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https://api.github.com/repos/huggingface/datasets/issues/5892
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User access requests with manual review do not notify the dataset owner
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[ "cc @SBrandeis", "I think this has been addressed.\r\n\r\nPlease open a new issue if you are still not getting notified." ]
2023-05-23T17:27:46Z
2023-07-21T13:55:37Z
2023-07-21T13:55:36Z
CONTRIBUTOR
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### Describe the bug When a user access requests are enabled, and new requests are set to Manual Review, the dataset owner should be notified of the pending requests. However, instead, currently nothing happens, and so the dataset request can go unanswered for quite some time until the owner happens to check that particular dataset's Settings pane. ### Steps to reproduce the bug 1. Enable a dataset's user access requests 2. Set to Manual Review 3. Ask another HF user to request access to the dataset 4. Dataset owner is not notified ### Expected behavior The dataset owner should receive some kind of notification, perhaps in their HF site inbox, or by email, when a dataset access request is made and manual review is enabled. ### Environment info n/a
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QASC: incomplete training set
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[ "Hi @danyaljj, thanks for reporting.\r\n\r\nUnfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:\r\n```ipython\r\nIn [10]: ds[\"train\"]\r\nOut[10]:\r\nDataset({\r\n features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_quest...
2021-07-22T21:59:44Z
2021-07-23T13:30:07Z
2021-07-23T13:30:07Z
CONTRIBUTOR
null
null
null
## Describe the bug The training instances are not loaded properly. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("qasc", script_version='1.10.2') def load_instances(split): instances = dataset[split] print(f"split: {split} - size: {len(instances)}") for x in instances: print(json.dumps(x)) load_instances('test') load_instances('validation') load_instances('train') ``` ## results For test and validation, we can see the examples in the output (which is good!): ``` split: test - size: 920 {"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"} {"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"} {"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"} ... ``` However, only a few instances are loaded for the training split, which is not correct. ## Environment info - `datasets` version: '1.10.2' - Platform: MaxOS - Python version:3.7 - PyArrow version: 3.0.0
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"Couldn't cast array of type" in complex datasets
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[ "Thanks for reporting! \r\n\r\nSpecifying the target features explicitly should avoid this error:\r\n```python\r\ndataset = dataset.map(\r\n batch_process,\r\n batched=True,\r\n batch_size=1,\r\n num_proc=1,\r\n remove_columns=dataset.column_names,\r\n features=datasets.Features({\"texts\": datase...
2023-06-19T14:16:14Z
2023-07-26T15:13:53Z
2023-07-26T15:13:53Z
NONE
null
null
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### Describe the bug When doing a map of a dataset with complex types, sometimes `datasets` is unable to interpret the valid schema of a returned datasets.map() function. This often comes from conflicting types, like when both empty lists and filled lists are competing for the same field value. This is prone to happen in batch mapping, when the mapper returns a sequence of null/empty values and other batches are non-null. A workaround is to manually cast the new batch to a pyarrow table (like implemented in this [workaround](https://github.com/piercefreeman/lassen/pull/3)) but it feels like this ideally should be solved at the core library level. Note that the reproduction case only throws this error if the first datapoint has the empty list. If it is processed later, datasets already detects its representation as list-type and therefore allows the empty list to be provided. ### Steps to reproduce the bug A trivial reproduction case: ```python from typing import Iterator, Any import pandas as pd from datasets import Dataset def batch_to_examples(batch: dict[str, list[Any]]) -> Iterator[dict[str, Any]]: for i in range(next(iter(lengths))): yield {feature: values[i] for feature, values in batch.items()} def examples_to_batch(examples) -> dict[str, list[Any]]: batch = {} for example in examples: for feature, value in example.items(): if feature not in batch: batch[feature] = [] batch[feature].append(value) return batch def batch_process(examples, explicit_schema: bool): new_examples = [] for example in batch_to_examples(examples): new_examples.append(dict(texts=example["raw_text"].split())) return examples_to_batch(new_examples) df = pd.DataFrame( [ {"raw_text": ""}, {"raw_text": "This is a test"}, {"raw_text": "This is another test"}, ] ) dataset = Dataset.from_pandas(df) # datasets won't be able to typehint a dataset that starts with an empty example. with pytest.raises(TypeError, match="Couldn't cast array of type"): dataset = dataset.map( batch_process, batched=True, batch_size=1, num_proc=1, remove_columns=dataset.column_names, ) ``` This results in crashes like: ```bash File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 2109, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1998, in array_cast raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") TypeError: Couldn't cast array of type string to null ``` ### Expected behavior The code should successfully map and create a new dataset without error. ### Environment info Mac OSX, Linux
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I tried to load a custom dataset using the following statement: dataset = load_dataset('json', data_files=data_files). The dataset contains 50 million text-image pairs, but an error occurred.
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[ "Thanks for reporting, @cjt222.\r\n\r\nWhat is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead. ", "> Thanks for reporting, @cjt222.\r\n> \r\n> What is the structure of your JSON files. Please note that it is normally simpler if the data file...
2023-05-30T02:55:26Z
2023-07-24T12:00:38Z
2023-07-24T12:00:38Z
NONE
null
null
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### Describe the bug File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/builder.py", line 1858, in _prepare_split_single Downloading and preparing dataset json/default to /home/kas/diffusers/examples/dreambooth/cache_data/datasets/json/default-acf423d8c6ef99d0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data files: 100%|██████████| 1/1 [00:00<00:00, 84.35it/s] Extracting data files: 0%| | 0/1 [00:00<?, ?it/s] for _, table in generator: File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 114, in _generate_tables io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) File "pyarrow/_json.pyx", line 258, in pyarrow._json.read_json Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 27.72it/s] Generating train split: 0 examples [00:00, ? examples/s] File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 125, in pyarrow.lib.check_status pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2390448764 ### Steps to reproduce the bug 1、data_files = ["1.json", "2.json", "3.json"] 2、dataset = load_dataset('json', data_files=data_files) ### Expected behavior Read the dataset normally. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-4.15.0-29-generic-x86_64-with-debian-buster-sid - Python version: 3.7.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 1.3.5
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I_kwDODunzps5JYEmL
4,304
Language code search does direct matches
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[ "Thanks for reporting ! I forwarded the issue to the front-end team :)\r\n\r\nWill keep you posted !\r\n\r\nI also changed the tagging app to suggest two letters code for now." ]
2022-05-10T11:59:16Z
2022-05-10T12:38:42Z
null
CONTRIBUTOR
null
null
null
## Describe the bug Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search. ## Steps to reproduce the bug 1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL)) 2. Look for datasets using the full code 3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq)) Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`. One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :) ## Expected results Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`). ## Actual results The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches. ## Environment info (web app)
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Bump fsspec from 2021.11.1 to 2022.3.0
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-07-28T09:37:15Z
2023-07-28T10:16:11Z
2023-07-28T10:07:02Z
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Fix https://github.com/huggingface/datasets/issues/6087 (Colab installs 2023.6.0, so we should be good)
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docs: fix missing quotation
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2021-03-16T10:07:54Z
2021-03-17T09:21:57Z
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The json code misses a quote
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Fix the error of msr_sqa dataset
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2022-02-12T16:27:54Z
2022-02-13T11:21:05Z
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Fix the error of _load_table_data function in msr_sqa dataset, it is wrong to use comma to split each row.
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Minor fix in the docs
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2021-02-10T12:33:09Z
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Fix dataset_dict.shuffle with single seed
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2020-12-23T14:33:36Z
2021-01-04T10:00:04Z
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Fix #1610 I added support for single integer used in `DatasetDict.shuffle`. Previously only a dictionary of seed was allowed. Moreover I added the missing `seed` parameter. Previously only `seeds` was allowed.
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simpllify faiss index save
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2021-05-12T03:54:10Z
2021-05-17T13:41:41Z
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Fixes #2350 In some cases, Faiss GPU index objects do not have neither "device" nor "getDevice". Possibly this happens when some part of the index is computed on CPU. In particular, this would happen with the index `OPQ16_128,IVF512,PQ32` (issue #2350). I did check it, but it is likely that `OPQ` or `PQ` transforms cause it. I propose, instead of using the index object to get the device, to infer it form the `FaissIndex.device` field as it is done in `.add_vectors`. Here we assume that `.device` always corresponds to the index placement and it seems reasonable.
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`dataset = dataset.map()` causes faiss index lost
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[ "Hi ! Indeed `map` is dropping the index right now, because one can create a dataset with more or fewer rows using `map` (and therefore the index might not be relevant anymore)\r\n\r\nI guess we could check the resulting dataset length, and if the user hasn't changed the dataset size we could keep the index, what d...
2022-02-21T21:59:23Z
2022-06-27T14:56:29Z
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## Describe the bug assigning the resulted dataset to original dataset causes lost of the faiss index ## Steps to reproduce the bug `my_dataset` is a regular loaded dataset. It's a part of a customed dataset structure ```python self.dataset.add_faiss_index('embeddings') self.dataset.list_indexes() # ['embeddings'] dataset2 = my_dataset.map( lambda x: self._get_nearest_examples_batch(x['text']), batch=True ) # the unexpected result: dataset2.list_indexes() # [] self.dataset.list_indexes() # ['embeddings'] ``` in case something wrong with my `_get_nearest_examples_batch()`, it's like this ```python def _get_nearest_examples_batch(self, examples, k=5): queries = embed(examples) scores_batch, retrievals_batch = self.dataset.get_nearest_examples_batch(self.faiss_column, queries, k) return { 'neighbors': [batch['text'] for batch in retrievals_batch], 'scores': scores_batch } ``` ## Expected results `map` shouldn't drop the indexes, in another word, indexes should be carried to the generated dataset ## Actual results map drops the indexes ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 20.04.3 LTS - Python version: 3.8.12 - PyArrow version: 7.0.0
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Added dataset Makhzan
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[ "The only CI error comes from \r\n```\r\nFAILED tests/test_file_utils.py::TempSeedTest::test_tensorflow\r\n```\r\n\r\nwhich is not related to your PR and is fixed on master.\r\n\r\nYou can ignore it", "@lhoestq I've made the changes. Please review and merge. \r\n\r\nI have a similar PR https://github.com/huggingf...
2020-12-12T15:34:07Z
2020-12-16T15:04:52Z
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Need help with the dummy data.
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Pin sqlalchemy
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-01-27T11:26:38Z
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since sqlalchemy update to 2.0.0 the CI started to fail: https://github.com/huggingface/datasets/actions/runs/4023742457/jobs/6914976514 the error comes from pandas: https://github.com/pandas-dev/pandas/issues/51015
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[ "dupe of #1992" ]
2021-03-17T16:08:58Z
2021-03-18T09:10:23Z
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```python # benchmark_filter.py import logging import sys import time from datasets import load_dataset, set_caching_enabled if __name__ == "__main__": set_caching_enabled(False) logging.basicConfig(level=logging.DEBUG) bc = load_dataset("bookcorpus") now = time.time() try: bc["train"].filter(lambda x: len(x["text"]) < 64, num_proc=int(sys.argv[1])) except Exception as e: print(f"cancelled: {e}") elapsed = time.time() - now print(elapsed) ``` Running `python benchmark_filter.py 1` (20min+) is faster than `python benchmark_filter.py 2` (2hrs+)
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## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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[`to_json`] add multi-proc sharding support
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[ "Hi @stas00, \r\nI want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert pyarrow ...
2021-07-16T19:41:50Z
2021-09-13T13:56:37Z
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As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR. I implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards? I think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process. I'm not sure whether the user should be responsible to concatenate the shards at the end or `datasets`, either way works for my needs. The code I was using: ``` from multiprocessing import cpu_count, Process, Queue [...] filtered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count()) DATASET_NAME = "oscar" SHARDS = 10 def process_shard(idx): print(f"Sharding {idx}") ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True) # ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling print(f"Saving {DATASET_NAME}-{idx}.jsonl") ds_shard.to_json(f"{DATASET_NAME}-{idx}.jsonl", orient="records", lines=True, force_ascii=False) queue = Queue() processes = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)] for p in processes: p.start() for p in processes: p.join() ``` Thank you! @lhoestq
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Remove unused allowed_extensions param
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-08-10T10:09:54Z
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This PR removes unused `allowed_extensions` parameter from `create_builder_configs_from_metadata_configs`.
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[ "I see that @lhoestq overlooked this PR with his commit 07e2b05. 😢 \r\n\r\nI'm adding a script so that this issue does not happen again.\r\n", "For the moment, the script only includes `update_custom_js`, but in a follow-up PR I will include all the required steps to make a package release.", "I think we just ...
2021-06-11T07:26:03Z
2021-06-14T09:14:20Z
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Currently docs default version is 1.5.0. This PR fixes this and sets the latest version instead.
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Update Wikipedia dataset
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I'm starting to generate the pre-processed data for some of the languages (for backward compatibility).\r\n\r\nOnce this merged, we will create the pre-processed data on the Hub under the Wikimedia namespace.", "All steps have been...
2022-03-04T08:19:21Z
2022-03-21T12:35:23Z
2022-03-21T12:31:00Z
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This PR combines all updates to Wikipedia dataset. Once approved, this will be used to generate the pre-processed Wikipedia datasets. Finally, this PR will be able to be merged into master: - NOT using squash - BUT a regular MERGE (or REBASE+MERGE), so that all commits are preserved TODO: - [x] #3435 - [x] #3789 - [x] #3825 - [x] Run to get the pre-processed data for big languages (backward compatibility) - [x] #3958 CC: @geohci
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Fix `ClassLabel` min max check for `None` values
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-07-12T15:46:12Z
2023-07-12T16:29:26Z
2023-07-12T16:18:04Z
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Fix #6022
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Add desc parameter to filter
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2022-01-03T14:44:18Z
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Fix #3317
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Roman Urdu Stopwords List
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[ "Hi ! Thanks for sharing :)\r\nI think the best place to share this is probably the `Languages at Hugging Face` section of the forum:\r\nhttps://discuss.huggingface.co/c/languages-at-hugging-face/15\r\n\r\nSince this is not a dataset, I'm closing this PR if you don't mind", "Thank you I will look into the link th...
2021-05-13T18:29:27Z
2021-05-19T08:50:43Z
2021-05-17T14:05:10Z
NONE
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A list of most frequently used Roman Urdu words with different spellings and usages. This is a very basic effort to collect some basic stopwords for Roman Urdu to help efforts of analyzing text data in roman Urdu which makes up a huge part of daily internet interaction of Roman-Urdu users.
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5,822
Audio Dataset with_format torch problem
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[ "Hi ! Can you try with a more recent version of `datasets` ?", "Ok, yes it worked with the most recent version. Thanks" ]
2023-05-04T20:07:51Z
2023-05-11T20:45:53Z
2023-05-11T20:45:53Z
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### Describe the bug Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets ``` audio_dataset = \ (Dataset .from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()}) .cast_column("audio", Audio(sampling_rate=16_000)) .with_format('numpy')) audio_dataset[0]["audio"] ``` works, but ``` audio_dataset = \ (Dataset .from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()}) .cast_column("audio", Audio(sampling_rate=16_000)) .with_format('torch')) audio_dataset[0]["audio"] ``` does not instead I get ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[54], line 1 ----> 1 audio_dataset[0]["audio"] File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2154, in Dataset.__getitem__(self, key) 2152 def __getitem__(self, key): # noqa: F811 2153 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 2154 return self._getitem( 2155 key, 2156 ) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2139, in Dataset._getitem(self, key, decoded, **kwargs) 2137 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs) 2138 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 2139 formatted_output = format_table( 2140 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 2141 ) 2142 return formatted_output File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:58, in TorchFormatter.format_row(self, pa_table) 56 def format_row(self, pa_table: pa.Table) -> dict: 57 row = self.numpy_arrow_extractor().extract_row(pa_table) ---> 58 return self.recursive_tensorize(row) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:54, in TorchFormatter.recursive_tensorize(self, data_struct) 53 def recursive_tensorize(self, data_struct: dict): ---> 54 return map_nested(self._recursive_tensorize, data_struct, map_list=False) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:356, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 354 num_proc = 1 355 if num_proc <= 1 or len(iterable) <= num_proc: --> 356 mapped = [ 357 _single_map_nested((function, obj, types, None, True, None)) 358 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 359 ] 360 else: 361 split_kwds = [] # We organize the splits ourselve (contiguous splits) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:357, in <listcomp>(.0) 354 num_proc = 1 355 if num_proc <= 1 or len(iterable) <= num_proc: 356 mapped = [ --> 357 _single_map_nested((function, obj, types, None, True, None)) 358 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 359 ] 360 else: 361 split_kwds = [] # We organize the splits ourselve (contiguous splits) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in _single_map_nested(args) 306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc) 308 if isinstance(data_struct, dict): --> 309 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} 310 else: 311 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in <dictcomp>(.0) 306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc) 308 if isinstance(data_struct, dict): --> 309 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} 310 else: 311 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:293, in _single_map_nested(args) 291 # Singleton first to spare some computation 292 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 293 return function(data_struct) 295 # Reduce logging to keep things readable in multiprocessing with tqdm 296 if rank is not None and logging.get_verbosity() < logging.WARNING: File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:51, in TorchFormatter._recursive_tensorize(self, data_struct) 49 if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects 50 return [self.recursive_tensorize(substruct) for substruct in data_struct] ---> 51 return self._tensorize(data_struct) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:38, in TorchFormatter._tensorize(self, value) 35 import torch 37 default_dtype = {} ---> 38 if np.issubdtype(value.dtype, np.integer): 39 default_dtype = {"dtype": torch.int64} 40 elif np.issubdtype(value.dtype, np.floating): AttributeError: 'NoneType' object has no attribute 'dtype' ``` ### Steps to reproduce the bug 1. Download some audio dataset in this case I used Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets 2. Try the Code from above ### Expected behavior It should work for torch ### Environment info pytorch: 2.0.0 datasets: 2.3.2 numpy: 1.21.6 Python: 3.8 Linux
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Support streaming iwslt2017 dataset
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2022-09-20T08:35:41Z
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Support streaming iwslt2017 dataset. Once this PR is merged: - [x] Remove old ".tgz" data files from the Hub.
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5,592
Fix docstring example
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-02-28T18:42:37Z
2023-02-28T19:26:33Z
2023-02-28T19:19:15Z
MEMBER
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Fixes #5581 to use the correct output for the `set_format` method.
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1,670,034,582
I_kwDODunzps5jirSW
5,761
One or several metadata.jsonl were found, but not in the same directory or in a parent directory
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[ "Also, when generated from a zip archive, the dataset contains only a few images. In my case, 20 versus 2000+ contained in the archive. The generation from folders works as expected.", "Thanks for reporting, @blghtr.\r\n\r\nYou should include the `metadata.jsonl` in your ZIP archives, at the root level directory....
2023-04-16T16:21:55Z
2023-04-19T11:53:24Z
null
NONE
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### Describe the bug An attempt to generate a dataset from a zip archive using imagefolder and metadata.jsonl does not lead to the expected result. Tried all possible locations of the json file: the file in the archive is ignored (generated dataset contains only images), the file next to the archive like [here](https://huggingface.co/docs/datasets/image_dataset#imagefolder) leads to an error: ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1610, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1609 _time = time.time() -> 1610 for key, record in generator: 1611 if max_shard_size is not None and writer._num_bytes > max_shard_size: File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\packaged_modules\folder_based_builder\folder_based_builder.py:370, in FolderBasedBuilder._generate_examples(self, files, metadata_files, split_name, add_metadata, add_labels) 369 else: --> 370 raise ValueError( 371 f"One or several metadata.{metadata_ext} were found, but not in the same directory or in a parent directory of {downloaded_dir_file}." 372 ) 373 if metadata_dir is not None and downloaded_metadata_file is not None: ValueError: One or several metadata.jsonl were found, but not in the same directory or in a parent directory of C:\Users\User\.cache\huggingface\datasets\downloads\extracted\f7fb7de25fb28ae63089974524f2d271a39d83888bc456d04aa3b3d45f33e6a6\ff0745a0-a741-4d9e-b228-a93b851adf61.png. The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[3], line 1 ----> 1 dataset = load_dataset("imagefolder", data_dir=r'C:\Users\User\data') File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\load.py:1791, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 1788 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1790 # Download and prepare data -> 1791 builder_instance.download_and_prepare( 1792 download_config=download_config, 1793 download_mode=download_mode, 1794 verification_mode=verification_mode, 1795 try_from_hf_gcs=try_from_hf_gcs, 1796 num_proc=num_proc, 1797 storage_options=storage_options, 1798 ) 1800 # Build dataset for splits 1801 keep_in_memory = ( 1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1803 ) File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:891, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 889 if num_proc is not None: 890 prepare_split_kwargs["num_proc"] = num_proc --> 891 self._download_and_prepare( 892 dl_manager=dl_manager, 893 verification_mode=verification_mode, 894 **prepare_split_kwargs, 895 **download_and_prepare_kwargs, 896 ) 897 # Sync info 898 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1651, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs) 1650 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs): -> 1651 super()._download_and_prepare( 1652 dl_manager, 1653 verification_mode, 1654 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS 1655 or verification_mode == VerificationMode.ALL_CHECKS, 1656 **prepare_splits_kwargs, 1657 ) File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:986, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 982 split_dict.add(split_generator.split_info) 984 try: 985 # Prepare split will record examples associated to the split --> 986 self._prepare_split(split_generator, **prepare_split_kwargs) 987 except OSError as e: 988 raise OSError( 989 "Cannot find data file. " 990 + (self.manual_download_instructions or "") 991 + "\nOriginal error:\n" 992 + str(e) 993 ) from None File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1490, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size) 1488 gen_kwargs = split_generator.gen_kwargs 1489 job_id = 0 -> 1490 for job_id, done, content in self._prepare_split_single( 1491 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1492 ): 1493 if done: 1494 result = content File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1646, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1644 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1645 e = e.__context__ -> 1646 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1648 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug 1. Organize directory structure like in the docs: folder/metadata.jsonl folder/train.zip 2. Run load_dataset("imagefolder", data_dir='folder/metadata.jsonl', split='train') ### Expected behavior Dataset generated with all additional features from metadata.jsonl ### Environment info - `datasets` version: 2.11.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.9.0 - Huggingface_hub version: 0.13.4 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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MDU6SXNzdWU4NDE1MzUyODM=
2,117
load_metric from local "glue.py" meet error 'NoneType' object is not callable
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[ "@Frankie123421 what was the resolution to this?", "> @Frankie123421 what was the resolution to this?\r\n\r\nuse glue_metric.py instead of glue.py in load_metric", "thank you!" ]
2021-03-26T02:35:22Z
2021-08-25T21:44:05Z
2021-03-26T02:40:26Z
NONE
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actual_task = "mnli" if task == "mnli-mm" else task dataset = load_dataset(path='/home/glue.py', name=actual_task) metric = load_metric(path='/home/glue.py', name=actual_task) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-8-7ab77a465d81> in <module> 1 actual_task = "mnli" if task == "mnli-mm" else task 2 dataset = load_dataset(path='/home/jcli/glue.py', name=actual_task) ----> 3 metric = load_metric(path='/home/jcli/glue.py', name=actual_task) ~/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs) 508 keep_in_memory=keep_in_memory, 509 experiment_id=experiment_id, --> 510 **metric_init_kwargs, 511 ) 512 TypeError: 'NoneType' object is not callable Please help
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PR_kwDODunzps4uEAlw
3,205
Add Multidoc2dial Dataset
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[ "@songfeng cc", "Hi @sivasankalpp, thanks for your PR.\r\n\r\nThere was a bug in TensorFlow/Keras. We have made a temporary fix in our master branch. Please, merge master into your PR branch, so that the CI tests pass.\r\n\r\n```\r\ngit checkout multidoc2dial\r\ngit fetch upstream master\r\ngit merge upstream/mas...
2021-11-03T20:48:31Z
2021-11-24T17:32:49Z
2021-11-24T16:55:08Z
CONTRIBUTOR
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This PR adds the MultiDoc2Dial dataset introduced in this [paper](https://arxiv.org/pdf/2109.12595v1.pdf )
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_prepare_split will overwrite DatasetBuilder.info.features
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[ "Hi ! This might be related to #2153 \r\n\r\nYou're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\nI'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n\r\nEDIT: opened #2201", "> Hi ! This might be related to #2153\r\n> \r\n> Yo...
2021-04-09T11:47:13Z
2021-06-04T10:37:35Z
2021-06-04T10:37:35Z
NONE
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Hi, here is my issue: I initialized a Csv datasetbuilder with specific features: ``` def get_dataset_features(data_args): features = {} if data_args.text_features: features.update({text_feature: hf_features.Value("string") for text_feature in data_args.text_features.strip().split(",")}) if data_args.num_features: features.update({text_feature: hf_features.Value("float32") for text_feature in data_args.num_features.strip().split(",")}) if data_args.label_classes: features["label"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(",")) else: features["label"] = hf_features.Value("float32") return hf_features.Features(features) datasets = load_dataset(extension, data_files=data_files, sep=data_args.delimiter, header=data_args.header, column_names=data_args.column_names.split(",") if data_args.column_names else None, features=get_dataset_features(data_args=data_args)) ``` The `features` is printout as below before `builder_instance.as_dataset` is called: ``` {'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)} ```` But after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to: ``` {'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)} ``` After digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`. But `ArrowWriter` is initailized without passing `features`. So my concern is: It's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function?
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Document better that loading a dataset passing its name does not use the local script
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[ "Thanks for the feedback!\r\n\r\nI think since this issue is closely related to loading, I can add a clearer explanation under [Load > local loading script](https://huggingface.co/docs/datasets/main/en/loading#local-loading-script).", "That makes sense but I think having a line about it under https://huggingface....
2022-07-22T06:07:31Z
2022-08-23T16:32:23Z
2022-08-23T16:32:23Z
MEMBER
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As reported by @TrentBrick here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191858596, it could be more clear that loading a dataset by passing its name does not use the (modified) local script of it. What he did: - he installed `datasets` from source - he modified locally `datasets/the_pile/the_pile.py` loading script - he tried to load it but using `load_dataset("the_pile")` instead of `load_dataset("datasets/the_pile")` - as explained here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191040245: - the former does not use the local script, but instead it downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that. He suggests adding a more clear explanation about this. He suggests adding it maybe in [Installation > source](https://huggingface.co/docs/datasets/installation)) CC: @stevhliu
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Dummy data nowhere to be found
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[ "Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for ...
2022-07-25T19:18:42Z
2022-11-04T14:04:24Z
2022-11-04T14:04:10Z
CONTRIBUTOR
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## Describe the bug To finalize my dataset, I wanted to create dummy data as per the guide and I ran ```shell datasets-cli dummy_data datasets/hebban-reviews --auto_generate ``` where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs and shows a message at the end that it succeeded, I cannot find the dummy data anywhere. Where is it? ## Expected results To see the dummy data in the datasets' folder or in the folder where I ran the command. ## Actual results I see the following message but I cannot find the dummy data anywhere. ``` Dummy data generation done and dummy data test succeeded for config 'filtered''. Automatic dummy data generation succeeded for all configs of '.\datasets\hebban-reviews\' ``` ## Environment info - `datasets` version: 2.4.1.dev0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.8.8 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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Use a new low-memory approach for tf dataset index shuffling
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5863). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma...
2023-05-15T15:28:34Z
2023-06-08T16:40:18Z
2023-06-08T16:32:51Z
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This PR tries out a new approach to generating the index tensor in `to_tf_dataset`, which should reduce memory usage for very large datasets. I'll need to do some testing before merging it! Fixes #5855
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CRD3 dataset card
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2021-06-18T00:24:07Z
2021-06-21T10:18:44Z
2021-06-21T10:18:44Z
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This PR adds additional information to the CRD3 dataset card.
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NewsPH NLI dataset script fails to access test data.
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[ "Thanks @bhavitvyamalik for the fix !\r\nThe fix will be available in the next release.\r\nIt's already available on the `master` branch. For now you can either install `datasets` from source or use `script_version=\"master\"` in `load_dataset` to use the fixed version of this dataset." ]
2021-04-26T06:44:41Z
2021-04-29T09:32:03Z
2021-04-29T09:30:20Z
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In Newsph-NLI Dataset (#1192), it fails to access test data. According to the script below, the download manager will download the train data when trying to download the test data. https://github.com/huggingface/datasets/blob/2a2dd6316af2cc7fdf24e4779312e8ee0c7ed98b/datasets/newsph_nli/newsph_nli.py#L71 If you download it according to the script above, you can see that train and test receive the same data as shown below. ```python >>> from datasets import load_dataset >>> newsph_nli = load_dataset(path="./datasets/newsph_nli.py") >>> newsph_nli DatasetDict({ train: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 420000 }) test: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 420000 }) validation: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 90000 }) }) >>> newsph_nli["train"][0] {'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).', 'label': 1, 'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'} >>> newsph_nli["test"][0] {'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).', 'label': 1, 'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'} ``` In local, I modified the code of the source as below and got the correct result. ```python 71 test_path = os.path.join(download_path, "test.csv") ``` ```python >>> from datasets import load_dataset >>> newsph_nli = load_dataset(path="./datasets/newsph_nli.py") >>> newsph_nli DatasetDict({ train: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 420000 }) test: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 9000 }) validation: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 90000 }) }) >>> newsph_nli["train"][0] {'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).', 'label': 1, 'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'} >>> newsph_nli["test"][0] {'hypothesis': '-- JAI (@JaiPaller) September 13, 2019', 'label': 1, 'premise': 'Pinag-iingat ng Konsulado ng Pilipinas sa Dubai ang publiko, partikular ang mga donor, laban sa mga scam na gumagamit ng mga charitable organization.'} ``` I don't have experience with open source pull requests, so I suggest that you reflect them in the source. Thank you for reading :)
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process .opus files (for Multilingual Spoken Words)
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[ "@lhoestq I still have problems with processing `.opus` files with `soundfile` so I actually cannot fully check that it works but it should... Maybe this should be investigated in case of someone else would also have problems with that.\r\n\r\nAlso, as the data is in a private repo on the hub (before we come to a ...
2022-02-02T15:21:48Z
2022-02-22T10:04:03Z
2022-02-22T10:03:53Z
CONTRIBUTOR
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Opus files requires `libsndfile>=1.0.30`. Add check for this version and tests. **outdated:** Add [Multillingual Spoken Words dataset](https://mlcommons.org/en/multilingual-spoken-words/) You can specify multiple languages for downloading 😌: ```python ds = load_dataset("datasets/ml_spoken_words", languages=["ar", "tt"]) ``` 1. I didn't take into account that each time you pass a set of languages the data for a specific language is downloaded even if it was downloaded before (since these are custom configs like `ar+tt` and `ar+tt+br`. Maybe that wasn't a good idea? 2. The script will have to be slightly changed after merge of https://github.com/huggingface/datasets/pull/3664 2. Just can't figure out what wrong with dummy files... 😞 Maybe we should get rid of them at some point 😁
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Add info in docs about behavior of download_config.num_proc
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2022-07-05T17:01:00Z
2022-07-28T10:40:32Z
2022-07-28T10:40:32Z
CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** I went to override `download_config.num_proc` and was confused about what was happening under the hood. It would be nice to have the behavior documented a bit better so folks know what's happening when they use it. **Describe the solution you'd like** - Add note about how the default number of workers is 16. Related code: https://github.com/huggingface/datasets/blob/7bcac0a6a0fc367cc068f184fa132b8de8dfa11d/src/datasets/download/download_manager.py#L299-L302 - Add note that if the number of workers is higher than the number of files to download, it won't use multiprocessing. **Describe alternatives you've considered** maybe it would also be nice to set `num_proc` = `num_files` when `num_proc` > `num_files`. **Additional context** ...
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2,213
Fix lc_quad download checksum
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2021-04-12T14:16:59Z
2021-04-14T22:04:54Z
2021-04-14T13:42:25Z
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Fixes #2211
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[MINOR] Typo
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-02-01T14:39:39Z
2023-02-02T07:42:28Z
2023-02-02T07:35:14Z
CONTRIBUTOR
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Add reasoning_bg
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[ "Hi @saradhix have you had the chance to reduce the size of the dummy data ?\r\n\r\nFeel free to ping me when it's done so we can merge :) ", "@lhoestq I have reduced the size of the dummy data manually and pushed the changes.", "The CI errors are not related to your dataset.\r\nThey're fixed on master, you can...
2020-12-09T17:30:49Z
2020-12-17T16:50:43Z
2020-12-17T16:50:42Z
CONTRIBUTOR
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Adding reading comprehension dataset for Bulgarian language
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Fix SQuAD v2 metric docs on `references` format
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate" ]
2022-03-24T18:27:10Z
2023-07-11T09:35:46Z
2023-07-11T09:35:15Z
CONTRIBUTOR
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`references` it's not a list of dictionaries but a dictionary that has a list in its values.
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1,073,600,382
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3,400
Improve Wikipedia loading script
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[ "Thanks! See https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikipedia%20Processing.ipynb for more implementation details / some data around the overhead induced by adding the extra preprocessing steps (stripping link prefixes and magic words)", "Closed by:\r\n- #3435" ]
2021-12-07T17:29:25Z
2022-03-22T16:52:28Z
2022-03-22T16:52:28Z
MEMBER
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As reported by @geohci, the "wikipedia" processing/loading script could be improved by some additional small suggested processing functions: - _extract_content(filepath): - Replace .startswith("#redirect") with more structured approach: if elem.find(f"./{namespace}redirect") is None: continue - _parse_and_clean_wikicode(raw_content, parser): - Remove rm_template from cleaning -- this is redundant with .strip_code() from mwparserformhell - Build a language-specific list of namespace prefixes to filter out per below get_namespace_prefixes - Optional: strip prefixes like categories -- e.g., Category:Towns in Tianjin becomes Towns in Tianjin - Optional: strip magic words
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description disappearing from Info when Uploading a Dataset Created with `from_dict`
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[ "Here's a minimal way to reproduce the bug, for the sake of convenience.\r\n````\r\nfrom datasets import Dataset, DatasetInfo, load_dataset\r\n\r\n\r\nepisodes_dict = {\"test\":[1,2,3],\"test2\": [1,2,4]}\r\n\r\nhugging_face_dataset = Dataset.from_dict(\r\n episodes_dict, info=DatasetInfo(description=\"test_str\...
2023-06-20T19:18:26Z
2023-06-22T14:23:56Z
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### Describe the bug When uploading a dataset created locally using `from_dict` with a specified `description` field. It appears before upload, but is missing after upload and re-download. ### Steps to reproduce the bug I think the most relevant pattern in the code might be the following lines: ``` description_json_str = json.dumps( { "dataset_id": dataset.spec.dataset_id, "env_name": dataset.spec.env_spec.id, "action_space": serialize_space(dataset.spec.action_space), "observation_space": serialize_space(dataset.spec.observation_space), } ) hugging_face_dataset = Dataset.from_dict( episodes_dict, info=DatasetInfo(description=description_json_str) ) ``` Which comes from this function https://github.com/balisujohn/minarai/blob/8e023727f0a8488c4451651d9f7a79b981412c40/minari/integrations/hugging_face.py#L39 To replicate, clone this branch of my Minari fork https://github.com/balisujohn/minarai/tree/dev-huggingface then run ``` python3.8 -m venv env source env/bin/activate python3 -m pip install -e . python3 -m pip install pytest ``` The change the hugging face repo path in the test called `test_hugging_face_push_and_pull_dataset` in `tests/integrations/test_hugging_face.py` to one you have permissions to write to. Then run: ``` pytest tests/integrations/test_hugging_face.py::test_hugging_face_push_and_pull_dataset ``` ### Expected behavior DATASET INFO BEFORE UPLOADING DatasetInfo(description='{"dataset_id": "dummy-combo-test-v0", "env_name": "DummyComboEnv-v0", "action_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}]}", "observation_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"component_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [-1.0], \\"high\\": [1.0]}, \\"component_2\\": {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"subcomponent_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, \\"subcomponent_2\\": {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}, {\\"type\\": \\"Discrete\\", \\"dtype\\": \\"int64\\", \\"start\\": 0, \\"n\\": 10}]}}}}}]}]}"}', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None) ... DATASET INFO AFTER UPLOADING AND DOWNLOADING DatasetInfo(description='', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits={'train': SplitInfo(name='train', num_bytes=4846, num_examples=60, shard_lengths=None, dataset_name='parquet')}, download_checksums={'https://huggingface.co/datasets/balisujohn/minari_test/resolve/8217b614ff9ba5edc1a30c7df430e92a46f65363/data/train-00000-of-00001-7c5900b93b35745e.parquet': {'num_bytes': 9052, 'checksum': None}}, download_size=9052, post_processing_size=None, dataset_size=4846, size_in_bytes=13898) ... ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2
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2,723
Fix en subset by modifying dataset_info with correct validation infos
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2021-07-28T13:36:19Z
2021-07-28T15:22:23Z
2021-07-28T15:22:23Z
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- Related to: #2682 We correct the values of `en` subset concerning the expected validation values (both `num_bytes` and `num_examples`. Instead of having: `{"name": "validation", "num_bytes": 828589180707, "num_examples": 364868892, "dataset_name": "c4"}` We replace with correct values: `{"name": "validation", "num_bytes": 825767266, "num_examples": 364608, "dataset_name": "c4"}` There are still issues with validation with other subsets, but I can't download all the files, unzip to check for the correct number of bytes. (If you have a fast way to obtain those values for other subsets, I can do this in this PR ... otherwise I can't spend those resources)
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3,352
Make LABR dataset streamable
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2021-12-01T08:22:27Z
2021-12-01T10:49:02Z
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MEMBER
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Fix LABR dataset to make it streamable. Related to: #3350.
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1,641,763,478
I_kwDODunzps5h21KW
5,675
Filter datasets by language code
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[ "The dataset still can be found, if instead of using the search form you just enter the language code in the url, like https://huggingface.co/datasets?language=language:myv. \r\n\r\nBut of course having a more complete list of languages in the search form (or just a fallback to the language codes, if they are missi...
2023-03-27T09:42:28Z
2023-03-30T08:08:15Z
2023-03-30T08:08:15Z
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Hi! I use the language search field on https://huggingface.co/datasets However, some of the datasets tagged by ISO language code are not accessible by this search form. For example, [myv_ru_2022](https://huggingface.co/datasets/slone/myv_ru_2022) is has `myv` language tag but it is not included in Languages search form. I've also noticed the same problem with `mhr` (see https://huggingface.co/datasets/AigizK/mari-russian-parallel-corpora)
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Big_Patent dataset broken
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[ "Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive.\r\n\r\nSee related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com\r\n\r\nTo quote [@lhoestq](https://gith...
2022-04-25T15:31:45Z
2022-05-26T06:29:43Z
2022-05-02T18:21:15Z
NONE
null
null
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## Dataset viewer issue for '*big_patent*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)* *Unable to view because it says FileNotFound, also cannot download it through the python API* Am I the one who added this dataset ? No
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NonMatchingChecksumError on Spider dataset
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[ "Hi @kolk, thanks for reporting.\r\n\r\nIndeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change:\r\n- #3787 \r\n\r\nWe just made patch release last week: 1.18.4 https://github.com/huggingface/datasets/releases/tag/1.18.4\r\n\r\nPlease, feel free to...
2022-03-14T14:54:53Z
2022-03-15T07:09:51Z
2022-03-15T07:09:51Z
NONE
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## Describe the bug Failure to generate dataset ```spider``` because of checksums error for dataset source files. ## Steps to reproduce the bug ``` from datasets import load_dataset spider = load_dataset("spider") ``` ## Expected results Checksums should match for files from url ['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0'] ## Actual results ``` >>> load_dataset("spider") load_dataset("spider") Downloading and preparing dataset spider/spider (download: 95.12 MiB, generated: 5.17 MiB, post-processed: Unknown size, total: 100.29 MiB) to /home/user/.cache/huggingface/datasets/spider/spider/1.0.0/79778ebea87c59b19411f1eb3eda317e9dd5f7788a556d837ef25c3ae6e5e8b7... Traceback (most recent call last): File "/home/user/py3_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-5-d4cb54197348>", line 1, in <module> load_dataset("spider") File "/home/user/py3_env/lib/python3.8/site-packages/datasets/load.py", line 1702, in load_dataset builder_instance.download_and_prepare( File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 665, in _download_and_prepare verify_checksums( File "/home/user/py3_env/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0'] ``` ## Environment info datasets version: 1.18.3 Platform: Ubuntu 20 LTS Python version: 3.8.10 PyArrow version: 6.0.1
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fix error where reading breaks when batch missing an assigned column feature
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5290). All of your documentation changes will be reflected on that endpoint." ]
2022-11-24T03:53:46Z
2022-11-25T03:21:54Z
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Type error when calling `map` over dataset containing 0-d tensors
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[ "Hi! \r\n\r\nWe could address this by calling `.item()` on such tensors to extract the value, but this would lose us the type, which could lead to storing the generated dataset in a suboptimal format. Considering this, I think the only proper fix would be implementing support for 0-D tensors on Apache Arrow's side ...
2022-11-11T08:27:28Z
2023-01-13T16:00:53Z
2023-01-13T16:00:53Z
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### Describe the bug 0-dimensional tensors in a dataset lead to `TypeError: iteration over a 0-d array` when calling `map`. It is easy to generate such tensors by using `.with_format("...")` on the whole dataset. ### Steps to reproduce the bug ``` ds = datasets.Dataset.from_list([{"a": 1}, {"a": 1}]).with_format("torch") ds.map(None) ``` ### Expected behavior Getting back `ds` without errors. ### Environment info Python 3.10.8 datasets 2.6. torch 1.13.0
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Implement Dataset as context manager
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When used as context manager, it would be safely deleted if some exception is raised. This will avoid > During handling of the above exception, another exception occurred:
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Fix query table with iterable
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The benchmark runs are failing on master because it tries to use an iterable to query the dataset. However there's currently an issue caused by the use of `np.array` instead of `np.fromiter` on the iterable. This PR fixes it
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add stereoset
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StereoSet is a dataset that measures stereotype bias in language models. StereoSet consists of 17,000 sentences that measures model preferences across gender, race, religion, and profession.
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default config name doesn't work when config kwargs are specified.
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[ "@lhoestq ", "What should be the behavior in this case ? Should it override the default config with the added parameter ?", "I know why it should be treated as a new config if overriding parameters are passed. But in some case, I just pass in some common fields like `data_dir`.\r\n\r\nFor example, I want to ext...
2023-08-09T12:43:15Z
2023-08-22T10:03:41Z
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### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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Minor tqdm fixes
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-04-14T18:15:14Z
2023-04-20T15:27:58Z
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`GeneratorBasedBuilder`'s TQDM bars were not used as context managers. This PR fixes that (missed these bars in https://github.com/huggingface/datasets/pull/5560). Also, this PR modifies the single-proc `save_to_disk` to fix the issue with the TQDM bar not accumulating the progress in the multi-shard setting (again, this bug was introduced by me in the linked PR 😎)
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[ "Hii please follow me", "thank you" ]
2020-12-12T16:52:02Z
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Add Twi texts
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Couldn't cast array of type string error with cast_column
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[ "Hi ! I wasn't able to reproduce the error, are you still experiencing this ? I tried calling `cast_column` on a string column containing paths.\r\n\r\nIf you manage to share a reproducible code example that would be perfect", "Hi,\r\n\r\nI think my team mate got this solved. Clolsing it for now and will reopen i...
2022-02-02T18:33:29Z
2022-07-19T13:36:24Z
2022-07-19T13:36:24Z
NONE
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## Describe the bug In OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column("audio",Audio(sampling_rate=16_000)) method I get error ![image](https://user-images.githubusercontent.com/25264037/152214027-9c42a71a-dd24-463c-a346-57e0287e5a8f.png) This was working with datasets version 1.17.1.dev0 but now with version 1.18.3 produces the error above. ## Steps to reproduce the bug load dataset: ![image](https://user-images.githubusercontent.com/25264037/152216145-159553b6-cddc-4f0b-8607-7e76b600e22a.png) remove columns: ![image](https://user-images.githubusercontent.com/25264037/152214707-7c7e89d1-87d8-4b4f-8cfc-5d7223d35644.png) run my fix_path function. This also creates the audio column that is referring to the absolute file path of the audio ![image](https://user-images.githubusercontent.com/25264037/152214773-51f71ccf-d31b-4449-b63a-1af56436e49f.png) Then I concatenate few other datasets and finally try the cast_column method ![image](https://user-images.githubusercontent.com/25264037/152215032-f341ec86-9d6d-48c9-943b-e2efe37a4d98.png) but get error: ![image](https://user-images.githubusercontent.com/25264037/152215073-b85bd057-98e8-413c-9b05-51e9805f2c24.png) ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: OVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn/ovh_huggingface) ![image](https://user-images.githubusercontent.com/25264037/152215161-b4ff7bfb-2736-4afb-9223-761a3338d23c.png) - Python version: 3.8.8 - PyArrow version: ![image](https://user-images.githubusercontent.com/25264037/152215936-4d365760-557e-456b-b5eb-ad1d15cf5073.png)
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Add SPECTER dataset
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[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/SPECTER)" ]
2022-07-07T01:41:32Z
2022-07-14T02:07:49Z
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## Adding a Dataset - **Name:** *SPECTER* - **Description:** *SPECTER: Document-level Representation Learning using Citation-informed Transformers* - **Paper:** *https://doi.org/10.18653/v1/2020.acl-main.207* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/specter_train_triples.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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Adding msr_genomics_kbcomp dataset
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2020-12-07T16:01:30Z
2020-12-08T18:08:55Z
2020-12-08T18:08:47Z
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2,986
Refac module factory + avoid etag requests for hub datasets
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[ "> One thing is that I still don't know at what extent we want to keep backward compatibility for prepare_module. For now I just kept it (except I removed two parameters) just in case, but it's not used anywhere anymore.\r\n\r\nFYI, various other projects currently use it, thus clearly a major version would be requ...
2021-09-29T10:42:00Z
2021-10-11T11:05:53Z
2021-10-11T11:05:52Z
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## Refactor the module factory When trying to extend the `data_files` logic to avoid doing unnecessary ETag requests, I noticed that the module preparation mechanism needed a refactor: - the function was 600 lines long - it was not readable - it contained many different cases that made it complex to maintain - it was hard to properly test it - it was hard to extend without breaking anything The module preparation mechanism is in charge of taking the name of a dataset or a metric given by the user (ex: "squad", "accuracy", "lhoestq/test", "path/to/my/script.py", "path/to/my/data/directory", "json", "csv") and return a module (possibly downloaded from the Hub) that contains the dataset builder or the metric class to use. ### Implementation details I decided to separate all these use cases into different dataset/metric module factories. First, the metric module factories: - **CanonicalMetricModuleFactory**: "accuracy", "rouge", ... - **LocalMetricModuleFactory**: "path/to/my/metric.py" Then, the dataset module factories: - **CanonicalDatasetModuleFactory**: "squad", "glue", ... - **CommunityDatasetModuleFactoryWithScript**: "lhoestq/test" - **CommunityDatasetModuleFactoryWithoutScript**: "lhoestq/demo1" - **PackagedDatasetModuleFactory**: "json", "csv", ... - **LocalDatasetModuleFactoryWithScript**: "path/to/my/script.py" - **LocalDatasetModuleFactoryWithoutScript**: "path/to/my/data/directory" And finally, additional factories when users have no internet: - **CachedDatasetModuleFactory** - **CachedMetricModuleFactory** ### Breaking changes One thing is that I still don't know at what extent we want to keep backward compatibility for `prepare_module`. For now I just kept it (except I removed two parameters) just in case, but it's not used anywhere anymore. ## Avoid etag requests for hub datasets To do this I added a class `DataFilesDict` that can be hashed to define the cache directory of the dataset. It contains the usual data files formatted as `{"train": ["train.txt"]}` for example. But each list of file is a `DataFilesList` that also has a `origin_metadata` attribute that contains metadata about the origin of each file: - for URLs: it stores the ETags of the files - for local files: it stores the last modification data - for files from a Hugging Face repository on the Hub: it stores the pattern (`*`, `*.csv`, "train.txt", etc.) and the commit sha of the repository (so there're no ETag requests !) This way if any file changes, the hash of the `DataFilesDict` changes too ! You can instantiate a `DataFilesDict` by using patterns for local/remote files or files in a HF repository: - for local/remote files: `DataFilesDict.from_local_or_remote(patterns)` - for files in a HF repository: `DataFilesDict.from_hf_repo(patterns, dataset_info)` Fix #2859 ## TODO Fix the latest test: - [x] fix the call to dataset_info in offline mode (related to https://github.com/huggingface/huggingface_hub/issues/372) Add some more tests: - [x] test all the factories - [x] test the new data files logic Other: - [x] docstrings - [x] comments
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Update PAN-X data URL in XTREME dataset
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[ "Merging since the CI is just about missing infos in the dataset card" ]
2021-07-26T12:21:17Z
2021-07-26T13:27:59Z
2021-07-26T13:27:59Z
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Related to #2710, #2691.
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3,649
Add IGLUE dataset
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2022-01-28T14:59:41Z
2022-01-28T15:02:35Z
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## Adding a Dataset - **Name:** IGLUE - **Description:** IGLUE brings together 4 vision-and-language tasks across 20 languages (Twitter [thread](https://twitter.com/ebugliarello/status/1487045497583976455?s=20&t=SB4LZGDhhkUW83ugcX_m5w)) - **Paper:** https://arxiv.org/abs/2201.11732 - **Data:** https://github.com/e-bug/iglue - **Motivation:** This dataset would provide a nice example of combining the text and image features of `datasets` together for multimodal applications. Note: the data / code are not yet visible on the GitHub repo, so I've pinged the authors for more information. 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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Add `data_dir` to `data_files` resolution and misc improvements to HfFileSystem
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2022-02-25T18:26:35Z
2022-03-01T13:10:43Z
2022-03-01T13:10:42Z
CONTRIBUTOR
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As discussed in https://github.com/huggingface/datasets/pull/2830#issuecomment-1048989764, this PR adds a QOL improvement to easily reference the files inside a directory in `load_dataset` using the `data_dir` param (very handy for ImageFolder because it avoids globbing, but also useful for the other loaders). Additionally, it fixes the issue with `HfFileSystem.isdir`, which would previously always return `False`, and aligns the path-handling logic in `HfFileSystem` with `fsspec.GitHubFileSystem`.
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Add evaluation data to acronym_identification
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-06-24T11:17:33Z
2022-06-27T09:37:55Z
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How do i get "amazon_us_reviews
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[ "Seems like the problem isn't with the library, but the dataset itself hosted on AWS S3.\r\n\r\nIts [homepage](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) returns an `AccessDenied` XML response, which is the same thing you get if you try to log the `record` that triggers the exception\r\n\r\n```python\...
2023-07-30T11:02:17Z
2023-08-21T05:08:08Z
2023-08-10T05:02:35Z
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### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data
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1,075,642,915
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3,408
Typo in Dataset viewer error message
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[ "Fixed, thanks\r\n<img width=\"661\" alt=\"Capture d’écran 2021-12-22 à 12 02 30\" src=\"https://user-images.githubusercontent.com/1676121/147082881-cf700e8d-0511-4431-b214-d6cf8137db10.png\">\r\n" ]
2021-12-09T14:34:02Z
2021-12-22T11:02:53Z
2021-12-22T11:02:53Z
MEMBER
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## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* When creating an empty dataset repo, the Dataset Preview provides a helpful message that no files were found. There is a tiny typo in that message: "ressource" should be "resource" ![Screen Shot 2021-12-09 at 15 31 31](https://user-images.githubusercontent.com/26859204/145415725-9cd728f0-c2c8-4b4e-a8e1-4f4d7841c94a.png) Am I the one who added this dataset ? N/A
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replaced PathLike as a variable for save_to_disk for dataset_path wit…
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2023-06-23T00:57:05Z
2023-09-11T04:17:17Z
2023-09-11T04:17:17Z
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…h str like that of load_from_disk
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Temporarily pin tensorflow < 2.14.0
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-09-27T08:16:06Z
2023-09-27T08:45:24Z
2023-09-27T08:36:39Z
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Temporarily pin tensorflow < 2.14.0 until permanent solution is found. Hot fix #6263.
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Add test-datasets CI job
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Closing this one since the dataset scripts will be removed in https://github.com/huggingface/datasets/pull/4974" ]
2022-09-08T13:38:30Z
2023-09-24T10:05:57Z
2022-09-16T13:25:48Z
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To avoid having too many conflicts in the datasets and metrics dependencies I split the CI into test and test-catalog test does the test of the core of the `datasets` lib, while test-catalog tests the datasets scripts and metrics scripts This also makes `pip install -e .[dev]` much smaller for developers WDYT @albertvillanova ?
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I_kwDODunzps5e6ymy
5,555
`.shuffle` throwing error `ValueError: Protocol not known: parent`
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[ "Hi ! The indices mapping is written in the same cachedirectory as your dataset.\r\n\r\nCan you run this to show your current cache directory ?\r\n```python\r\nprint(train_dataset.cache_files)\r\n```", "```\r\n[{'filename': '.../train/dataset.arrow'}, {'filename': '.../train/dataset.arrow'}]\r\n```\r\n\r\nThese a...
2023-02-20T21:33:45Z
2023-02-27T09:23:34Z
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### Describe the bug ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In [16], line 1 ----> 1 train_dataset = train_dataset.shuffle() File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs) 544 self_format = { 545 "type": self._format_type, 546 "format_kwargs": self._format_kwargs, 547 "columns": self._format_columns, 548 "output_all_columns": self._output_all_columns, 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, *args, **kwargs) 482 # Update fingerprint of in-place transforms + update in-place history of transforms 484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3616, in Dataset.shuffle(self, seed, generator, keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint) 3610 return self._new_dataset_with_indices( 3611 fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name 3612 ) 3614 permutation = generator.permutation(len(self)) -> 3616 return self.select( 3617 indices=permutation, 3618 keep_in_memory=keep_in_memory, 3619 indices_cache_file_name=indices_cache_file_name if not keep_in_memory else None, 3620 writer_batch_size=writer_batch_size, 3621 new_fingerprint=new_fingerprint, 3622 ) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs) 544 self_format = { 545 "type": self._format_type, 546 "format_kwargs": self._format_kwargs, 547 "columns": self._format_columns, 548 "output_all_columns": self._output_all_columns, 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, *args, **kwargs) 482 # Update fingerprint of in-place transforms + update in-place history of transforms 484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3266, in Dataset.select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 3263 return self._select_contiguous(start, length, new_fingerprint=new_fingerprint) 3265 # If not contiguous, we need to create a new indices mapping -> 3266 return self._select_with_indices_mapping( 3267 indices, 3268 keep_in_memory=keep_in_memory, 3269 indices_cache_file_name=indices_cache_file_name, 3270 writer_batch_size=writer_batch_size, 3271 new_fingerprint=new_fingerprint, 3272 ) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs) 544 self_format = { 545 "type": self._format_type, 546 "format_kwargs": self._format_kwargs, 547 "columns": self._format_columns, 548 "output_all_columns": self._output_all_columns, 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, *args, **kwargs) 482 # Update fingerprint of in-place transforms + update in-place history of transforms 484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3389, in Dataset._select_with_indices_mapping(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 3387 logger.info(f"Caching indices mapping at {indices_cache_file_name}") 3388 tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False) -> 3389 writer = ArrowWriter( 3390 path=tmp_file.name, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices" 3391 ) 3393 indices = indices if isinstance(indices, list) else list(indices) 3395 size = len(self) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_writer.py:315, in ArrowWriter.__init__(self, schema, features, path, stream, fingerprint, writer_batch_size, hash_salt, check_duplicates, disable_nullable, update_features, with_metadata, unit, embed_local_files, storage_options) 312 self._disable_nullable = disable_nullable 314 if stream is None: --> 315 fs_token_paths = fsspec.get_fs_token_paths(path, storage_options=storage_options) 316 self._fs: fsspec.AbstractFileSystem = fs_token_paths[0] 317 self._path = ( 318 fs_token_paths[2][0] 319 if not is_remote_filesystem(self._fs) 320 else self._fs.unstrip_protocol(fs_token_paths[2][0]) 321 ) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:593, in get_fs_token_paths(urlpath, mode, num, name_function, storage_options, protocol, expand) 591 else: 592 urlpath = stringify_path(urlpath) --> 593 chain = _un_chain(urlpath, storage_options or {}) 594 if len(chain) > 1: 595 inkwargs = {} File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:330, in _un_chain(path, kwargs) 328 for bit in reversed(bits): 329 protocol = split_protocol(bit)[0] or "file" --> 330 cls = get_filesystem_class(protocol) 331 extra_kwargs = cls._get_kwargs_from_urls(bit) 332 kws = kwargs.get(protocol, {}) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/registry.py:240, in get_filesystem_class(protocol) 238 if protocol not in registry: 239 if protocol not in known_implementations: --> 240 raise ValueError("Protocol not known: %s" % protocol) 241 bit = known_implementations[protocol] 242 try: ValueError: Protocol not known: parent ``` This is what the `train_dataset` object looks like ``` Dataset({ features: ['label', 'input_ids', 'attention_mask'], num_rows: 364166 }) ``` ### Steps to reproduce the bug The `train_dataset` obj is created by concatenating two datasets And then shuffle is called, but it throws the mentioned error. ### Expected behavior Should shuffle the dataset properly. ### Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.15.0-1022-aws-x86_64-with-glibc2.31 - Python version: 3.9.13 - PyArrow version: 10.0.0 - Pandas version: 1.4.4
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Update version in xor_tydi_qa.py
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[ "Hi ! Thanks for updating the version\r\n\r\n> Should I revert to the old dummy/1.0.0 or delete it and keep only dummy/1.1.0?\r\n\r\nFeel free to delete the old dummy data files\r\n" ]
2021-06-08T02:23:45Z
2021-06-14T15:35:25Z
2021-06-14T15:35:25Z
CONTRIBUTOR
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Fix #2449 @lhoestq Should I revert to the old `dummy/1.0.0` or delete it and keep only `dummy/1.1.0`?
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Fixed bug in the shape property
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2020-12-23T13:33:21Z
2021-01-02T23:22:52Z
2020-12-23T14:13:13Z
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Fix to the bug reported in issue #1622. Just replaced `return tuple(self._indices.num_rows, self._data.num_columns)` by `return (self._indices.num_rows, self._data.num_columns)`.
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Adding CoNLLpp dataset.
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[ "It looks like this PR now includes changes to many other files than the ones for CoNLLpp.\r\n\r\nTo fix that feel free to create another branch and another PR.\r\n\r\nThis was probably caused by a git rebase. You can avoid this issue by using git merge if you've already pushed your branch." ]
2021-02-19T05:12:30Z
2021-03-04T22:02:47Z
2021-03-04T22:02:47Z
CONTRIBUTOR
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Fix bash snippet formatting in ADD_NEW_DATASET.md
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2021-04-17T16:01:08Z
2021-04-19T10:57:31Z
2021-04-19T07:51:36Z
CONTRIBUTOR
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This PR indents the paragraphs around the bash snippets in ADD_NEW_DATASET.md to fix formatting.
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Multilingual dIalogAct benchMark (miam)
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[ "Hello. All aforementioned changes have been made. I've also re-run black on miam.py. :-)", "I will run isort again. Hopefully it resolves the current check_code_quality test failure.", "Once the review period is over, feel free to open a PR to add all the missing information ;)", "Hi! I will follow up right ...
2021-03-12T23:02:55Z
2021-03-23T10:36:34Z
2021-03-19T10:47:13Z
CONTRIBUTOR
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My collaborators (@EmileChapuis, @PierreColombo) and I within the Affective Computing team at Telecom Paris would like to anonymously publish the miam dataset. It is assocated with a publication currently under review. We will update the dataset with full citations once the review period is over.
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[hub] Fix `api.create_repo` call?
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4102). All of your documentation changes will be reflected on that endpoint.", "Closing in favor of https://github.com/huggingface/datasets/pull/4106" ]
2022-04-05T19:21:52Z
2023-09-24T10:01:14Z
2022-04-12T08:41:46Z
MEMBER
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[CI] fixing seqeval install in ci by pinning setuptools-scm
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2022-06-23T10:24:16Z
2022-06-23T10:13:44Z
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The latest setuptools-scm version supported on 3.6 is 6.4.2. However for some reason circleci has version 7, which doesn't work. I fixed this by pinning the version of setuptools-scm in the circleci job Fix https://github.com/huggingface/datasets/issues/4544
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This PR aligns the signature of `list_repo_files` with the current one in `hfh`, by renaming deprecated `token` to `use_auth_token`. This is already the case for `dataset_info`. CC: @lhoestq
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Last `sacrebleu` release (v2.0.0) has renamed `sacrebleu.corpus_bleu` args from `(sys_stream, ref_streams)` to `(hipotheses, references)`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15 This PR passes the args without parameter names, so that it is valid for all versions of `sacrebleu`. This is a partial hotfix of #2781. Close #2781.
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This PR adds the PASS dataset. Closes #3043
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Related to: - https://github.com/huggingface/transformers/issues/18379 Fix partially #4776.
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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mariosasko thank you for the review! do you have any idea why `test_hash_torch_tensor` fails on \"ubuntu-latest deps-minimum\"? I removed the `torchaudio<0.12.0` test dependency so it uses the latest `torch` now, might it be connect...
2023-02-23T19:19:44Z
2023-02-28T20:25:14Z
2023-02-28T20:16:02Z
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I've removed `torchaudio` completely and switched to use `soundfile` for everything. With the new version of `soundfile` package this should work smoothly because the `libsndfile` C library is bundled, in Linux wheels too. Let me know if you think it's too harsh and we should continue to support `torchaudio` decoding. I decided that we can drop it completely because: 1. it's always something wrong with `torchaudio` (for example recently https://github.com/huggingface/datasets/issues/5488 ) 2. the results of mp3 decoding are different depending on `torchaudio` version 3. `soundfile` is slightly faster then the latest `torchaudio` 4. anyway users can pass any custom decoding function with any library they want if needed (worth putting a snippet in the docs). cc @sanchit-gandhi @vaibhavad
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Update XSUM Factuality DatasetCard
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Proposing metric card for Competition MATH
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Align `column_names` type check with type hint in `sort`
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-06-30T13:15:50Z
2023-06-30T14:18:32Z
2023-06-30T14:11:24Z
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Fix #5998
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Apply flake8-comprehensions to codebase
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2023-02-19T20:05:38Z
2023-02-23T13:59:41Z
2023-02-23T13:59:41Z
CONTRIBUTOR
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### Feature request Apply ruff flake8 comprehension checks to codebase. ### Motivation This should strictly improve the performance / readability of the codebase by removing unnecessary iteration, function calls, etc. This should generate better Python bytecode which should strictly improve performance. I already applied this fixes to PyTorch and Sympy with little issue and have opened PRs to diffusers and transformers todo this as well. ### Your contribution Making a PR.
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Add spanish billion words corpus
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[ "Thank you for your feedback! I've reduced the dummy data size to 2KB.\r\n\r\nI had to rebase to fix `RemoteDatasetTest` fails, sorry about the 80 commits. \r\nI could create a new clean PR if you prefer.", "I have seen that in similar cases you have suggested to other contributors to create another branch and an...
2020-12-08T22:51:38Z
2020-12-11T11:26:39Z
2020-12-11T11:15:28Z
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Add an unannotated Spanish corpus of nearly 1.5 billion words, compiled from different resources from the web.
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SMS Spam Dataset
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2020-12-07T23:18:06Z
2020-12-08T17:42:19Z
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Hi :) I added this [SMS Spam Dataset](http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection)
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Filtering refactor
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[ "I thought at first that the multiproc test was not relevant now that we do stuff only in memory, but I think there's something that's actually broken, my tiny benchmark on bookcorpus runs forever (2hrs+) when I add `num_proc=4` as a kwarg, will investigate 👀 \r\n\r\nI'm not familiar with the caching you describe ...
2021-03-16T09:23:30Z
2023-09-24T09:52:57Z
2021-10-13T09:09:03Z
CONTRIBUTOR
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fix https://github.com/huggingface/datasets/issues/2032 benchmarking is somewhat inconclusive, currently running on `book_corpus` with: ```python bc = load_dataset("bookcorpus") now = time.time() bc.filter(lambda x: len(x["text"]) < 64) elapsed = time.time() - now print(elapsed) ``` this branch does it in 233 seconds, master in 1409 seconds.
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More fine-grained taxonomy of error types
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2021-10-22T09:35:29Z
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**Is your feature request related to a problem? Please describe.** Exceptions like `FileNotFoundError` can be raised by different parts of the code, and it's hard to detect which one did **Describe the solution you'd like** Give a specific exception type for every group of similar errors **Describe alternatives you've considered** Rely on the error message, using regex
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2022-05-23T09:11:20Z
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MEMBER
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Since Python 3.8, the typing module: - raises an AttributeError when trying to access `__args__` on any type, e.g.: `List.__args__` - provides the `get_args` function instead: `get_args(List)` This PR implements a fix for Python >=3.8 whereas maintaining backward compatibility.
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Refactorize tests to use Dataset as context manager
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[ "I find very interesting that idea of using a fixture instead!\r\n\r\nLet me rework a little bit this PR, @lhoestq.", "@lhoestq, as this is a big refactoring, I had many problems to solve the conflicts with the master branch...\r\n\r\nTherefore, I think it is better to merge this as it is, and then to make other ...
2021-04-08T11:21:04Z
2021-04-19T07:53:11Z
2021-04-19T07:53:10Z
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Refactorize Dataset tests to use Dataset as context manager.
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switching some low-level log.info's to log.debug?
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[ "Hi @stas00, thanks for pointing out this issue with logging.\r\n\r\nI agree that `datasets` can sometimes be too verbose... I can create a PR and we could discuss there the choice of the log levels for different parts of the code." ]
2021-06-23T19:26:55Z
2021-06-25T13:40:19Z
2021-06-25T13:40:19Z
CONTRIBUTOR
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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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add Korean Sarcasm Dataset
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2020-12-17T22:49:56Z
2021-09-17T16:54:32Z
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Normalise license names
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[ "we'll add the same server-side metadata validation system as for hf.co/models soon-ish\r\n\r\n(you can check on hf.co/models that licenses are \"clean\")", "Fixed by #4367." ]
2022-05-09T13:51:32Z
2022-05-20T09:51:50Z
2022-05-20T09:51:50Z
CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata. **Describe the solution you'd like** I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) . **Describe alternatives you've considered** None **Additional context** None **Priority** Low
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Fix data URL and metadata of vivos dataset
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-09-12T06:12:34Z
2022-09-12T07:16:15Z
2022-09-12T07:14:19Z
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After contacting the authors of the VIVOS dataset to report that their data server is down, we have received a reply from Hieu-Thi Luong that their data is now hosted on Zenodo: https://doi.org/10.5281/zenodo.7068130 This PR updates their data URL and some metadata (homepage, citation and license). Fix #4936.
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Update data URL of lm1b dataset
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[ "Hi ! I'm getting some 503 from both the http and https addresses. Do you think we could host this data somewhere else ? (please check if there is a license and if it allows redistribution)", "Both HTTP and HTTPS links are working now.\r\n\r\nWe are closing this PR." ]
2022-02-10T18:46:30Z
2022-09-23T11:52:39Z
2022-09-23T11:52:39Z
NONE
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0
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The http address doesn't work anymore
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https://api.github.com/repos/huggingface/datasets/issues/2380
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https://github.com/huggingface/datasets/pull/2380
895,367,201
MDExOlB1bGxSZXF1ZXN0NjQ3NTk3NTc3
2,380
maintain YAML structure reading from README
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2021-05-19T12:12:07Z
2021-05-19T13:08:38Z
2021-05-19T13:08:38Z
CONTRIBUTOR
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How YAML used be loaded earlier in the string (structure of YAML was affected because of this and YAML for datasets with multiple configs was not being loaded correctly): ``` annotations_creators: labeled_final: - expert-generated labeled_swap: - expert-generated unlabeled_final: - machine-generated language_creators: - machine-generated languages: - en licenses: - other multilinguality: - monolingual size_categories: labeled_final: - 10K<n<100K labeled_swap: - 10K<n<100K unlabeled_final: - 100K<n<1M source_datasets: - original task_categories: - text-classification - text-scoring task_ids: - semantic-similarity-classification - semantic-similarity-scoring - text-scoring-other-paraphrase-identification ``` How YAML is loaded in string now: ``` annotations_creators: labeled_final: - expert-generated labeled_swap: - expert-generated unlabeled_final: - machine-generated language_creators: - machine-generated languages: - en licenses: - other multilinguality: - monolingual size_categories: labeled_final: - 10K<n<100K labeled_swap: - 10K<n<100K unlabeled_final: - 100K<n<1M source_datasets: - original task_categories: - text-classification - text-scoring task_ids: - semantic-similarity-classification - semantic-similarity-scoring - text-scoring-other-paraphrase-identification ```
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