str] = None, ) -> Tuple[torch.Tensor, int]: raise RuntimeError("Failed to load audio from {}".format(filepath)) ``` Maybe we should raise a more actionable error message so that the user knows how to fix it. UPDATE: - this is an incompatibility of latest torchaudio (0.12.0) and the sox backend TODO: - [x] as a temporary solution, we should recommend installing torchaudio<0.12.0 - #4777 - #4785 - [ ] however, a stable solution must be found for torchaudio>=0.12.0 Related to: - https://github.com/huggingface/transformers/issues/18379

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I_kwDODunzps5O7Lgm

Streaming not supported in Theivaprakasham/wildreceipt

closed

NONE

### Link _No response_ ### Description _No response_ ### Owner _No response_

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I_kwDODunzps5O4Rzk

Training hangs at the end of epoch, with set_transform/with_transform+multiple workers

open

NONE

## Describe the bug I use load_dataset() (I tried with [wiki](https://huggingface.co./datasets/wikipedia) and my own json data) and use set_transform/with_transform for preprocessing. But it hangs at the end of the 1st epoch if dataloader_num_workers>=1. No problem with single worker. ## Steps to reproduce the bug ```python train_dataset = datasets.load_dataset("wikipedia", "20220301.en", split='train', cache_dir=model_args.cache_dir, streaming=False) train_dataset.set_transform(psg_parse_fn) train_dataloader = DataLoader( train_dataset, batch_size=args.train_batch_size, sampler=DistributedSampler(train_dataset), collate_fn=data_collator, drop_last=args.dataloader_drop_last, num_workers=args.dataloader_num_workers, ) ``` ## Expected results ## Actual results It simply hangs. The ending step is num_example/batch_size (one epoch). ## Environment info - `datasets` version: 2.4.1.dev0 - Platform: Linux-5.4.170+-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.1

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PR_kwDODunzps48WNV3

Document loading from relative path

closed

MEMBER

This PR describes loading a dataset from the Hub by specifying a relative path in `data_dir` or `data_files` in `load_dataset` (see #4757).

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I_kwDODunzps5O1rID

AssertionError when using label_cols in to_tf_dataset

closed

NONE

## Describe the bug An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`. The assertion is in this line: https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475 ## Steps to reproduce the bug ```python from datasets import load_dataset from transformers import DefaultDataCollator dataset = load_dataset('glue', 'mrpc', split='train') tf_dataset = dataset.to_tf_dataset( columns=["sentence1", "sentence2", "idx"], label_cols=["label"], batch_size=16, collate_fn=DefaultDataCollator(return_tensors="tf"), ) ``` ## Expected results No assertion error. ## Actual results ``` AssertionError: in user code: File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels * assert set(features.keys()).union(labels.keys()) == set(input_batch.keys()) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps48VjWx