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https://api.github.com/repos/huggingface/datasets/issues/3799
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Xtreme-S Metrics
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[ "@lhoestq - if you could take a final review here this would be great (if you have 5min :-) ) ", "Don't think the failures are related but not 100% sure", "Yes the CI fail is unrelated - you can ignore it" ]
2022-03-01T13:42:28Z
2022-03-16T14:40:29Z
2022-03-16T14:40:26Z
MEMBER
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**Added datasets (TODO)**: - [x] MLS - [x] Covost2 - [x] Minds-14 - [x] Voxpopuli - [x] FLoRes (need data) **Metrics**: Done
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Better error message when download fails
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2021-11-30T17:38:50Z
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From our discussions in https://github.com/huggingface/datasets/issues/3269 and https://github.com/huggingface/datasets/issues/3282 it would be nice to have better messages if a download fails. In particular the error now shows: - the error from the HEAD request if there's one - otherwise the response code of the HEAD request I also added an error to tell users to pass `use_auth_token` when the Hugging Face Hub returns 401 (Unauthorized). While paying around with this I also fixed a minor issue with the `force_download` parameter that was not always taken into account
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Dataset.map throws pyarrow.lib.ArrowNotImplementedError when converting from list of empty lists
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[ "Hi @sanderland ! Thanks for reporting :) This is a bug, I opened a PR to fix it. We'll do a new release soon\r\n\r\nIn the meantime you can fix it by specifying in advance that the \"label\" are integers:\r\n```python\r\nimport numpy as np\r\n\r\nds = Dataset.from_dict(\r\n {\r\n \"text\": [\"the lazy do...
2022-06-13T10:47:52Z
2022-06-14T13:34:14Z
2022-06-14T13:34:14Z
CONTRIBUTOR
null
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## Describe the bug Dataset.map throws pyarrow.lib.ArrowNotImplementedError: Unsupported cast from int64 to null using function cast_null when converting from a type of 'empty lists' to 'lists with some type'. This appears to be due to the interaction of arrow internals and some assumptions made by datasets. The bug appeared when binarizing some labels, and then adding a dataset which had all these labels absent (to force the model to not label empty strings such with anything) Particularly the fact that this only happens in batched mode is strange. ## Steps to reproduce the bug ```python import numpy as np ds = Dataset.from_dict( { "text": ["the lazy dog jumps over the quick fox", "another sentence"], "label": [[], []], } ) def mapper(features): features['label'] = [ [0,0,0] for l in features['label'] ] return features ds_mapped = ds.map(mapper,batched=True) ``` ## Expected results Not crashing ## Actual results ``` ../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2346: in map return self._map_single( ../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:532: in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:499: in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/fingerprint.py:458: in wrapper out = func(self, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2751: in _map_single writer.write_batch(batch) ../.venv/lib/python3.8/site-packages/datasets/arrow_writer.py:503: in write_batch arrays.append(pa.array(typed_sequence)) pyarrow/array.pxi:230: in pyarrow.lib.array ??? pyarrow/array.pxi:110: in pyarrow.lib._handle_arrow_array_protocol ??? ../.venv/lib/python3.8/site-packages/datasets/arrow_writer.py:198: in __arrow_array__ out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) ../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper return func(array, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/table.py:1812: in cast_array_to_feature casted_values = _c(array.values, feature.feature) ../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper return func(array, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/table.py:1843: in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper return func(array, *args, **kwargs) ../.venv/lib/python3.8/site-packages/datasets/table.py:1752: in array_cast return array.cast(pa_type) pyarrow/array.pxi:915: in pyarrow.lib.Array.cast ??? ../.venv/lib/python3.8/site-packages/pyarrow/compute.py:376: in cast return call_function("cast", [arr], options) pyarrow/_compute.pyx:542: in pyarrow._compute.call_function ??? pyarrow/_compute.pyx:341: in pyarrow._compute.Function.call ??? pyarrow/error.pxi:144: in pyarrow.lib.pyarrow_internal_check_status ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ > ??? E pyarrow.lib.ArrowNotImplementedError: Unsupported cast from int64 to null using function cast_null pyarrow/error.pxi:121: ArrowNotImplementedError ``` ## Workarounds * Not using batched=True * Using an np.array([],dtype=float) or similar instead of [] in the input * Naming the output column differently from the input column ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.2 - Platform: Ubuntu - Python version: 3.8 - PyArrow version: 8.0.0
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DatasetDict.filter and Dataset.filter crashes with any "fn_kwargs" argument
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[ "Hi @DrMatters, thanks for reporting.\r\n\r\nThis issue was fixed 14 days ago: #2950.\r\n\r\nCurrently, the fix is only in the master branch and will be made available in our next library release.\r\n\r\nIn the meantime, you can incorporate the fix by installing datasets from the master branch:\r\n```shell\r\npip i...
2021-10-04T00:49:29Z
2021-10-11T10:18:01Z
2021-10-04T08:46:13Z
NONE
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## Describe the bug The ".filter" method of DatasetDict or Dataset objects fails when passing any "fn_kwargs" argument ## Steps to reproduce the bug ```python import datasets example_dataset = datasets.Dataset.from_dict({"a": {1, 2, 3, 4}}) def filter_value(example, value): return example['a'] == value filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3}) ``` ## Expected results `filtered` is a dataset containing {"a": {3}} ## Actual results > Traceback (most recent call last): > File "C:\Users\qsemi\Documents\git\nlp_experiments\gpt_celebrity\src\test_faulty_filter.py", line 8, in <module> > filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3}) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper > out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper > out = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2169, in filter > indices = self.map( > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1686, in map > return self._map_single( > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper > out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper > out = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2048, in _map_single > batch = apply_function_on_filtered_inputs( > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1939, in apply_function_on_filtered_inputs > function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) > TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'value' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.9.7 - PyArrow version: 5.0.0
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Add SST-2 dataset
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[ "_The documentation is not available anymore as the PR was closed or merged._", "on the hub this dataset is referenced as `sst-2` not `sst2` – is there a canonical orthography? If not, could we name it `sst-2`?", "@julien-c, we normally do not use hyphens for dataset names: whenever the original dataset name co...
2022-06-10T13:37:26Z
2022-06-13T14:11:34Z
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Add SST-2 dataset. Currently it is part of GLUE benchmark. This PR adds it as a standalone dataset. CC: @julien-c
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Use config pandas version in CSV dataset builder
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2022-02-15T15:47:49Z
2022-02-15T16:55:45Z
2022-02-15T16:55:44Z
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Fix #3724.
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6,214
Unpin fsspec < 2023.9.0
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2023-09-05T11:02:58Z
2023-09-26T15:32:52Z
2023-09-26T15:32:52Z
MEMBER
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Once root issue is fixed, remove temporary pin of fsspec < 2023.9.0 introduced by: - #6210 Related to issue: - #6209 After investigation, I think the root issue is related to the new glob behavior with double asterisk `**` they have introduced in: - https://github.com/fsspec/filesystem_spec/pull/1329
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Add CodeSearchNet corpus dataset
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[ "@lhoestq ready for a second review" ]
2020-12-08T10:07:50Z
2020-12-09T17:05:28Z
2020-12-09T17:05:28Z
CONTRIBUTOR
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This PR adds the CodeSearchNet corpus proxy dataset for semantic code search: https://github.com/github/CodeSearchNet I have had a few issues, mentioned below. Would appreciate some help on how to solve them. ## Issues generating dataset card Is there something wrong with my declaration of the dataset features ? ``` features=datasets.Features( { "repository_name": datasets.Value("string"), "func_path_in_repository": datasets.Value("string"), "func_name": datasets.Value("string"), "whole_func_string": datasets.Value("string"), "language": datasets.Value("string"), "func_code_string": datasets.Value("string"), "func_code_tokens": datasets.Sequence(datasets.Value("string")), "func_documentation_string": datasets.Value("string"), "func_documentation_tokens": datasets.Sequence(datasets.Value("string")), "split_name": datasets.Value("string"), "func_code_url": datasets.Value("string"), # TODO - add licensing info in the examples } ), ``` When running the streamlite app for tagging the dataset on my machine, I get the following error : ![image](https://user-images.githubusercontent.com/33657802/101469132-9ed12c80-3944-11eb-94ff-2d9c1d0ea080.png) ## Issues with dummy data Due to the unusual structure of the data, I have been unable to generate dummy data automatically. I tried to generate it manually, but pytests fail when using the manually-generated dummy data ! Pytests work fine when using the real data. ``` ============================================================================================== test session starts ============================================================================================== platform linux -- Python 3.7.9, pytest-6.1.2, py-1.9.0, pluggy-0.13.1 plugins: xdist-2.1.0, forked-1.3.0 collected 1 item tests/test_dataset_common.py F [100%] =================================================================================================== FAILURES ==================================================================================================== ________________________________________________________________________ LocalDatasetTest.test_load_dataset_all_configs_code_search_net _________________________________________________________________________ self = <tests.test_dataset_common.LocalDatasetTest testMethod=test_load_dataset_all_configs_code_search_net>, dataset_name = 'code_search_net' @slow def test_load_dataset_all_configs(self, dataset_name): configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True) > self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True) tests/test_dataset_common.py:237: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ tests/test_dataset_common.py:198: in check_load_dataset self.parent.assertTrue(len(dataset[split]) > 0) E AssertionError: False is not true --------------------------------------------------------------------------------------------- Captured stdout call ---------------------------------------------------------------------------------------------- Downloading and preparing dataset code_search_net/all (download: 1.00 MiB, generated: 1.00 MiB, post-processed: Unknown size, total: 2.00 MiB) to /tmp/tmppx78sj24/code_search_net/all/1.0.0... Dataset code_search_net downloaded and prepared to /tmp/tmppx78sj24/code_search_net/all/1.0.0. Subsequent calls will reuse this data. --------------------------------------------------------------------------------------------- Captured stderr call ---------------------------------------------------------------------------------------------- ... (irrelevant info - Deprecation warnings) ============================================================================================ short test summary info ============================================================================================ FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_code_search_net - AssertionError: False is not true ========================================================================================= 1 failed, 4 warnings in 3.00s ======================================================================================== ``` ## Note : Data structure in S3 The data is stored on S3, and organized by programming languages. It is stored in the following repository structure: ``` . ├── <language_name> # e.g. python │   └── final │   └── jsonl │   ├── test │   │   └── <language_name>_test_0.jsonl.gz │   ├── train │   │   ├── <language_name>_train_0.jsonl.gz │   │   ├── <language_name>_train_1.jsonl.gz │   │   ├── ... │   │   └── <language_name>_train_n.jsonl.gz │   └── valid │   └── <language_name>_valid_0.jsonl.gz ├── <language_name>_dedupe_definitions_v2.pkl └── <language_name>_licenses.pkl ```
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HfHubHTTPError - exceeded our hourly quotas for action: commit
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[ "how is your dataset structured? (file types, how many commits and files are you trying to push, etc)", "I succeeded in uploading it after several attempts with an hour gap between each attempt (inconvenient but worked). The final dataset is [here](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2), code ...
2023-09-25T06:11:43Z
2023-10-16T13:30:49Z
2023-10-16T13:30:48Z
NONE
null
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### Describe the bug I try to upload a very large dataset of images, and get the following error: ``` File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/hf_api.py:2712, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future) 2710 try: 2711 commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params) -> 2712 hf_raise_for_status(commit_resp, endpoint_name="commit") 2713 except RepositoryNotFoundError as e: 2714 e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE) File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:301, in hf_raise_for_status(response, endpoint_name) 297 raise BadRequestError(message, response=response) from e 299 # Convert `HTTPError` into a `HfHubHTTPError` to display request information 300 # as well (request id and/or server error message) --> 301 raise HfHubHTTPError(str(e), response=response) from e HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/yuvalkirstain/pickapic_v2/commit/main (Request ID: Root=1-65112399-12d63f7d7f28bfa40a36a0fd) You have exceeded our hourly quotas for action: commit. We invite you to retry later. ``` this makes it much less convenient to host large datasets on HF hub. ### Steps to reproduce the bug Upload a very large dataset of images ### Expected behavior the upload to work well ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-1033-aws-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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Checking that split name is correct happens only after the data is downloaded
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2023-02-10T19:13:03Z
2023-02-10T19:14:50Z
null
CONTRIBUTOR
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### Describe the bug Verification of split names (=indexing data by split) happens after downloading the data. So when the split name is incorrect, users learn about that only after the data is fully downloaded, for large datasets it might take a lot of time. ### Steps to reproduce the bug Load any dataset with random split name, for example: ```python from datasets import load_dataset load_dataset("mozilla-foundation/common_voice_11_0", "en", split="blabla") ``` and the download will start smoothly, despite there is no split named "blabla". ### Expected behavior Raise error when split name is incorrect. ### Environment info `datasets==2.9.1.dev0`
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Speed up batched PyTorch DataLoader
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2023-02-08T13:38:59Z
2023-02-19T18:35:09Z
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I implemented `__getitems__` to speed up batched data loading in PyTorch close https://github.com/huggingface/datasets/issues/5505
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fix: 🐛 be more specific when catching exceptions
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[ "To give more context: after our discussion, if I understood properly, you are trying to fix a call to `datasets` that takes 15 minutes: https://github.com/huggingface/datasets-preview-backend/issues/17 Is this right?\r\n\r\n", "Yes, that's it. And to do that I'm trying to use https://pypi.org/project/stopit/, wh...
2021-09-01T12:18:12Z
2021-09-02T09:53:36Z
2021-09-02T09:52:03Z
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The same specific exception is catched in other parts of the same function.
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Add DER metric for SUPERB speaker diarization task
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[ "Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate" ]
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Add Neural Code Search Dataset
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[ "> Really good thanks !\r\n> \r\n> I left a few comments\r\n\r\nThanks, resolved them :) ", "looks like this PR includes changes about many other files than the ones for Code Search\r\n\r\ncan you create another branch and another PR please ?", "> looks like this PR includes changes about many other files than ...
2020-12-06T14:12:39Z
2020-12-09T16:40:15Z
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Fix: ALT - fix duplicated examples in alt-parallel
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2021-02-17T15:53:56Z
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As noticed in #1898 by @10-zin the examples of the `alt-paralel` configurations have all the same values for the `translation` field. This was due to a bad copy of a python dict. This PR fixes that.
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Fix cast from fixed size list to variable size list
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[ "<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 | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-09-15T14:23:33Z
2023-09-19T18:02:21Z
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Fix #6242
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CI fails recurrently and randomly on Windows
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As reported by @lhoestq, The windows CI is currently flaky: some dependencies like `aiobotocore`, `multiprocess` and `seqeval` sometimes fail to install. In particular it seems that building the wheels fail. Here is an example of logs: ``` Building wheel for seqeval (setup.py): started Running command 'C:\tools\miniconda3\envs\py37\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"'; __file__='"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6' No parent package detected, impossible to derive `name` running bdist_wheel running build running build_py package init file 'seqeval\__init__.py' not found (or not a regular file) package init file 'seqeval\metrics\__init__.py' not found (or not a regular file) C:\tools\miniconda3\envs\py37\lib\site-packages\setuptools\command\install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools. setuptools.SetuptoolsDeprecationWarning, installing to build\bdist.win-amd64\wheel running install running install_lib warning: install_lib: 'build\lib' does not exist -- no Python modules to install running install_egg_info running egg_info creating UNKNOWN.egg-info writing UNKNOWN.egg-info\PKG-INFO writing dependency_links to UNKNOWN.egg-info\dependency_links.txt writing top-level names to UNKNOWN.egg-info\top_level.txt writing manifest file 'UNKNOWN.egg-info\SOURCES.txt' reading manifest file 'UNKNOWN.egg-info\SOURCES.txt' writing manifest file 'UNKNOWN.egg-info\SOURCES.txt' Copying UNKNOWN.egg-info to build\bdist.win-amd64\wheel\.\UNKNOWN-0.0.0-py3.7.egg-info running install_scripts creating build\bdist.win-amd64\wheel\UNKNOWN-0.0.0.dist-info\WHEEL creating 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6\UNKNOWN-0.0.0-py3-none-any.whl' and adding 'build\bdist.win-amd64\wheel' to it adding 'UNKNOWN-0.0.0.dist-info/METADATA' adding 'UNKNOWN-0.0.0.dist-info/WHEEL' adding 'UNKNOWN-0.0.0.dist-info/top_level.txt' adding 'UNKNOWN-0.0.0.dist-info/RECORD' removing build\bdist.win-amd64\wheel Building wheel for seqeval (setup.py): finished with status 'done' Created wheel for seqeval: filename=UNKNOWN-0.0.0-py3-none-any.whl size=963 sha256=67eb93a6e1ff4796c5882a13f9fa25bb0d3d103796e2525f9cecf3b2ef26d4b1 Stored in directory: c:\users\circleci\appdata\local\pip\cache\wheels\05\96\ee\7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7 WARNING: Built wheel for seqeval is invalid: Wheel has unexpected file name: expected 'seqeval', got 'UNKNOWN' ```
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[doc build] Use secrets
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[ "<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 | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-06-07T16:09:39Z
2023-06-09T10:16:58Z
2023-06-09T09:53:16Z
CONTRIBUTOR
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Companion pr to https://github.com/huggingface/doc-builder/pull/379
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Fix broken URL's (GEM)
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5056). All of your documentation changes will be reflected on that endpoint.", "Thanks, @manandey. We have removed all dataset scripts from this repo. Subsequent PRs should be opened directly on the Hugging Face Hub." ]
2022-10-03T13:13:22Z
2022-10-04T13:49:00Z
2022-10-04T13:48:59Z
CONTRIBUTOR
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This PR fixes the broken URL's in GEM. cc. @lhoestq, @albertvillanova
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Add `batch_size` parameter when calling `add_faiss_index` and `add_faiss_index_from_external_arrays`
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[ "Also, I had a doubt while checking the code related to the indices... \r\n\r\n@lhoestq, there's a value in `config.py` named `DATASET_INDICES_FILENAME` which has the arrow extension (which I assume it should be `indices.faiss`, as the Elastic Search indices are not stored in a file, but not sure), and it's just us...
2022-06-21T12:18:49Z
2022-06-27T16:25:09Z
2022-06-27T16:14:36Z
CONTRIBUTOR
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Currently, even though the `batch_size` when adding vectors to the FAISS index can be tweaked in `FaissIndex.add_vectors()`, the function `ArrowDataset.add_faiss_index` doesn't have either the parameter `batch_size` to be propagated to the nested `FaissIndex.add_vectors` function or `*args, **kwargs`, so on, this PR adds the `batch_size` parameter to both `ArrowDataset.add_faiss_index` and `ArrowDataset.add_faiss_index_from_external_arrays`. This is useful so as to tweak the `batch_size` according to the VM specifications.
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dataset `asset` - change to raw.githubusercontent.com URLs
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2022-01-03T16:43:57Z
2022-01-03T17:39:02Z
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Changed the URLs to the ones it was automatically re-directing. Before, the download was failing
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Document librispeech configs
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[ "I think the main purpose of #4179 was how to be able to load both configs into one, so should we maybe add this part of the code: https://github.com/huggingface/datasets/issues/4179#issuecomment-1102383717 \r\n\r\nto the doc? \r\n\r\nActually @lhoestq would this work given that they have different split names: htt...
2022-04-19T14:26:59Z
2023-09-24T10:02:24Z
2022-04-19T15:15:20Z
MEMBER
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Added an example of how to load one config or the other
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Offline dataset viewer
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[ "Hi, thanks for the suggestion. It's not possible at the moment. The viewer is part of the Hub codebase and only works on public datasets. Also, it relies on [Datasets Server](https://github.com/huggingface/datasets-server/), which prepares the data and provides an API to access the rows, size, etc.\r\n\r\nIf you'r...
2023-08-10T11:30:00Z
2023-09-29T13:10:23Z
2023-09-29T13:10:22Z
NONE
null
null
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### Feature request The dataset viewer feature is very nice. It enables to the user to easily view the dataset. However, when working for private companies we cannot always upload the dataset to the hub. Is there a way to create dataset viewer offline? I.e. to run a code that will open some kind of html or something that makes it easy to view the dataset. ### Motivation I want to easily view my dataset even when it is hosted locally. ### Your contribution N.A.
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DBPedia14 Dataset Checksum bug?
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[ "Hi ! :)\r\n\r\nThis looks like the same issue as https://github.com/huggingface/datasets/issues/1856 \r\nBasically google drive has quota issues that makes it inconvenient for downloading files.\r\n\r\nIf the quota of a file is exceeded, you have to wait 24h for the quota to reset (which is painful).\r\n\r\nThe er...
2021-02-18T22:25:48Z
2021-02-22T23:22:05Z
2021-02-22T23:22:04Z
CONTRIBUTOR
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Hi there!!! I've been using successfully the DBPedia dataset (https://huggingface.co/datasets/dbpedia_14) with my codebase in the last couple of weeks, but in the last couple of days now I get this error: ``` Traceback (most recent call last): File "./conditional_classification/basic_pipeline.py", line 178, in <module> main() File "./conditional_classification/basic_pipeline.py", line 128, in main corpus.load_data(limit_train_examples_per_class=args.data_args.train_examples_per_class, File "/home/fp/dev/conditional_classification/conditional_classification/datasets_base.py", line 83, in load_data datasets = load_dataset(self.name, split=dataset_split) File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/load.py", line 609, in load_dataset builder_instance.download_and_prepare( File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 526, in download_and_prepare self._download_and_prepare( File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 586, in _download_and_prepare verify_checksums( File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 39, 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=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k'] ``` I've seen this has happened before in other datasets as reported in #537. I've tried clearing my cache and call again `load_dataset` but still is not working. My same codebase is successfully downloading and using other datasets (e.g. AGNews) without any problem, so I guess something has happened specifically to the DBPedia dataset in the last few days. Can you please check if there's a problem with the checksums? Or this is related to any other stuff? I've seen that the path in the cache for the dataset is `/home/fp/.cache/huggingface/datasets/d_bpedia14/dbpedia_14/2.0.0/a70413e39e7a716afd0e90c9e53cb053691f56f9ef5fe317bd07f2c368e8e897...` and includes `d_bpedia14` instead maybe of `dbpedia_14`. Was this maybe a bug introduced recently? Thanks!
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Update README.md
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2021-03-31T03:21:19Z
2021-04-01T10:20:37Z
2021-04-01T10:20:36Z
CONTRIBUTOR
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Updated some descriptions of Wino_Bias dataset.
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Dataset Viewer issue for Toygar/turkish-offensive-language-detection
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[ "I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.\r\n<img width=\"1557\" alt=\"Capture d’écran 2022-07-28 à 09 17 39\" src=\"https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png\">\r\n\r\n" ]
2022-07-28T11:21:43Z
2022-07-28T13:17:56Z
2022-07-28T13:17:48Z
NONE
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### Link https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection ### Description Status code: 400 Exception: Status400Error Message: The dataset does not exist. Hi, I provided train.csv, test.csv and valid.csv files. However, viewer says dataset does not exist. Should I need to do anything else? ### Owner Yes
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Fix push_to_hub with null images
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3856). All of your documentation changes will be reflected on that endpoint." ]
2022-03-08T11:07:09Z
2022-03-08T15:22:17Z
2022-03-08T15:22:16Z
MEMBER
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This code currently raises an error because of the null image: ```python import datasets dataset_dict = { 'name': ['image001.jpg', 'image002.jpg'], 'image': ['cat.jpg', None] } features = datasets.Features({ 'name': datasets.Value('string'), 'image': datasets.Image(), }) dataset = datasets.Dataset.from_dict(dataset_dict, features) dataset.push_to_hub("username/dataset") # this line produces an error: 'NoneType' object is not subscriptable ``` I fixed this in this PR TODO: - [x] add a test
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file missing when load_dataset with openwebtext on windows
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[ "I have tried to extract ```0015896-b1054262f7da52a0518521e29c8e352c.txt``` from ```17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz``` with 7-zip\r\nand put the file into cache_path ```F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd...
2022-08-16T04:04:22Z
2023-01-04T03:39:12Z
2023-01-04T03:39:12Z
NONE
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## Describe the bug 0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz with 7-zip. ## Steps to reproduce the bug ```sh python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base ``` or ```python from datasets import load_dataset load_dataset("openwebtext", None, cache_dir=None, use_auth_token=None) ``` ## Expected results Loading is successful ## Actual results Traceback (most recent call last): File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 704, in download_and_prepare self._download_and_prepare( File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 795, in _download_and_prepare raise OSError( OSError: Cannot find data file. Original error: [Errno 22] Invalid argument: 'F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa/0015896-b1054262f7da52a0518521e29c8e352c.txt' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: windows - Python version: 3.8.5 - PyArrow version: 9.0.0
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Choose columns to stream parquet data in streaming mode
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2023-10-11T08:59:36Z
2023-10-11T16:21:38Z
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Currently passing columns= to load_dataset in streaming mode fails ``` Tried to load parquet data with columns '['link']' with mismatching features '{'caption': Value(dtype='string', id=None), 'image': {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='null', id=None)}, 'link': Value(dtype='string', id=None), 'message_id': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None)}' ``` similar to https://github.com/huggingface/datasets/issues/6039 reported at https://huggingface.co/datasets/laion/dalle-3-dataset/discussions/3#65259a09617407d4520f4ad9
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Fix builder docstring
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-06-01T09:45:30Z
2022-06-02T17:43:47Z
2022-06-02T17:35:15Z
MEMBER
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Currently, the args of `DatasetBuilder` do not appear in the docs: https://huggingface.co/docs/datasets/v2.1.0/en/package_reference/builder_classes#datasets.DatasetBuilder
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404 on Datasets Documentation Page
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[ "This wasn’t working for me a bit earlier, but it looks to be back up now", "We had a minor issue updating the docs after the latest release. It should work now :)." ]
2023-06-22T20:14:57Z
2023-06-26T15:45:03Z
2023-06-26T15:45:03Z
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### Describe the bug Getting a 404 from the Hugging Face Datasets docs page: https://huggingface.co/docs/datasets/index ### Steps to reproduce the bug 1. Go to URL https://huggingface.co/docs/datasets/index 2. Notice 404 not found ### Expected behavior URL should either show docs or redirect to new location ### Environment info hugginface.co
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6117). 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...
2023-08-03T14:46:04Z
2023-08-03T14:56:59Z
2023-08-03T14:46:18Z
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[ "Hi, thanks!\r\n\r\n> I was using Hugging Face datasets to process some very large datasets and found that it would be quite handy to have a feature that will allow to \"split\" these large datasets into chunks with equal size\r\n\r\n`map`, the method we use for processing in `datasets`, already does that if `batch...
2022-08-06T09:14:06Z
2022-10-03T15:35:10Z
2022-10-03T15:35:10Z
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Hi everyone! I was using Hugging Face datasets to process some very large datasets and found that it would be quite handy to have a feature that will allow to "split" these large datasets into chunks with equal size. Even better - be able to run through these chunks one by one in simple and convenient way. So I decided to add the method called shard_generator() to the main Dataset class. It works similar to shard method but it returns a generator of datasets with equal size (defined by shard_size attribute). Example: ```python >>> from datasets import load_dataset >>> ds = load_dataset("rotten_tomatoes", split="validation") >>> ds Dataset({ features: ['text', 'label'], num_rows: 1066 }) >>> next(ds.shard_generator(300)) Dataset({ features: ['text', 'label'], num_rows: 300 }) ``` I hope it can be helpful to someone. Thanks!
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Make Dataset.map accept list of np.array
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Fix #2987.
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Config name / split name lost after map with multiproc
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[ "This must be due to DatasetInfo.from_merge which drops them and is used in `concatenate_datasets`.\r\n\r\nAnd you're experiencing this issue because multiprocessing does concatenate the resulting datasets from each process.\r\n\r\nMaybe they should be kept if all the subdatasets share the same values for config_na...
2023-06-19T17:27:36Z
2023-06-28T08:55:25Z
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CONTRIBUTOR
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### Describe the bug Performing a `.map` method on a dataset loses it's config name / split name only if run with multiproc ### Steps to reproduce the bug ```python from datasets import Audio, load_dataset from transformers import AutoFeatureExtractor import numpy as np # load dummy dataset libri = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") # make train / test splits libri = libri["validation"].train_test_split(seed=42, shuffle=True, test_size=0.1) # example feature extractor model_id = "ntu-spml/distilhubert" feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, do_normalize=True, return_attention_mask=True) sampling_rate = feature_extractor.sampling_rate libri = libri.cast_column("audio", Audio(sampling_rate=sampling_rate)) max_duration = 30.0 def preprocess_function(examples): audio_arrays = [x["array"] for x in examples["audio"]] inputs = feature_extractor( audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=int(feature_extractor.sampling_rate * max_duration), truncation=True, return_attention_mask=True, ) return inputs # single proc map libri_encoded = libri.map( preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=1 ) print(10 * "=" ,"Single processing", 10 * "=") print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split) print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split) # multi proc map libri_encoded = libri.map( preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=2 ) print(10 * "=" ,"Multi processing", 10 * "=") print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split) print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split) ``` **Print Output:** ``` ========== Single processing ========== Config name before: clean Split name before: validation Config name after: clean Split name after: validation ========== Multi processing ========== Config name before: clean Split name before: validation Config name after: None Split name after: None ``` => we can see that the config/split names are lost in the multiprocessing setting ### Expected behavior Should retain both config / split names in the multiproc setting ### Environment info - `datasets` version: 2.13.1.dev0 - Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2
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NonMatchingChecksumError in xcopa dataset
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[ "@albertvillanova here's a separate issue for a bug similar to #3792", "Hi @afcruzs-ms, thanks for opening this separate issue for your problem.\r\n\r\nThe root problem in the other issue (#3792) was a change in the service of Google Drive.\r\n\r\nBut in your case, the `xcopa` dataset is not hosted on Google Driv...
2022-03-02T18:10:19Z
2022-05-20T06:00:42Z
2022-03-03T17:40:31Z
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## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version:
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Support streaming compguesswhat dataset
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-09-12T05:42:24Z
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Support streaming `compguesswhat` dataset. Fix #3191.
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`path` is `None` when downloading a custom audio dataset from the Hub
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[ "Hi! Yes, this is expected behavior - we do this as a security measure to not leak local paths (this info would be useless on other users' machines anyways) and only push audio bytes. \r\n" ]
2022-11-02T11:51:25Z
2022-11-02T12:55:02Z
2022-11-02T12:55:02Z
MEMBER
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### Describe the bug I've created an [audio dataset](https://huggingface.co/datasets/lewtun/audio-test-push) using the `audiofolder` feature desribed in the [docs](https://huggingface.co/docs/datasets/audio_dataset#audiofolder) and then pushed it to the Hub. Locally, I can see the `audio.path` feature is of the expected form `path/to/data_dir`, but when I download the dataset from the Hub, I see `audio.path` is `None` Here's an example: ```python from datasets import load_dataset ds = load_dataset("lewtun/audio-test-push") ds["train"][0] # { # "audio": { # "path": None, <-- Is this expected? # "array": array( # [ # 3.97140226e-07, # 7.30310290e-07, # 7.56406735e-07, # ..., # -1.19636677e-01, # -1.16811886e-01, # -1.12441722e-01, # ] # ), # "sampling_rate": 44100, # }, # "song_id": 0, # "genre_id": 0, # "genre": "Electronic", # } ``` Is this expected behaviour? If yes, feel free to close this issue as it's not a true bug then :) ### Steps to reproduce the bug 1. Create an audio dataset with the `audiofolder` feature 2. Push the dataset to the Hub with `push_to_hub()` 3. Download the Hub dataset and inspect the `audio.path` feature ### Expected behavior `audio.path` points to the file associated with the audio data ### Environment info - `datasets` version: 2.6.2.dev0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.5.1
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5,167
Add ffmpeg4 installation instructions in warnings
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[ "_The documentation is not available anymore as the PR was closed or merged._", "To make it warn only once, feel free to use a global counter in python - and if the warning has already been done, you don't do it again", "> Added the same formatting for the error message :)\r\n\r\nnice!! thank you! \r\n\r\n> Oh ...
2022-10-26T14:21:14Z
2022-10-27T09:01:12Z
2022-10-27T08:58:58Z
CONTRIBUTOR
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Adds instructions on how to install `ffmpeg=4` on Linux (relevant for Colab users). Looks pretty ugly because I didn't find a way to check `ffmpeg` version from python (without `subprocess.call()`; `ctypes.util.find_library` doesn't work`), so the warning is raised on each decoding. Any suggestions on how to make it look nice are welcome! This is how it looks on Colab: ![image](https://user-images.githubusercontent.com/16348744/198052412-d48018d1-4416-4aa5-9114-f7f9b4af031f.png)
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5,640
Less zip false positives
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[ "<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 | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-03-15T16:48:59Z
2023-03-16T13:47:37Z
2023-03-16T13:40:12Z
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`zipfile.is_zipfile` return false positives for some Parquet files. It causes errors when loading certain parquet datasets, where some files are considered ZIP files by `zipfile.is_zipfile` This is a known issue: https://github.com/python/cpython/issues/72680 At first I wanted to rely only on magic numbers, but then I found that someone contributed a [fix to is_zipfile](https://github.com/python/cpython/pull/5053) - do you think we should use it @albertvillanova or not ? IMO it's ok to rely on magic numbers only for now, since in streaming mode we've had no issue checking only the magic number so far. Close https://github.com/huggingface/datasets/issues/5639
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Add IndoNLI dataset
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[ "@lhoestq thanks for the review! I've modified the labels to follow other NLI datasets.\r\nPlease review my change and let me know if I miss anything." ]
2021-11-20T20:46:03Z
2021-11-25T14:51:48Z
2021-11-25T14:51:48Z
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This PR adds IndoNLI dataset, from https://aclanthology.org/2021.emnlp-main.821/
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Unable to load a dataset
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[ "Hi! `load_dataset` is intended to be used to load a canonical dataset (`wikipedia`), a packaged dataset (`csv`, `json`, ...) or a dataset hosted on the Hub. For local datasets saved with `save_to_disk(\"path/to/dataset\")`, use `load_from_disk(\"path/to/dataset\")`.", "Maybe we should raise an informative error ...
2022-02-10T15:05:53Z
2022-02-11T22:56:39Z
2022-02-11T22:56:39Z
NONE
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## Describe the bug Unable to load a dataset from Huggingface that I have just saved. ## Steps to reproduce the bug On Google colab `! pip install datasets ` `from datasets import load_dataset` `my_path = "wiki_dataset"` `dataset = load_dataset('wikipedia', "20200501.fr")` `dataset.save_to_disk(my_path)` `dataset = load_dataset(my_path)` ## Expected results Loading the dataset ## Actual results ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: null _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: string to {'builder_name': Value(dtype='string', id=None), 'citation': Value(dtype='string', id=None), 'config_name': Value(dtype='string', id=None), 'dataset_size': Value(dtype='int64', id=None), 'description': Value(dtype='string', id=None), 'download_checksums': {}, 'download_size': Value(dtype='int64', id=None), 'features': {'title': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}, 'text': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'post_processed': Value(dtype='null', id=None), 'post_processing_size': Value(dtype='null', id=None), 'size_in_bytes': Value(dtype='int64', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='string', id=None)}}, 'supervised_keys': Value(dtype='null', id=None), 'task_templates': Value(dtype='null', id=None), 'version': {'version_str': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'major': Value(dtype='int64', id=None), 'minor': Value(dtype='int64', id=None), 'patch': Value(dtype='int64', id=None)}} because column names don't match ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1
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fix old_val typo in f-string
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2021-11-19T20:51:08Z
2021-11-25T22:14:43Z
2021-11-22T17:04:19Z
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This PR is to correct a typo in #3277 that @Carlosbogo revieled in a comment. Related closed issue : #3257 Sorry about that 😅.
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Can't create a dataset with `float16` features
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[ "Hi @dconathan, thanks for reporting.\r\n\r\nWe rely on Arrow as a backend, and as far as I know currently support for `float16` in Arrow is not fully implemented in Python (C++), hence the `ArrowNotImplementedError` you get.\r\n\r\nSee, e.g.: https://arrow.apache.org/docs/status.html?highlight=float16#data-types",...
2022-09-15T21:03:24Z
2023-03-22T21:40:09Z
null
CONTRIBUTOR
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## Describe the bug I can't create a dataset with `float16` features. I understand from the traceback that this is a `pyarrow` error, but I don't see anywhere in the `datasets` documentation about how to successfully do this. Is it actually supported? I've tried older versions of `pyarrow` as well with the same exact error. The bug seems to arise from `datasets` casting the values to `double` and then `pyarrow` doesn't know how to convert those back to `float16`... does that sound right? Is there a way to bypass this since it's not necessary in the `numpy` and `torch` cases? Thanks! ## Steps to reproduce the bug All of the following raise the following error with the same exact (as far as I can tell) traceback: ```python ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float ``` ```python from datasets import Dataset, Features, Value Dataset.from_dict({"x": [0.0, 1.0, 2.0]}, features=Features(x=Value("float16"))) import numpy as np Dataset.from_dict({"x": np.arange(3, dtype=np.float16)}, features=Features(x=Value("float16"))) import torch Dataset.from_dict({"x": torch.arange(3).to(torch.float16)}, features=Features(x=Value("float16"))) ``` ## Expected results A dataset with `float16` features is successfully created. ## Actual results ```python --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) Cell In [14], line 1 ----> 1 Dataset.from_dict({"x": [1.0, 2.0, 3.0]}, features=Features(x=Value("float16"))) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py:870, in Dataset.from_dict(cls, mapping, features, info, split) 865 mapping = features.encode_batch(mapping) 866 mapping = { 867 col: OptimizedTypedSequence(data, type=features[col] if features is not None else None, col=col) 868 for col, data in mapping.items() 869 } --> 870 pa_table = InMemoryTable.from_pydict(mapping=mapping) 871 if info.features is None: 872 info.features = Features({col: ts.get_inferred_type() for col, ts in mapping.items()}) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:750, in InMemoryTable.from_pydict(cls, *args, **kwargs) 734 @classmethod 735 def from_pydict(cls, *args, **kwargs): 736 """ 737 Construct a Table from Arrow arrays or columns 738 (...) 748 :class:`datasets.table.Table`: 749 """ --> 750 return cls(pa.Table.from_pydict(*args, **kwargs)) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:3648, in pyarrow.lib.Table.from_pydict() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:5174, in pyarrow.lib._from_pydict() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:343, in pyarrow.lib.asarray() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:231, in pyarrow.lib.array() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py:197, in TypedSequence.__arrow_array__(self, type) 192 # otherwise we can finally use the user's type 193 elif type is not None: 194 # We use cast_array_to_feature to support casting to custom types like Audio and Image 195 # Also, when trying type "string", we don't want to convert integers or floats to "string". 196 # We only do it if trying_type is False - since this is what the user asks for. --> 197 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 198 return out 199 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs) 1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1682 else: -> 1683 return func(array, *args, **kwargs) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1853, in cast_array_to_feature(array, feature, allow_number_to_str) 1851 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str) 1852 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1853 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) 1854 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs) 1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1682 else: -> 1683 return func(array, *args, **kwargs) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1762, in array_cast(array, pa_type, allow_number_to_str) 1760 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type): 1761 raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") -> 1762 return array.cast(pa_type) 1763 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}") File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:919, in pyarrow.lib.Array.cast() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/compute.py:389, in cast(arr, target_type, safe, options) 387 else: 388 options = CastOptions.safe(target_type) --> 389 return call_function("cast", [arr], options) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:560, in pyarrow._compute.call_function() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:355, in pyarrow._compute.Function.call() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float ``` ## Environment info - `datasets` version: 2.4.0 - Platform: macOS-12.5.1-arm64-arm-64bit - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.4
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MDExOlB1bGxSZXF1ZXN0NTM1MjY4MjUx
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Add script_version suggestion when dataset/metric not found
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2020-12-09T15:37:38Z
2020-12-10T18:17:05Z
2020-12-10T18:17:05Z
CONTRIBUTOR
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Adds a helpful prompt to the error message when a dataset/metric is not found, suggesting the user might need to pass `script_version="master"` if the dataset was added recently. The whole error looks like: > Couldn't find file locally at blah/blah.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1/metrics/blah/blah.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/met rics/blah/blah.py. If the dataset was added recently, you may need to to pass script_version="master" to find the loading script on the master branch.
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6,208
Do not filter out .zip extensions from no-script datasets
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[ "<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 | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-09-04T06:07:12Z
2023-09-04T09:22:19Z
2023-09-04T09:13:32Z
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This PR is a hotfix of: - #6207 That PR introduced the filtering out of `.zip` extensions. This PR reverts that. Hot fix #6207. Maybe we should do patch releases: the bug was introduced in 2.13.1. CC: @lhoestq
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Include `metadata.jsonl` in resolved data files
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I still don't know if the way we implemented data files resolution could support the metadata.jsonl file without bad side effects for the other packaged builders. In particular here if you have a folder of csv/parquet/whatever files ...
2022-06-27T12:01:29Z
2022-07-01T12:44:55Z
2022-06-30T10:15:32Z
CONTRIBUTOR
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Include `metadata.jsonl` in resolved data files. Fix #4548 @lhoestq ~~https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 adds support for metadata files placed at the root, and https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 accounts for nested metadata files also, but this results in more complex code. Let me know which one of these two approaches you prefer.~~ Maybe https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 is good enough for now (for the sake of simplicity). https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 breaks the imagefolder tests due to duplicates in the resolved metadata files. One way to fix this would be to resolve the metadata pattern only on parent directories, but this adds even more logic to `_get_data_files_patterns`, so not sure if this is what we should do.
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Fix streaming datasets that are not reset correctly
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[ "Works smoothly with the `transformers.Trainer` class now, thank you!" ]
2022-01-27T17:21:02Z
2022-01-28T16:34:29Z
2022-01-28T16:34:28Z
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Streaming datasets that use `StreamingDownloadManager.iter_archive` and `StreamingDownloadManager.iter_files` had some issues. Indeed if you try to iterate over such dataset twice, then the second time it will be empty. This is because the two methods above are generator functions. I fixed this by making them return iterables that are reset properly instead. Close https://github.com/huggingface/datasets/issues/3645 cc @anton-l
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Fix CI by temporarily pinning fsspec < 2023.1.0
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Temporarily pin fsspec < 2023.1.0 Fix #5445.
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Add option to find unique elements in nested sequences when calling `Dataset.unique`
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[ "Hi @mariosasko!\r\n\r\nHas this been patched into any of the releases?", "Hi! Not yet, would you be interested in contributing a PR? I can give you some pointers if needed. ", "@mariosasko did this ever get implemented? Willing to help if you are still up for it.", "@dcruiz01 No, but here is an example of ho...
2021-11-19T13:16:06Z
2023-05-19T14:45:40Z
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It would be nice to have an option to flatten nested sequences to find unique elements stored in them when calling `Dataset.unique`. ~~Currently, `Dataset.unique` only supports finding unique sequences and not unique elements in that situation.~~
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More tags
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[ "Conll has `multilingual` but is only tagged as `en`", "good catch, that was a bad copy paste x)" ]
2021-01-21T13:50:10Z
2021-01-22T09:40:01Z
2021-01-22T09:40:00Z
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Since the hub v2 is going to be released soon I figured it would be great to add the missing tags at least for some of the datasets of reference listed [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#write-the-loadingprocessing-code)
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Fix sequence of array support for most dtype
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2023-06-13T12:38:59Z
2023-06-14T15:11:55Z
2023-06-14T15:03:33Z
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Fixes #5936 Also, a related fix to #5927
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Dataset.from_pandas with a DataFrame of PIL.Images
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[ "A duplicate of https://github.com/huggingface/datasets/issues/4796.\r\n\r\nWe could get this for free by implementing the `Image` feature as an extension type, as shown in [this](https://colab.research.google.com/drive/1Uzm_tXVpGTwbzleDConWcNjacwO1yxE4?usp=sharing) Colab (example with UUIDs).\r\n", "+1 to this\r...
2023-10-10T10:29:16Z
2023-10-12T17:36:27Z
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Currently type inference doesn't know what to do with a Pandas Series of PIL.Image objects, though it would be nice to get a Dataset with the Image type this way
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Pin version exclusion for tensorflow incompatible with keras
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2021-11-04T17:38:06Z
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Once `tensorflow` version 2.6.2 is released: - https://github.com/tensorflow/tensorflow/commit/c1867f3bfdd1042f694df7a9870be51ba80543cb - https://pypi.org/project/tensorflow/2.6.2/ with the patch: - tensorflow/tensorflow#52927 we can remove the temporary fix we introduced in: - #3208 Fix #3209.
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Fix spark imports
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2023-04-26T17:09:32Z
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Raise error for `.tar` archives in the same way as for `.tar.gz` and `.tgz` in `_get_extraction_protocol`
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-12-01T15:19:28Z
2022-12-14T16:37:16Z
2022-12-14T16:33:30Z
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Currently `download_and_extract` doesn't throw an error when it is used with files with `.tar` extension in streaming mode because `_get_extraction_protocol` doesn't do it (like it does for `tar.gz` and `tgz`). `_get_extraction_protocol` returns formatted url as if we support tar protocol but we don't. That means that in dataset scripts `.tar` files would be attempted to load and fail during examples generation (after `download_and_extract` execution). So this PR raises error for `tar` files too.
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Improving elasticsearch integration
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[ "@lhoestq @albertvillanova Iwas trying to fix the failing tests in circleCI but is there a test elasticsearch instance somewhere? If not, can I launch a docker container to have one?" ]
2021-10-07T13:28:35Z
2022-07-06T15:19:48Z
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- adding murmurhash signature to sample in index - adding optional credentials for remote elasticsearch server - enabling sample update in index - upgrade the elasticsearch 7.10.1 python client - adding ElasticsearchBulider to instantiate a dataset from an index and a filtering query
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to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows
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[ "I did some investigation and, as it seems, the bug stems from [this line](https://github.com/huggingface/datasets/blob/8004d7c3e1d74b29c3e5b0d1660331cd26758363/src/datasets/arrow_dataset.py#L325). The lifecycle of the dataset from the linked line is bound to one of the returned `tf.data.Dataset`. So my (hacky) sol...
2021-09-17T15:26:53Z
2021-10-13T09:03:23Z
2021-10-13T09:03:23Z
MEMBER
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To reproduce: ```python import datasets as ds import weakref import gc d = ds.load_dataset("mnist", split="train") ref = weakref.ref(d._data.table) tfd = d.to_tf_dataset("image", batch_size=1, shuffle=False, label_cols="label") del tfd, d gc.collect() assert ref() is None, "Error: there is at least one reference left" ``` This causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards. Moreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this. cc @Rocketknight1
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1,913,813,178
I_kwDODunzps5yEni6
6,261
Can't load a dataset
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[ "I believe is due to the fact that doesn't work with .tgz files.", "`JourneyDB/JourneyDB` is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.\r\n\r\n> I believe is due to the fact that d...
2023-09-26T15:46:25Z
2023-10-05T10:23:23Z
2023-10-05T10:23:22Z
NONE
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### Describe the bug Can't seem to load the JourneyDB dataset. It throws the following error: ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[15], line 2 1 # If the dataset is gated/private, make sure you have run huggingface-cli login ----> 2 dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1661 ignore_verifications = ignore_verifications or save_infos 1663 # Create a dataset builder -> 1664 builder_instance = load_dataset_builder( 1665 path=path, 1666 name=name, 1667 data_dir=data_dir, 1668 data_files=data_files, 1669 cache_dir=cache_dir, 1670 features=features, 1671 download_config=download_config, 1672 download_mode=download_mode, 1673 revision=revision, 1674 use_auth_token=use_auth_token, 1675 **config_kwargs, 1676 ) 1678 # Return iterable dataset in case of streaming 1679 if streaming: File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1488 download_config = download_config.copy() if download_config else DownloadConfig() 1489 download_config.use_auth_token = use_auth_token -> 1490 dataset_module = dataset_module_factory( 1491 path, 1492 revision=revision, 1493 download_config=download_config, 1494 download_mode=download_mode, 1495 data_dir=data_dir, 1496 data_files=data_files, 1497 ) 1499 # Get dataset builder class from the processing script 1500 builder_cls = import_main_class(dataset_module.module_path) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1238, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1236 raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1237 if isinstance(e1, FileNotFoundError): -> 1238 raise FileNotFoundError( 1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" 1241 ) from None 1242 raise e1 from None 1243 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/JourneyDB/JourneyDB/JourneyDB.py or any data file in the same directory. Couldn't find 'JourneyDB/JourneyDB' on the Hugging Face Hub either: FileNotFoundError: Unable to find data in dataset repository JourneyDB/JourneyDB with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` ### Steps to reproduce the bug 1) ``` from huggingface_hub import notebook_login notebook_login() ``` 2) ``` !pip install -q datasets from datasets import load_dataset ``` 3) `dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True)` ### Expected behavior Load the dataset ### Environment info Notebook
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I_kwDODunzps5vU4As
6,183
Load dataset with non-existent file
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[ "Same problem", "This was fixed in https://github.com/huggingface/datasets/pull/6155, which will be included in the next release (or you can install `datasets` from source to use it immediately)." ]
2023-08-25T22:21:22Z
2023-08-29T13:26:22Z
2023-08-29T13:26:22Z
NONE
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### Describe the bug When load a dataset from datasets and pass a wrong path to json with the data, error message does not contain something abount "wrong path" or "file do not exist" - ```SchemaInferenceError: Please pass `features` or at least one example when writing data``` ### Steps to reproduce the bug ```python from datasets import load_dataset load_dataset('json', data_files='/home/alexey/unreal_file.json') ``` ### Expected behavior Raise os FileNotFound error or custom error with informative message ### Environment info ``` # packages in environment at /home/alexey/.conda/envs/alex_LoRA: # # Name Version Build Channel _libgcc_mutex 0.1 main _openmp_mutex 5.1 1_gnu accelerate 0.21.0 pypi_0 pypi aiohttp 3.8.5 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi antlr4-python3-runtime 4.9.3 pypi_0 pypi appdirs 1.4.4 pypi_0 pypi asttokens 2.0.5 pyhd3eb1b0_0 async-timeout 4.0.3 pypi_0 pypi attrs 23.1.0 pypi_0 pypi backcall 0.2.0 pyhd3eb1b0_0 bitsandbytes 0.41.1 pypi_0 pypi bzip2 1.0.8 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config file : /home/alexey/.condarc populated config files : conda version : 23.1.0 conda-build version : 3.22.0 python version : 3.9.13.final.0 virtual packages : __archspec=1=x86_64 __cuda=12.0=0 __glibc=2.35=0 __linux=5.19.0=0 __unix=0=0 base environment : /opt/anaconda/anaconda3 (read only) conda av data dir : /opt/anaconda/anaconda3/etc/conda conda av metadata url : None channel URLs : https://repo.anaconda.com/pkgs/main/linux-64 https://repo.anaconda.com/pkgs/main/noarch https://repo.anaconda.com/pkgs/r/linux-64 https://repo.anaconda.com/pkgs/r/noarch package cache : /opt/anaconda/anaconda3/pkgs /home/alexey/.conda/pkgs envs directories : /home/alexey/.conda/envs /opt/anaconda/anaconda3/envs platform : linux-64 user-agent : conda/23.1.0 requests/2.31.0 CPython/3.9.13 Linux/5.19.0-46-generic ubuntu/22.04.2 glibc/2.35 UID:GID : 1009:1009 netrc file : /home/alexey/.netrc offline mode : False ```
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Unsupported data files raise TypeError: 'NoneType' object is not iterable
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2023-04-24T10:38:03Z
2023-04-27T12:57:30Z
2023-04-27T12:57:30Z
MEMBER
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Currently, we raise a TypeError for unsupported data files: ``` TypeError: 'NoneType' object is not iterable ``` See: - https://github.com/huggingface/datasets-server/issues/1073 We should give a more informative error message.
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4,299
Remove manual download from imagenet-1k
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the reviews @apsdehal and @lhoestq! As suggested by @lhoestq, I'll separate the train/val/test splits, apply the validation split fixes and shuffle the images files to simplify the script and make streaming faster.", "@a...
2022-05-09T20:49:18Z
2022-05-25T14:54:59Z
2022-05-25T14:46:16Z
CONTRIBUTOR
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Remove the manual download code from `imagenet-1k` to make it a regular dataset.
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3,293
Pin version exclusion for Markdown
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2021-11-18T06:56:01Z
2021-11-18T10:28:05Z
2021-11-18T10:28:04Z
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As Markdown version 3.3.5 has a bug, it is better to exclude it in case the users have it previously installed in their environment. Related to #3289, #3286.
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6,015
Add metadata ui screenshot in docs
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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...
2023-07-11T12:16:29Z
2023-07-11T16:07:28Z
2023-07-11T15:56:46Z
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HF Datasets data access is extremely slow even when in memory
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[ "Possibly related:\r\n- https://github.com/pytorch/pytorch/issues/22462" ]
2023-07-31T11:12:19Z
2023-08-01T11:22:43Z
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CONTRIBUTOR
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### Describe the bug Doing a simple `some_dataset[:10]` can take more than a minute. Profiling it: <img width="1280" alt="image" src="https://github.com/huggingface/datasets/assets/36224762/e641fb95-ff02-4072-9016-5416a65f75ab"> `some_dataset` is completely in memory with no disk cache. This is proving fatal to my usage of HF Datasets. Is there a way I can forgo the arrow format and store the dataset as PyTorch tensors so that `_tensorize` is not needed? And is `_consolidate` supposed to take this long? It's faster to produce the dataset from scratch than to access it from HF Datasets! ### Steps to reproduce the bug I have uploaded the dataset that causes this problem [here](https://huggingface.co/datasets/NightMachinery/hf_datasets_bug1). ```python #!/usr/bin/env python3 import sys import time import torch from datasets import load_dataset def main(dataset_name): # Start the timer start_time = time.time() # Load the dataset from Hugging Face Hub dataset = load_dataset(dataset_name) # Set the dataset format as torch dataset.set_format(type="torch") # Perform an identity map dataset = dataset.map(lambda example: example, batched=True, batch_size=20) # End the timer end_time = time.time() # Print the time taken print(f"Time taken: {end_time - start_time:.2f} seconds") if __name__ == "__main__": dataset_name = "NightMachinery/hf_datasets_bug1" print(f"dataset_name: {dataset_name}") main(dataset_name) ``` ### Expected behavior _ ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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4,594
load_from_disk suggests incorrect fix when used to load DatasetDict
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2022-06-29T01:40:01Z
2022-06-29T04:03:44Z
2022-06-29T04:03:44Z
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Edit: Please feel free to remove this issue. The problem was not the error message but the fact that the DatasetDict.load_from_disk does not support loading nested splits, i.e. if one of the splits is itself a DatasetDict. If nesting splits is an antipattern, perhaps the load_from_disk function can throw a warning indicating that?
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load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
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[ "Hi @nsaphra, thanks for reporting.\r\n\r\nThis issue was fixed in `datasets` version 1.3.0. Could you please update `datasets` and tell me if the problem persists?\r\n```shell\r\npip install -U datasets\r\n```", "There might be a bug in the conda version of `datasets` 1.2.1 where the datasets/metric scripts are ...
2021-04-12T20:26:01Z
2021-04-23T15:20:02Z
2021-04-23T15:20:02Z
NONE
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I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package. ```python >>> from datasets import load_metric >>> metric = load_metric("glue", "sst2") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 502, in load_metric File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 66, in import_main_class File "/ext3/miniconda3/lib/python3.8/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1014, in _gcd_import File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 783, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/ns4008/.cache/huggingface/modules/datasets_modules/metrics/glue/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de/glue.py", line 105, in <module> @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) AttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' ```
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[when Image type will exist] provide a way to get the data as binary + filename
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[ "@severo, maybe somehow related to this PR ?\r\n- #3129", "@severo I'll keep that in mind.\r\n\r\nYou can track progress on the Image feature in #3163 (still in the early stage). ", "Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the datase...
2021-10-22T13:23:49Z
2021-12-22T11:05:37Z
2021-12-22T11:05:36Z
CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web. Note: this issue would apply exactly the same for the `Audio` type. **Describe the solution you'd like** If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as: ```python filename: str data: bytes ``` **Describe alternatives you've considered** A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.
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Do not add index column by default when exporting to CSV
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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...
2023-02-01T10:20:55Z
2023-02-09T09:29:08Z
2023-02-09T09:22:23Z
MEMBER
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As pointed out by @merveenoyan, default behavior of `Dataset.to_csv` adds the index as an additional column without name. This PR changes the default behavior, so that now the index column is not written. To add the index column, now you need to pass `index=True` and also `index_label=<name of the index colum>` to name that column. CC: @merveenoyan
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Ensure ConcatenationTable.cast uses target_schema metadata
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[ "Hi @lhoestq, Thanks for the detailed comment. I've tested the suggested approach and can confirm it works for the testcase outlined above! The PR is updated with the changes.", "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-07-01T10:22:08Z
2022-07-19T13:48:45Z
2022-07-19T13:36:24Z
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Currently, `ConcatenationTable.cast` does not use target_schema metadata when casting subtables. This causes an issue when using cast_column and the underlying table is a ConcatenationTable. Code example of where issue arrises: ``` from datasets import Dataset, Image column1 = [0, 1] image_paths = ['/images/image1.jpg', '/images/image2.jpg'] ds = Dataset.from_dict({"column1": column1}) ds = ds.add_column("image", image_paths) ds.cast_column("image", Image()) # Fails here ``` Output ``` ... TypeError: Couldn't cast array of type string to {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ```
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[ "Let me know why the pyarrow test is failing. For one of the config \"trex\", I had to load an initial datafile for a dictionary which is used to augment the rest of the datasets. In the dummy data, the dictionary file was truncated so I had to fudge that. I'm not sure if that is the issue.\r\n", "@ontocord it ju...
2020-12-14T03:27:10Z
2020-12-28T09:51:47Z
2020-12-28T09:51:47Z
CONTRIBUTOR
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This the LAMA dataset for probing facts and common sense from language models. See https://github.com/facebookresearch/LAMA for more details.
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Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`
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[ "Hi ! I just tried to reproduce what you said:\r\n- create a local builder class\r\n- use `load_dataset`\r\n- update the builder class code\r\n- use `load_dataset` again (with or without `ignore_verifications=True`)\r\nAnd it creates a new cache, as expected.\r\n\r\nWhat modifications did you do to your builder's c...
2021-06-29T07:43:03Z
2022-08-04T11:58:36Z
2022-08-04T11:58:36Z
CONTRIBUTOR
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## Describe the bug If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets. ## Steps to reproduce the bug - Create a local dataset builder class - load the local builder class file using `load_dataset` and let the cache build - update the file's content - The cache should rebuilt. ## Expected results With `ignore_verifications=True`, `load_dataset` should pick up existing cache. ## Actual results Creates new cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.7 - PyArrow version: 3.0.0
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Bug for wiki_auto_asset_turk from GEM
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[ "Thanks for reporting, @StevenTang1998.\r\n\r\nI'm looking into it. ", "Hi @StevenTang1998,\r\n\r\nWe have fixed the issue:\r\n- #4389\r\n\r\nThe fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by installing `datasets` from our GitHub repo:\r\n```\r\npip...
2022-05-21T12:31:30Z
2022-05-24T05:55:52Z
2022-05-23T10:29:55Z
NONE
null
null
null
## Describe the bug The script of wiki_auto_asset_turk for GEM may be out of date. ## Steps to reproduce the bug ```python import datasets datasets.load_dataset('gem', 'wiki_auto_asset_turk') ``` ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1731, in load_dataset builder_instance.download_and_prepare( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 640, in download_and_prepare self._download_and_prepare( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 1158, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 707, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/tangtianyi/.cache/huggingface/modules/datasets_modules/datasets/gem/982a54473b12c6a6e40d4356e025fb7172a5bb2065e655e2c1af51f2b3cf4ca1/gem.py", line 538, in _split_generators dl_dir = dl_manager.download_and_extract(_URLs[self.config.name]) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 416, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 294, in download downloaded_path_or_paths = map_nested( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 351, in map_nested mapped = [ File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 352, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 288, in _single_map_nested return function(data_struct) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 320, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 234, in cached_path output_path = get_from_cache( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 579, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://github.com/facebookresearch/asset/raw/master/dataset/asset.test.orig ```
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Add the `GSM8K` dataset
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[ "_The documentation is not available anymore as the PR was closed or merged._", "The CI is failing because it's outdated, but the task tags are updated on `master`, merging :)" ]
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Deterministic split order in DatasetDict.map
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3913). All of your documentation changes will be reflected on that endpoint.", "I'm surprised this is needed because the order of the `dict` keys is deterministic as of Python 3.6 (documented in 3.7). Is there a reproducer for ...
2022-03-14T17:58:37Z
2023-09-24T09:55:10Z
2022-03-15T10:45:15Z
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The order in which the splits are processed by `map` is not deterministic in `DatasetDict.map`. This can cause caching issues when the processing function is stateful and sensible to the order in which examples are processed Close https://github.com/huggingface/datasets/issues/3847
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how to limit the size of memory mapped file?
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[ "Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.", "The trace of the error. Streaming works but is slower.\r\n```\r\nRoot Cause (first observed failure):\r\n[0]:\r\n time : 2023-08-24_06:06...
2023-08-24T05:33:45Z
2023-10-11T06:00:10Z
null
NONE
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### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine)
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3,435
Improve Wikipedia Loading Script
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[ "I wanted to flag a change from since we discussed this: I initially wrote a function for using the Wikimedia APIs to collect namespace aliases, but decided that adding in more http requests to the script wasn't a great idea so instead used that code to build a static list that I just added directly to the code.\r\...
2021-12-15T13:30:06Z
2022-03-04T08:16:00Z
2022-03-04T08:16:00Z
CONTRIBUTOR
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* More structured approach to detecting redirects * Remove redundant template filter code (covered by strip_code) * Add language-specific lists of additional media namespace aliases for filtering * Add language-specific lists of category namespace aliases for new link text cleaning step * Remove magic words (parser directions like __TOC__ that occasionally occur in text) Fix #3400 With support from @albertvillanova CC @yjernite
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MDU6SXNzdWU3NzQ0MjI2MDM=
1,633
social_i_qa wrong format of labels
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[ "@lhoestq, should I raise a PR for this? Just a minor change while reading labels text file", "Sure feel free to open a PR thanks !" ]
2020-12-24T13:11:54Z
2020-12-30T17:18:49Z
2020-12-30T17:18:49Z
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Hi, there is extra "\n" in labels of social_i_qa datasets, no big deal, but I was wondering if you could remove it to make it consistent. so label is 'label': '1\n', not '1' thanks ``` >>> import datasets >>> from datasets import load_dataset >>> dataset = load_dataset( ... 'social_i_qa') cahce dir /julia/cache/datasets Downloading: 4.72kB [00:00, 3.52MB/s] cahce dir /julia/cache/datasets Downloading: 2.19kB [00:00, 1.81MB/s] Using custom data configuration default Reusing dataset social_i_qa (/julia/datasets/social_i_qa/default/0.1.0/4a4190cc2d2482d43416c2167c0c5dccdd769d4482e84893614bd069e5c3ba06) >>> dataset['train'][0] {'answerA': 'like attending', 'answerB': 'like staying home', 'answerC': 'a good friend to have', 'context': 'Cameron decided to have a barbecue and gathered her friends together.', 'label': '1\n', 'question': 'How would Others feel as a result?'} ```
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I_kwDODunzps5LAi4_
4,439
TIMIT won't load after manual download: Errors about files that don't exist
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[ "To have some context, please see:\r\n- #4145\r\n\r\nPlease, also note that we have recently made some fixes to the script, which are in our GitHub master branch but not yet released:\r\n- #4422\r\n- #4425 \r\n- #4436", "Thanks Albert! I'll try pulling `datasets` from the git repo instead of PyPI, and/or just wai...
2022-06-02T16:35:56Z
2022-06-03T08:44:17Z
2022-06-03T08:44:16Z
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## Describe the bug I get the message from HuggingFace that it must be downloaded manually. From the URL provided in the message, I got to UPenn page for manual download. (UPenn apparently want $250? for the dataset??) ...So, ok, I obtained a copy from a friend and also a smaller version from Kaggle. But in both cases the HF dataloader fails; it is looking for files that don't exist anywhere in the dataset: it is looking for files with lower-case letters like "**test*" (all the filenames in both my copies are uppercase) and certain file extensions that exclude the .DOC which is provided in TIMIT: ## Steps to reproduce the bug ```python data = load_dataset('timit_asr', 'clean')['train'] ``` ## Expected results The dataset should load with no errors. ## Actual results This error message: ``` File "/home/ubuntu/envs/data2vec/lib/python3.9/site-packages/datasets/data_files.py", line 201, in resolve_patterns_locally_or_by_urls raise FileNotFoundError(error_msg) FileNotFoundError: Unable to resolve any data file that matches '['**test*', '**eval*']' at /home/ubuntu/datasets/timit with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` But this is a strange sort of error: why is it looking for lower-case file names when all the TIMIT dataset filenames are uppercase? Why does it exclude .DOC files when the only parts of the TIMIT data set with "TEST" in them have ".DOC" extensions? ...I wonder, how was anyone able to get this to work in the first place? The files in the dataset look like the following: ``` ³ PHONCODE.DOC ³ PROMPTS.TXT ³ SPKRINFO.TXT ³ SPKRSENT.TXT ³ TESTSET.DOC ``` ...so why are these being excluded by the dataset loader? ## Environment info - `datasets` version: 2.2.2 - Platform: Linux-5.4.0-1060-aws-x86_64-with-glibc2.27 - Python version: 3.9.9 - PyArrow version: 8.0.0 - Pandas version: 1.4.2
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#3337 Add typing overloads to Dataset.__getitem__ for mypy
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[ "Locally the `make quality` passes with the same dependencies. I would suggest upgrading flake8. (I can take care of it in another PR)\r\ncc @lhoestq ", "Thank you for fixing flake8! I think we are ready to merge then. " ]
2021-12-04T20:54:49Z
2021-12-14T10:28:55Z
2021-12-14T10:28:55Z
CONTRIBUTOR
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Add typing overloads to Dataset.__getitem__ for mypy Fixes #3337 **Iterable** Iterable from `collections` cannot have a type, so you can't do `Iterable[int]` for example. `typing` has a Generic version that builds upon the one from `collections`. **Flake8** I had to add `# noqa: F811`, this is a bug from Flake8. datasets uses flake8==3.7.9 which released in October 2019 if I update flake8 (4.0.1), I no longer get these errors, but I did not want to make the update without your approval. (It also triggers other errors like no args in f-strings.)
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Replace metadata utils with `huggingface_hub`'s RepoCard API
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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...
2023-06-13T13:03:19Z
2023-06-27T16:47:51Z
2023-06-27T16:38:32Z
CONTRIBUTOR
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Use `huggingface_hub`'s RepoCard API instead of `DatasetMetadata` for modifying the card's YAML, and deprecate `datasets.utils.metadata` and `datasets.utils.readme`. After removing these modules, we can also delete `datasets.utils.resources` since the moon landing repo now stores its own version of these resources for the metadata UI. PS: this change requires bumping `huggingface_hub` to 0.13.0 (Transformers requires 0.14.0, so should be ok)
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[ "Thanks for all the comments and for the corrections in the docs !\r\n\r\nAbout all the points you mentioned:\r\n\r\n> * the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch ...
2021-07-21T10:43:14Z
2021-07-27T18:40:54Z
2021-07-27T18:40:54Z
CONTRIBUTOR
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Some comments here: - the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch 'datasets[streaming]'` (maybe just link to https://huggingface.co/docs/datasets/installation.html + a one-liner that installs all the requirements / alternatively a requirements.txt file) - "If you’d like to play with the examples, you must install it from source." in https://huggingface.co/docs/datasets/installation.html: it's not clear to me what this means (what are these "examples"?) - in https://huggingface.co/docs/datasets/loading_datasets.html: "or AWS bucket if it’s not already stored in the library". It's the only place in the doc (aside from the docstring https://huggingface.co/docs/datasets/package_reference/loading_methods.html?highlight=aws bucket#datasets.list_datasets) where the "AWS bucket" is mentioned. It's not easy to understand what this means. Maybe explain more, and link to https://s3.amazonaws.com/datasets.huggingface.co and/or https://huggingface.co/docs/datasets/filesystems.html. - example in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files is obsoleted by https://github.com/huggingface/datasets/pull/2326. Also: see https://github.com/huggingface/datasets/issues/2691 for a bug on this specific dataset. - in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files the doc says "After you’ve downloaded the files, you can point to the folder hosting them locally with the data_dir argument as follows:", but the following example does not show how to use `data_dir` - in https://huggingface.co/docs/datasets/loading_datasets.html#csv-files, it would be nice to have an URL to the csv loader reference (but I'm not sure there is one in the API reference). This comment applies in many places in the doc: I would want the API reference to contain doc for all the code/functions/classes... and I would want a lot more links inside the doc pointing to the API entries. - in the API reference (docstrings) I would prefer "SOURCE" to link to github instead of a copy of the code inside the docs site (eg. https://github.com/huggingface/datasets/blob/master/src/datasets/load.py#L711 instead of https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset) - it seems like not all the API is exposed in the doc. For example, there is no doc for [`disable_progress_bar`](https://github.com/huggingface/datasets/search?q=disable_progress_bar), see https://huggingface.co/docs/datasets/search.html?q=disable_progress_bar, even if the code contains docstrings. Does it mean that the function is not officially supported? (otherwise, maybe it also deserves a mention in https://huggingface.co/docs/datasets/package_reference/logging_methods.html) - in https://huggingface.co/docs/datasets/loading_datasets.html?highlight=most%20efficient%20format%20have%20json%20files%20consisting%20multiple%20json%20objects#json-files, "The most efficient format is to have JSON files consisting of multiple JSON objects, one per line, representing individual data rows:", maybe link to https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON and give it a name ("line-delimited JSON"? "JSON Lines" as in https://huggingface.co/docs/datasets/processing.html#exporting-a-dataset-to-csv-json-parquet-or-to-python-objects ?) - in https://huggingface.co/docs/datasets/loading_datasets.html, for the local files sections, it would be nice to provide sample csv / json / text files to download, so that it's easier for the reader to try to load them (instead: they won't try) - the doc explains how to shard a dataset, but does not explain why and when a dataset should be sharded (I have no idea... for [parallelizing](https://huggingface.co/docs/datasets/processing.html#multiprocessing)?). It does neither give an idea of the number of shards a dataset typically should have and why. - the code example in https://huggingface.co/docs/datasets/processing.html#mapping-in-a-distributed-setting does not work, because `training_args` has not been defined before in the doc.
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I_kwDODunzps5INVwv
4,198
There is no dataset
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2022-04-21T19:19:26Z
2022-05-03T11:29:05Z
2022-04-22T06:12:25Z
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## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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PR_kwDODunzps4toGpe
3,158
Fix string encoding for Value type
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[ "That was fast! \r\n" ]
2021-10-25T13:44:13Z
2021-10-25T14:12:06Z
2021-10-25T14:12:05Z
MEMBER
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Some metrics have `string` features but currently it fails if users pass integers instead. Indeed feature encoding that handles the conversion of the user's objects to the right python type is missing a case for `string`, while it already works as expected for integers, floats and booleans Here is an example code that didn't work previously, but that works with this fix: ```python import datasets # Note that 'id' is an integer while the SQuAD metric uses strings predictions = [{'prediction_text': '1976', 'id': 5}] references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': 5}] squad_metric = datasets.load_metric("squad") squad_metric.add_batch(predictions=predictions, references=references) results = squad_metric.compute() # {'exact_match': 100.0, 'f1': 100.0} ``` cc @sgugger @philschmid
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I_kwDODunzps5EvV7h
3,795
can not flatten natural_questions dataset
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[ "same issue. downgrade it to a lower version.", "Thanks for reporting, I'll take a look tomorrow :)" ]
2022-02-27T13:57:40Z
2022-03-21T14:36:12Z
2022-03-21T14:36:12Z
NONE
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## Describe the bug after downloading the natural_questions dataset, can not flatten the dataset considering there are `long answer` and `short answer` in `annotations`. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('natural_questions',cache_dir = 'data/dataset_cache_dir') dataset['train'].flatten() ``` ## Expected results a dataset with `long_answer` as features ## Actual results Traceback (most recent call last): File "temp.py", line 5, in <module> dataset['train'].flatten() File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/fingerprint.py", line 413, in wrapper out = func(self, *args, **kwargs) File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1296, in flatten dataset._data = update_metadata_with_features(dataset._data, dataset.features) File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 536, in update_metadata_with_features features = Features({col_name: features[col_name] for col_name in table.column_names}) File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 536, in <dictcomp> features = Features({col_name: features[col_name] for col_name in table.column_names}) KeyError: 'annotations.long_answer' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.13 - Platform: MBP - Python version: 3.8 - PyArrow version: 6.0.1
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PR_kwDODunzps45X0XY
4,465
Fix bigbench config names
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-06-09T08:06:19Z
2022-06-09T14:38:36Z
2022-06-09T14:29:19Z
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Fix https://github.com/huggingface/datasets/issues/4462 in the case of bigbench
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3,983
Infinitely attempting lock
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[ "Hi ! Thanks for reporting. We're using `py-filelock` as our locking mechanism.\r\n\r\nCan you try deleting the .lock file mentioned in the logs and try again ? Make sure that no other process is generating the `cnn_dailymail` dataset.\r\n\r\nIf it doesn't work, could you try to set up a lock using the latest vers...
2022-03-21T18:11:57Z
2022-05-06T16:12:18Z
2022-05-06T16:12:18Z
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I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`. Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS). ``` %sh python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \ --model_name_or_path t5-small \ --do_train \ --do_eval \ --dataset_name cnn_dailymail \ --dataset_config "3.0.0" \ --source_prefix "summarize: " \ --output_dir /dbfs/transformers/tmp/tst-summarization \ --per_device_train_batch_size=4 \ --per_device_eval_batch_size=4 \ --overwrite_output_dir \ --predict_with_generate \ --log_level debug \ --cache_dir /dbfs/transformers/cache ``` All goes well until acquiring a lock -- ``` 03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock 03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ... ``` and so on. I imagine this has to do with DBFS -- is there a way to tackle this?
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Add `IterableDataset.from_generator` to the API
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[ "#take", "Thanks @hamid-vakilzadeh ! Let us know if you have some questions or if we can help", "Thank you! I certainly will reach out if I need any help." ]
2022-09-16T15:19:41Z
2022-10-05T12:10:49Z
2022-10-05T12:10:49Z
CONTRIBUTOR
null
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We've just added `Dataset.from_generator` to the API. It would also be cool to add `IterableDataset.from_generator` to support creating an iterable dataset from a generator. cc @lhoestq
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Validate name parameter in make_file_instructions
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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...
2023-05-26T11:12:46Z
2023-05-31T07:43:32Z
2023-05-31T07:34:57Z
MEMBER
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Validate `name` parameter in `make_file_instructions`. This way users get more informative error messages, instead of: ```stacktrace .../huggingface/datasets/src/datasets/arrow_reader.py in make_file_instructions(name, split_infos, instruction, filetype_suffix, prefix_path) 110 name2len = {info.name: info.num_examples for info in split_infos} 111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos} --> 112 name2filenames = { 113 info.name: filenames_for_dataset_split( 114 path=prefix_path, .../huggingface/datasets/src/datasets/arrow_reader.py in <dictcomp>(.0) 111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos} 112 name2filenames = { --> 113 info.name: filenames_for_dataset_split( 114 path=prefix_path, 115 dataset_name=name, .../huggingface/datasets/src/datasets/naming.py in filenames_for_dataset_split(path, dataset_name, split, filetype_suffix, shard_lengths) 68 69 def filenames_for_dataset_split(path, dataset_name, split, filetype_suffix=None, shard_lengths=None): ---> 70 prefix = filename_prefix_for_split(dataset_name, split) 71 prefix = os.path.join(path, prefix) 72 .../huggingface/datasets/src/datasets/naming.py in filename_prefix_for_split(name, split) 52 53 def filename_prefix_for_split(name, split): ---> 54 if os.path.basename(name) != name: 55 raise ValueError(f"Should be a dataset name, not a path: {name}") 56 if not re.match(_split_re, split): .../lib/python3.9/posixpath.py in basename(p) 140 def basename(p): 141 """Returns the final component of a pathname""" --> 142 p = os.fspath(p) 143 sep = _get_sep(p) 144 i = p.rfind(sep) + 1 TypeError: expected str, bytes or os.PathLike object, not NoneType ``` Related to #5895.
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3,465
Unable to load 'cnn_dailymail' dataset
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[ "Hi @talha1503, thanks for reporting.\r\n\r\nIt seems there is an issue with one of the data files hosted at Google Drive:\r\n```\r\nGoogle Drive - Quota exceeded\r\n\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing...
2021-12-21T03:32:21Z
2022-02-17T14:13:57Z
2022-02-17T14:13:57Z
NONE
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## Describe the bug I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True) ``` ## Expected results Expecting to load 'cnn_dailymail' dataset. ## Actual results `NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0
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6,293
Choose columns to stream parquet data in streaming mode
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2023-10-11T08:59:36Z
2023-10-11T16:21:38Z
2023-10-11T16:21:38Z
MEMBER
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Currently passing columns= to load_dataset in streaming mode fails ``` Tried to load parquet data with columns '['link']' with mismatching features '{'caption': Value(dtype='string', id=None), 'image': {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='null', id=None)}, 'link': Value(dtype='string', id=None), 'message_id': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None)}' ``` similar to https://github.com/huggingface/datasets/issues/6039 reported at https://huggingface.co/datasets/laion/dalle-3-dataset/discussions/3#65259a09617407d4520f4ad9
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Audio: `encode_example` fails with IndexError
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[ "Sorry bug on my part 😅 Closing " ]
2022-09-22T15:07:27Z
2022-09-23T09:05:18Z
2022-09-23T09:05:18Z
CONTRIBUTOR
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## Describe the bug Loading the dataset [earnings-22](https://huggingface.co/datasets/sanchit-gandhi/earnings22_split) from the Hub yields an Index Error. I created this dataset locally and then pushed to hub at the specified URL. Thus, I expect the dataset should work out-of-the-box! Indeed, the dataset viewer functions correctly, and there were no issues when I had the dataset locally. Don't think it's a sound file bug as the version matches what worked previously. Update: the bug appeared for me on a GPU, mysteriously on a TPU I can't repro and it downloads correctly... ## Steps to reproduce the bug ```python from datasets import load_dataset earnings22 = load_dataset("sanchit-gandhi/earnings22_split") ``` ## Expected results ``` >>> earnings22 DatasetDict({ validation: Dataset({ features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'], num_rows: 2650 }) train: Dataset({ features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'], num_rows: 52006 }) test: Dataset({ features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'], num_rows: 2735 }) }) ``` ## Actual results ``` Traceback (most recent call last): File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2764, in _map_single writer.write(example) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 451, in write self.write_examples_on_file() File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 409, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 508, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 231, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 197, in __arrow_array__ out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1683, in wrapper return func(array, *args, **kwargs) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1795, in cast_array_to_feature return feature.cast_storage(array) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in cast_storage storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()]) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in <listcomp> storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()]) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 92, in encode_example sf.write(buffer, value["array"], value["sampling_rate"], format="wav") File "/opt/conda/envs/hf/lib/python3.8/site-packages/soundfile.py", line 313, in write channels = data.shape[1] IndexError: tuple index out of range ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.3 Plus: - SoundFile version: 0.10.3.post1 cc @lhoestq @polinaeterna
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download_mode="force_redownload" does not refresh cached dataset
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2022-11-21T14:12:43Z
2022-11-21T14:13:03Z
null
NONE
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### Describe the bug `load_datasets` does not refresh dataset when features are imported from external file, even with `download_mode="force_redownload"`. The bug is not limited to nested fields, however it is more likely to occur with nested fields. ### Steps to reproduce the bug To reproduce the bug 3 files are needed: `dataset.py` (contains dataset loading script), `schema.py` (contains features of dataset) and `main.py` (to run `load_datasets`) `dataset.py` ```python import datasets from schema import features class NewDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( features=features ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN ) ] def _generate_examples(self): data = [ {"id": 0, "nested": []}, {"id": 1, "nested": []} ] for key, example in enumerate(data): yield key, example ``` `schema.py` ```python import datasets features = datasets.Features( { "id": datasets.Value("int32"), "nested": [ {"text": datasets.Value("string")} ] } ) ``` `main.py` ```python import datasets a = datasets.load_dataset("dataset.py") print(a["train"].info.features) ``` Now if `main.py` is run it prints the following correct output: `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`. However, if f.e. the label of the feature "text" is changed to something else, f.e. to `schema.py` ```python import datasets features = datasets.Features( { "id": datasets.Value("int32"), "nested": [ {"textfoo": datasets.Value("string")} ] } ) ``` `main.py` still prints `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`, even if run with `download_mode="force_redownload"`. The only fix is to delete the folder in the cache. ### Expected behavior The cached dataset is deleted and refreshed when using `load_datasets` with `download_mode="force_redownload"`. ### Environment info - `datasets` version: 2.7.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.9 - PyArrow version: 10.0.0 - Pandas version: 1.3.5
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1,660,944,807
I_kwDODunzps5jAAGn
5,726
Fallback JSON Dataset loading does not load all values when features specified manually
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[ "Thanks for reporting, @myluki2000.\r\n\r\nI am working on a fix." ]
2023-04-10T15:22:14Z
2023-04-21T06:35:28Z
2023-04-21T06:35:28Z
NONE
null
null
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### Describe the bug The fallback JSON dataset loader located here: https://github.com/huggingface/datasets/blob/1c4ec00511868bd881e84a6f7e0333648d833b8e/src/datasets/packaged_modules/json/json.py#L130-L153 does not load the values of features correctly when features are specified manually and not all features have a value in the first entry of the dataset. I'm pretty sure this is not supposed to be expected bahavior? To fix this you'd have to change this line: https://github.com/huggingface/datasets/blob/1c4ec00511868bd881e84a6f7e0333648d833b8e/src/datasets/packaged_modules/json/json.py#L140 To pass a schema to pyarrow which has the same structure as the features argument passed to the load_dataset() method. ### Steps to reproduce the bug Consider a dataset JSON like this: ``` [ { "instruction": "Do stuff", "output": "Answer stuff" }, { "instruction": "Do stuff2", "input": "Additional Input2", "output": "Answer stuff2" } ] ``` Using this code to load the dataset: ``` from datasets import load_dataset, Features, Value features = { "instruction": Value("string"), "input": Value("string"), "output": Value("string") } features = Features(features) ds = load_dataset("json", data_files="./ds.json", features=features) for row in ds["train"]: print(row) ``` we get a dataset that looks like this: | **Instruction** | **Input** | **Output** | |-----------------|--------------------|-----------------| | "Do stuff" | None | "Answer Stuff" | | "Do stuff2" | None | "Answer Stuff2" | ### Expected behavior The input column should contain values other than None for dataset entries that have the "input" attribute set: | **Instruction** | **Input** | **Output** | |-----------------|--------------------|-----------------| | "Do stuff" | None | "Answer Stuff" | | "Do stuff2" | "Additional Input2" | "Answer Stuff2" | ### Environment info Python 3.10.10 Datasets 2.11.0 Windows 10
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fix: 🐛 change string format to allow copy/paste to work in bash
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2021-07-21T15:30:40Z
2021-07-22T10:41:47Z
2021-07-22T10:41:47Z
CONTRIBUTOR
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Before: copy/paste resulted in an error because the square bracket characters `[]` are special characters in bash
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made suggested changes to hate-speech-and-offensive-language
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2020-12-23T23:25:32Z
2020-12-28T10:11:20Z
2020-12-28T10:11:20Z
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I_kwDODunzps5RUcXh
4,944
larger dataset, larger GPU memory in the training phase? Is that correct?
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[ "does the trainer save it in GPU? sooo curious... how to fix it", "It's my bad. didn't limit the input length" ]
2022-09-07T08:46:30Z
2022-09-07T12:34:58Z
2022-09-07T12:34:58Z
NONE
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from datasets import set_caching_enabled set_caching_enabled(False) for ds_name in ["squad","newsqa","nqopen","narrativeqa"]: train_ds = load_from_disk("../../../dall/downstream/processedproqa/{}-train.hf".format(ds_name)) break train_ds = concatenate_datasets([train_ds,train_ds,train_ds,train_ds]) #operation 1 trainer = QuestionAnsweringTrainer( #huggingface trainer model=model, args=training_args, train_dataset=train_ds, eval_dataset= None, eval_examples=None, answer_column_name=answer_column, dataset_name="squad", tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics if training_args.predict_with_generate else None, ) with operation 1, the GPU memory increases from 16G to 23G
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1,574
Diplomacy detection 3
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2020-12-14T23:28:51Z
2020-12-14T23:29:32Z
2020-12-14T23:29:32Z
CONTRIBUTOR
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Add docs for `to_tf_dataset`
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[ "This looks great, thank you!", "Thanks !\r\n\r\nFor some reason the new GIF is 6MB, which is a bit heavy for an image on a website. The previous one was around 200KB though which is perfect. For a good experience we usually expect images to be less than 500KB - otherwise for users with poor connection it takes t...
2021-10-28T20:55:22Z
2021-11-03T15:39:36Z
2021-11-03T10:07:23Z
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This PR adds some documentation for new features released in v1.13.0, with the main addition being `to_tf_dataset`: - Show how to use `to_tf_dataset` in the tutorial, and move `set_format(type='tensorflow'...)` to the Process section (let me know if I'm missing anything @Rocketknight1 😅). - Add an example for loading dataset from multiple zipped CSV files to the Load section. - Add an example for removing columns for an `IterableDataset`. - Add graphic for visualizing streaming.
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