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I_kwDODunzps5bgKkb

CI tests are broken: SchemaInferenceError

closed

MEMBER

CI is broken, raising a `SchemaInferenceError`: see https://github.com/huggingface/datasets/actions/runs/3930901593/jobs/6721492004 ``` FAILED tests/test_beam.py::BeamBuilderTest::test_download_and_prepare_sharded - datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data ``` Stack trace: ``` ______________ BeamBuilderTest.test_download_and_prepare_sharded _______________ [gw1] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python self = <tests.test_beam.BeamBuilderTest testMethod=test_download_and_prepare_sharded> @require_beam def test_download_and_prepare_sharded(self): import apache_beam as beam original_write_parquet = beam.io.parquetio.WriteToParquet expected_num_examples = len(get_test_dummy_examples()) with tempfile.TemporaryDirectory() as tmp_cache_dir: builder = DummyBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner") with patch("apache_beam.io.parquetio.WriteToParquet") as write_parquet_mock: write_parquet_mock.side_effect = partial(original_write_parquet, num_shards=2) > builder.download_and_prepare() tests/test_beam.py:97: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:864: in download_and_prepare **download_and_prepare_kwargs, /opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:1976: in _download_and_prepare num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter)) /opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:694: in finalize shard_num_bytes, _ = parquet_to_arrow(source, destination) /opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:740: in parquet_to_arrow num_bytes, num_examples = writer.finalize() _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <datasets.arrow_writer.ArrowWriter object at 0x7f6dcbb3e810> close_stream = True def finalize(self, close_stream=True): self.write_rows_on_file() # In case current_examples < writer_batch_size, but user uses finalize() if self._check_duplicates: self.check_duplicate_keys() # Re-intializing to empty list for next batch self.hkey_record = [] self.write_examples_on_file() # If schema is known, infer features even if no examples were written if self.pa_writer is None and self.schema: self._build_writer(self.schema) if self.pa_writer is not None: self.pa_writer.close() self.pa_writer = None if close_stream: self.stream.close() else: if close_stream: self.stream.close() > raise SchemaInferenceError("Please pass `features` or at least one example when writing data") E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data /opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:593: SchemaInferenceError ```

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I_kwDODunzps5bd9xa

Sort on multiple keys with datasets.Dataset.sort()

open

NONE

### Feature request From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1 `sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function. The suggested solution: > ... having something similar to pandas and be able to specify multiple columns for sorting. We’re already using pandas under the hood to do the sorting in datasets. The suggested workaround: > convert your dataset to pandas and use `df.sort_values()` ### Motivation Preserved ordering when sorting is very handy when one needs to sort on multiple columns, A and B, so that e.g. whenever A is equal for two or more rows, B is kept sorted. Having a parameter to do this in 🤗datasets would be cleaner than going through pandas and back, and it wouldn't add much complexity to the library. Alternatives: - the possibility to specify multiple keys to sort by with decreasing priority (suggested solution), - the ability to provide a key function for sorting, so that one can manually specify the sorting criteria. ### Your contribution I'll be happy to contribute by submitting a PR. Will get documented on `CONTRIBUTING.MD`. Would love to get thoughts on this, if anyone has anything to add.

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I_kwDODunzps5bdQGE

When applying `ReadInstruction` to custom load it's not DatasetDict but list of Dataset?

open

NONE

### Describe the bug I am loading datasets from custom `tsv` files stored locally and applying split instructions for each split. Although the ReadInstruction is being applied correctly and I was expecting it to be `DatasetDict` but instead it is a list of `Dataset`. ### Steps to reproduce the bug Steps to reproduce the behaviour: 1. Import `from datasets import load_dataset, ReadInstruction` 2. Instruction to load the dataset ``` instructions = [ ReadInstruction(split_name="train", from_=0, to=10, unit='%', rounding='closest'), ReadInstruction(split_name="dev", from_=0, to=10, unit='%', rounding='closest'), ReadInstruction(split_name="test", from_=0, to=5, unit='%', rounding='closest') ] ``` 3. Load `dataset = load_dataset('csv', data_dir="data/", data_files={"train":"train.tsv", "dev":"dev.tsv", "test":"test.tsv"}, delimiter="\t", split=instructions)` ### Expected behavior **Current behaviour**  : **Expected behaviour**  ### Environment info ``datasets==2.8.0 `` `Python==3.8.5 ` `Platform - Ubuntu 20.04.4 LTS`

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I_kwDODunzps5bZZoX

Datasets load error for saved github issues

open

NONE

### Describe the bug Loading a previously downloaded & saved dataset as described in the HuggingFace course: issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train") Gives this error: datasets.builder.DatasetGenerationError: An error occurred while generating the dataset A work-around I found was to use streaming. ### Steps to reproduce the bug Reproduce by executing the code provided: https://huggingface.co/course/chapter5/5?fw=pt From the heading: 'let’s create a function that can download all the issues from a GitHub repository' ### Expected behavior No error ### Environment info Datasets version 2.8.0. Note that version 2.6.1 gives the same error (related to null timestamp). **[EDIT]** This is the complete error trace confirming the issue is related to the timestamp (`Couldn't cast array of type timestamp[s] to null`) ``` Using custom data configuration default-950028611d2860c8 Downloading and preparing dataset json/default to [...]/.cache/huggingface/datasets/json/default-950028611d2860c8/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51... Downloading data files: 100%|██████████| 1/1 [00:00<?, ?it/s] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 500.63it/s] Generating train split: 2619 examples [00:00, 7155.72 examples/s]Traceback (most recent call last): File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1831, in _prepare_split_single writer.write_table(table) File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\arrow_writer.py", line 567, in write_table pa_table = table_cast(pa_table, self._schema) File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2282, in table_cast return cast_table_to_schema(table, schema) File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in cast_array_to_feature arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in <listcomp> arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper return func(array, *args, **kwargs) File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2101, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper return func(array, *args, **kwargs) File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1990, in array_cast raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") TypeError: Couldn't cast array of type timestamp[s] to null The above exception was the direct cause of the following exception: Traceback (most recent call last): File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\pydevconsole.py", line 364, in runcode coro = func() File "<input>", line 1, in <module> File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "[...]\PycharmProjects\TransformersTesting\dataset_issues.py", line 20, in <module> issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train") File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\load.py", line 1757, in load_dataset builder_instance.download_and_prepare( File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 860, in download_and_prepare self._download_and_prepare( File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 953, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1706, in _prepare_split for job_id, done, content in self._prepare_split_single( File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1849, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset Generating train split: 2619 examples [00:19, 7155.72 examples/s] ```

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I_kwDODunzps5bVLYj

Support case-insensitive Hub dataset name in load_dataset

closed

CONTRIBUTOR

### Feature request The dataset name on the Hub is case-insensitive (see https://github.com/huggingface/moon-landing/pull/2399, internal issue), i.e., https://huggingface.co/datasets/GLUE redirects to https://huggingface.co/datasets/glue. Ideally, we could load the glue dataset using the following: ``` from datasets import load_dataset load_dataset('GLUE', 'cola') ``` It breaks because the loading script `GLUE.py` does not exist (`glue.py` should be selected instead). Minor additional comment: in other cases without a loading script, we can load the dataset, but the automatically generated config name depends on the casing: - `load_dataset('severo/danish-wit')` generates the config name `severo--danish-wit-e6fda5b070deb133`, while - `load_dataset('severo/danish-WIT')` generates the config name `severo--danish-WIT-e6fda5b070deb133` ### Motivation To follow the same UX on the Hub and in the datasets library. ### Your contribution ...

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I_kwDODunzps5bUHZq

label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator

open

NONE

### Describe the bug When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default column name is `label` if binary or `label_ids` if multi-class problem. It is required to rename the column accordingly to the expected name : `label` or `label_ids` ### Steps to reproduce the bug ```python from datasets import TextClassification, AutoTokenized, DataCollatorWithPadding ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0')) print(ds_prepared) tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") ds_tokenized = ds_prepared.map(lambda x: tokenizer(x['text'], truncation=True), batched=True) print(ds_tokenized) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") tf_data = model.prepare_tf_dataset(ds_tokenized, shuffle=True, batch_size=16, collate_fn=data_collator) print(tf_data) ``` ### Expected behavior Without renaming the the column, the target column is not in the final tf_data since it is not in the column name expected by the data_collator. To correct this, we have to rename the column: ```python ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0')).rename_column('labels', 'label') ``` ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 - Python version: 3.10.6 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 - `transformers` version: 4.26.0.dev0 - Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.11.1 - PyTorch version (GPU?): not installed (NA) - Tensorflow version (GPU?): 2.11.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in>

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I_kwDODunzps5bM6TQ

Add ProgressBar for `to_parquet`

closed

CONTRIBUTOR

### Feature request Add a progress bar for `Dataset.to_parquet`, similar to how `to_json` works. ### Motivation It's a bit frustrating to not know how long a dataset will take to write to file and if it's stuck or not without a progress bar ### Your contribution Sure I can help if needed

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I_kwDODunzps5bArgd

RuntimeError: Sharding is ambiguous for this dataset

closed

MEMBER

### Describe the bug When loading some datasets, a RuntimeError is raised. For example, for "ami" dataset: https://huggingface.co/datasets/ami/discussions/3 ``` .../huggingface/datasets/src/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size) 1415 fpath = path_join(self._output_dir, fname) 1416 -> 1417 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs) 1418 if num_input_shards <= 1 and num_proc is not None: 1419 logger.warning( .../huggingface/datasets/src/datasets/utils/sharding.py in _number_of_shards_in_gen_kwargs(gen_kwargs) 10 lists_lengths = {key: len(value) for key, value in gen_kwargs.items() if isinstance(value, list)} 11 if len(set(lists_lengths.values())) > 1: ---> 12 raise RuntimeError( 13 ( 14 "Sharding is ambiguous for this dataset: " RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize: - key samples_paths has length 6 - key ids has length 7 - key verification_ids has length 6 To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length. ``` This behavior was introduced when implementing multiprocessing by PR: - #5107 ### Steps to reproduce the bug ```python ds = load_dataset("ami", "microphone-single", split="train", revision="2d7620bb7c3f1aab9f329615c3bdb598069d907a") ``` ### Expected behavior No error raised. ### Environment info Since datasets 2.7.0

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I_kwDODunzps5a8Nm6

Sharding error with Multilingual LibriSpeech

closed

NONE

### Describe the bug Loading the German Multilingual LibriSpeech dataset results in a RuntimeError regarding sharding with the following stacktrace: ``` Downloading and preparing dataset multilingual_librispeech/german to /home/nithin/datadrive/cache/huggingface/datasets/facebook___multilingual_librispeech/german/2.1.0/1904af50f57a5c370c9364cc337699cfe496d4e9edcae6648a96be23086362d0... Downloading data files: 100% 3/3 [00:00<00:00, 107.23it/s] Downloading data files: 100% 1/1 [00:00<00:00, 35.08it/s] Downloading data files: 100% 6/6 [00:00<00:00, 303.36it/s] Downloading data files: 100% 3/3 [00:00<00:00, 130.37it/s] Downloading data files: 100% 1049/1049 [00:00<00:00, 4491.40it/s] Downloading data files: 100% 37/37 [00:00<00:00, 1096.78it/s] Downloading data files: 100% 40/40 [00:00<00:00, 1003.93it/s] Extracting data files: 100% 3/3 [00:11<00:00, 2.62s/it] Generating train split: 469942/0 [34:13<00:00, 273.21 examples/s] Output exceeds the size limit. Open the full output data in a text editor --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-14-74fa6d092bdc> in <module> ----> 1 mls = load_dataset(MLS_DATASET, 2 LANGUAGE, 3 cache_dir="~/datadrive/cache/huggingface/datasets", 4 ignore_verifications=True) /anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs) 1755 1756 # Download and prepare data -> 1757 builder_instance.download_and_prepare( 1758 download_config=download_config, 1759 download_mode=download_mode, /anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 858 if num_proc is not None: 859 prepare_split_kwargs["num_proc"] = num_proc --> 860 self._download_and_prepare( 861 dl_manager=dl_manager, 862 verify_infos=verify_infos, /anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs) 1609 1610 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs): ... RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize: - key audio_archives has length 1049 - key local_extracted_archive has length 1049 - key limited_ids_paths has length 1 To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length. ``` ### Steps to reproduce the bug Here is the code to reproduce it: ```python from datasets import load_dataset MLS_DATASET = "facebook/multilingual_librispeech" LANGUAGE = "german" mls = load_dataset(MLS_DATASET, LANGUAGE, cache_dir="~/datadrive/cache/huggingface/datasets", ignore_verifications=True) ``` ### Expected behavior The expected behaviour is that the dataset is successfully loaded. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.4.0-1094-azure-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyArrow version: 10.0.1 - Pandas version: 1.2.4

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I_kwDODunzps5a32zd

concatenate_datasets fails when two dataset with shards > 1 and unequal shard numbers

closed

NONE

### Describe the bug When using `concatenate_datasets([dataset1, dataset2], axis = 1)` to concatenate two datasets with shards > 1, it fails: ``` File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/combine.py", line 182, in concatenate_datasets return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5499, in _concatenate_map_style_datasets table = concat_tables([dset._data for dset in dsets], axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1778, in concat_tables return ConcatenationTable.from_tables(tables, axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1483, in from_tables blocks = _extend_blocks(blocks, table_blocks, axis=axis) File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1477, in _extend_blocks result[i].extend(row_blocks) IndexError: list index out of range ``` ### Steps to reproduce the bug dataset = concatenate_datasets([dataset1, dataset2], axis = 1) ### Expected behavior The datasets are correctly concatenated. ### Environment info datasets==2.8.0

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I_kwDODunzps5a2jad

load_dataset() cannot find dataset_info.json with multiple training runs in parallel

closed

NONE

### Describe the bug I have a custom local dataset in JSON form. I am trying to do multiple training runs in parallel. The first training run runs with no issue. However, when I start another run on another GPU, the following code throws this error. If there is a workaround to ignore the cache I think that would solve my problem too. I am using datasets version 2.8.0. ### Steps to reproduce the bug 1. Start training run of GPU 0 loading dataset from ``` load_dataset( "json", data_files=tr_dataset_path, split=f"train", download_mode="force_redownload", ) ``` 2. While GPU 0 is training, start an identical run on GPU 1. GPU 1 will produce the following error: ``` Traceback (most recent call last): File "/local-scratch1/data/mt/code/qq/train.py", line 198, in <module> main() File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1130, in __call__ return self.main(*args, **kwargs) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1055, in main rv = self.invoke(ctx) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 760, in invoke return __callback(*args, **kwargs) File "/local-scratch1/data/mt/code/qq/train.py", line 113, in main load_dataset( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1734, in load_dataset builder_instance = load_dataset_builder( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1518, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/builder.py", line 366, in __init__ self.info = DatasetInfo.from_directory(self._cache_dir) File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/info.py", line 313, in from_directory with fs.open(path_join(dataset_info_dir, config.DATASET_INFO_FILENAME), "r", encoding="utf-8") as f: File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1094, in open self.open( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1106, in open f = self._open( File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 175, in _open return LocalFileOpener(path, mode, fs=self, **kwargs) File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 273, in __init__ self._open() File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 278, in _open self.f = open(self.path, mode=self.mode) FileNotFoundError: [Errno 2] No such file or directory: '/home/username/.cache/huggingface/datasets/json/default-43d06a4aedb25e6d/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/dataset_info.json' ``` ### Expected behavior Expected behavior: 2nd GPU training run should run the same as 1st GPU training run. ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.8.0 - Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.10 - Python version: 3.8.15 - PyArrow version: 9.0.0 - Pandas version: 1.5.2

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I_kwDODunzps5al7FA

dataset map function could not be hash properly

closed

NONE

### Describe the bug I follow the [blog post](https://huggingface.co/blog/fine-tune-whisper#building-a-demo) to finetune a Cantonese transcribe model. When using map function to prepare dataset, following warning pop out: `common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=1)` > Parameter 'function'=<function prepare_dataset at 0x000001D1D9D79A60> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. I read https://github.com/huggingface/datasets/issues/4521 and https://github.com/huggingface/datasets/issues/3178 but cannot solve the issue. ### Steps to reproduce the bug ```python from datasets import load_dataset, DatasetDict common_voice = DatasetDict() common_voice["train"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK", split="train+validation") common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK", split="test") common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"]) from transformers import WhisperFeatureExtractor, WhisperTokenizer, WhisperProcessor feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small") tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="chinese", task="transcribe") processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="chinese", task="transcribe") from datasets import Audio common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000)) def prepare_dataset(batch): # load and resample audio data from 48 to 16kHz audio = batch["audio"] # compute log-Mel input features from input audio array batch["input_features"] = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0] # encode target text to label ids batch["labels"] = tokenizer(batch["sentence"]).input_ids return batch common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=1) ``` ### Expected behavior Should be no warning shown. ### Environment info - `datasets` version: 2.7.0 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.9.12 - PyArrow version: 8.0.0 - Pandas version: 1.3.5 - dill version: 0.3.4 - multiprocess version: 0.70.12.2

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I_kwDODunzps5alkRh

Datasets.from_sql() generates deprecation warning

closed

NONE

### Describe the bug Calling `Datasets.from_sql()` generates a warning: `.../site-packages/datasets/builder.py:712: FutureWarning: 'use_auth_token' was deprecated in version 2.7.1 and will be removed in 3.0.0. Pass 'use_auth_token' to the initializer/'load_dataset_builder' instead.` ### Steps to reproduce the bug Any valid call to `Datasets.from_sql()` will produce the deprecation warning. ### Expected behavior No warning. The fix should be simply to remove the parameter `use_auth_token` from the call to `builder.download_and_prepare()` at line 43 of `io/sql.py` (it is set to `None` anyway, and is not needed). ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-4.15.0-169-generic-x86_64-with-glibc2.27 - Python version: 3.9.15 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

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I_kwDODunzps5ajD7A

[2.6.1][2.7.0] Upgrade `datasets` to fix `TypeError: can only concatenate str (not "int") to str`

open

MEMBER

`datasets` 2.6.1 and 2.7.0 started to stop supporting datasets like IMDB, ConLL or MNIST datasets. When loading a dataset using 2.6.1 or 2.7.0, you may this error when loading certain datasets: ```python TypeError: can only concatenate str (not "int") to str ``` This is because we started to update the metadata of those datasets to a format that is not supported in 2.6.1 and 2.7.0 This change is required or those datasets won't be supported by the Hugging Face Hub. Therefore if you encounter this error or if you're using `datasets` 2.6.1 or 2.7.0, we encourage you to update to a newer version. For example, versions 2.6.2 and 2.7.1 patch this issue. ```python pip install -U datasets ``` All the datasets affected are the ones with a ClassLabel feature type and YAML "dataset_info" metadata. More info [here](https://github.com/huggingface/datasets/issues/5275). We apologize for the inconvenience.

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I_kwDODunzps5aeQBa

size_in_bytes the same for all splits

open

NONE

### Describe the bug Hi, it looks like whenever you pull a dataset and get size_in_bytes, it returns the same size for all splits (and that size is the combined size of all splits). It seems like this shouldn't be the intended behavior since it is misleading. Here's an example: ``` >>> from datasets import load_dataset >>> x = load_dataset("glue", "wnli") Found cached dataset glue (/Users/breakend/.cache/huggingface/datasets/glue/wnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad) 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1097.70it/s] >>> x["train"].size_in_bytes 186159 >>> x["validation"].size_in_bytes 186159 >>> x["test"].size_in_bytes 186159 >>> ``` ### Steps to reproduce the bug ``` >>> from datasets import load_dataset >>> x = load_dataset("glue", "wnli") >>> x["train"].size_in_bytes 186159 >>> x["validation"].size_in_bytes 186159 >>> x["test"].size_in_bytes 186159 ``` ### Expected behavior The expected behavior is that it should return the separate sizes for all splits. ### Environment info - `datasets` version: 2.7.1 - Platform: macOS-12.5-arm64-arm-64bit - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

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I_kwDODunzps5adDl7

Better integration of BIG-bench

open

MEMBER

### Feature request Ideally, it would be nice to have a maintained PyPI package for `bigbench`. ### Motivation We'd like to allow anyone to access, explore and use any task. ### Your contribution @lhoestq has opened an issue in their repo: - https://github.com/google/BIG-bench/issues/906

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I_kwDODunzps5acdSV

Missing state.json when creating a cloud dataset using a dataset_builder

open

NONE

### Describe the bug Using `load_dataset_builder` to create a builder, run `download_and_prepare` do upload it to S3. However when trying to load it, there are missing `state.json` files. Complete example: ```python from aiobotocore.session import AioSession as Session from datasets import load_from_disk, load_datase, load_dataset_builder import s3fs storage_options = {"session": Session()} fs = s3fs.S3FileSystem(**storage_options) output_dir = "s3://bucket/imdb" builder = load_dataset_builder("imdb") builder.download_and_prepare(output_dir, storage_options=storage_options) load_from_disk(output_dir, fs=fs) # ERROR # [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json' ``` As a comparison, if you use the non lazy `load_dataset`, it works and the S3 folder has different structure + state.json files. Example: ```python from aiobotocore.session import AioSession as Session from datasets import load_from_disk, load_dataset, load_dataset_builder import s3fs storage_options = {"session": Session()} fs = s3fs.S3FileSystem(**storage_options) output_dir = "s3://bucket/imdb" dataset = load_dataset("imdb",) dataset.save_to_disk(output_dir, fs=fs) load_from_disk(output_dir, fs=fs) # WORKS ``` You still want the 1st option for the laziness and the parquet conversion. Thanks! ### Steps to reproduce the bug ```python from aiobotocore.session import AioSession as Session from datasets import load_from_disk, load_datase, load_dataset_builder import s3fs storage_options = {"session": Session()} fs = s3fs.S3FileSystem(**storage_options) output_dir = "s3://bucket/imdb" builder = load_dataset_builder("imdb") builder.download_and_prepare(output_dir, storage_options=storage_options) load_from_disk(output_dir, fs=fs) # ERROR # [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json' ``` BTW, you need the AioSession as s3fs is now based on aiobotocore, see https://github.com/fsspec/s3fs/issues/385. ### Expected behavior Expected to be able to load the dataset from S3. ### Environment info ``` s3fs 2022.11.0 s3transfer 0.6.0 datasets 2.8.0 aiobotocore 2.4.2 boto3 1.24.59 botocore 1.27.59 ``` python 3.7.15.

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I_kwDODunzps5aVW8L

Got disconnected from remote data host. Retrying in 5sec [2/20]

closed

NONE

### Describe the bug While trying to upload my image dataset of a CSV file type to huggingface by running the below code. The dataset consists of a little over 100k of image-caption pairs ### Steps to reproduce the bug ``` df = pd.read_csv('x.csv', encoding='utf-8-sig') features = Features({ 'link': Image(decode=True), 'caption': Value(dtype='string'), }) #make sure u r logged in to HF ds = Dataset.from_pandas(df, features=features) ds.features ds.push_to_hub("x/x") ``` I got the below error and It always stops at the same progress ``` 100%|██████████| 4/4 [23:53<00:00, 358.48s/ba] 100%|██████████| 4/4 [24:37<00:00, 369.47s/ba]%|▍ | 1/22 [00:06<02:09, 6.16s/it] 100%|██████████| 4/4 [25:00<00:00, 375.15s/ba]%|▉ | 2/22 [25:54<2:36:15, 468.80s/it] 100%|██████████| 4/4 [24:53<00:00, 373.29s/ba]%|█▎ | 3/22 [51:01<4:07:07, 780.39s/it] 100%|██████████| 4/4 [24:01<00:00, 360.34s/ba]%|█▊ | 4/22 [1:17:00<5:04:07, 1013.74s/it] 100%|██████████| 4/4 [23:59<00:00, 359.91s/ba]%|██▎ | 5/22 [1:41:07<5:24:06, 1143.90s/it] 100%|██████████| 4/4 [24:16<00:00, 364.06s/ba]%|██▋ | 6/22 [2:05:14<5:29:15, 1234.74s/it] 100%|██████████| 4/4 [25:24<00:00, 381.10s/ba]%|███▏ | 7/22 [2:29:38<5:25:52, 1303.52s/it] 100%|██████████| 4/4 [25:24<00:00, 381.24s/ba]%|███▋ | 8/22 [2:56:02<5:23:46, 1387.58s/it] 100%|██████████| 4/4 [25:08<00:00, 377.23s/ba]%|████ | 9/22 [3:22:24<5:13:17, 1445.97s/it] 100%|██████████| 4/4 [24:11<00:00, 362.87s/ba]%|████▌ | 10/22 [3:48:24<4:56:02, 1480.19s/it] 100%|██████████| 4/4 [24:44<00:00, 371.11s/ba]%|█████ | 11/22 [4:12:42<4:30:10, 1473.66s/it] 100%|██████████| 4/4 [24:35<00:00, 368.81s/ba]%|█████▍ | 12/22 [4:37:34<4:06:29, 1478.98s/it] 100%|██████████| 4/4 [24:02<00:00, 360.67s/ba]%|█████▉ | 13/22 [5:03:24<3:45:04, 1500.45s/it] 100%|██████████| 4/4 [24:07<00:00, 361.78s/ba]%|██████▎ | 14/22 [5:27:33<3:17:59, 1484.97s/it] 100%|██████████| 4/4 [23:39<00:00, 354.85s/ba]%|██████▊ | 15/22 [5:51:48<2:52:10, 1475.82s/it] Pushing dataset shards to the dataset hub: 73%|███████▎ | 16/22 [6:16:58<2:28:37, 1486.31s/it]Got disconnected from remote data host. Retrying in 5sec [1/20] Got disconnected from remote data host. Retrying in 5sec [2/20] Got disconnected from remote data host. Retrying in 5sec [3/20] Got disconnected from remote data host. Retrying in 5sec [4/20] Got disconnected from remote data host. Retrying in 5sec [5/20] Got disconnected from remote data host. Retrying in 5sec [6/20] Got disconnected from remote data host. Retrying in 5sec [7/20] Got disconnected from remote data host. Retrying in 5sec [8/20] Got disconnected from remote data host. Retrying in 5sec [9/20] ... Got disconnected from remote data host. Retrying in 5sec [19/20] Got disconnected from remote data host. Retrying in 5sec [20/20] 75%|███████▌ | 3/4 [24:47<08:15, 495.86s/ba] Pushing dataset shards to the dataset hub: 73%|███████▎ | 16/22 [6:41:46<2:30:39, 1506.65s/it] Output exceeds the size limit. Open the full output data in a text editor --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-1-dbf8530779e9> in <module> 16 ds.features ``` ### Expected behavior I was trying to upload an image dataset and expected it to be fully uploaded ### Environment info - `datasets` version: 2.8.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.9 - PyArrow version: 10.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5aREuP

Unpin pydantic

closed

MEMBER

Once `pydantic` fixes their issue in their 1.10.3 version, unpin it. See issue: - #5394 See temporary fix: - #5395

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I_kwDODunzps5aPXGl

CI error: TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'

closed

MEMBER

### Describe the bug While installing the dependencies, the CI raises a TypeError: ``` Traceback (most recent call last): File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 183, in _run_module_as_main mod_name, mod_spec, code = _get_module_details(mod_name, _Error) File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 142, in _get_module_details return _get_module_details(pkg_main_name, error) File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 109, in _get_module_details __import__(pkg_name) File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/__init__.py", line 6, in <module> from .errors import setup_default_warnings File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/errors.py", line 2, in <module> from .compat import Literal File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/compat.py", line 3, in <module> from thinc.util import copy_array File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/__init__.py", line 5, in <module> from .config import registry File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/config.py", line 2, in <module> import confection File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/confection/__init__.py", line 10, in <module> from pydantic import BaseModel, create_model, ValidationError, Extra File "pydantic/__init__.py", line 2, in init pydantic.__init__ File "pydantic/dataclasses.py", line 46, in init pydantic.dataclasses # | None | Attribute is set to None. | File "pydantic/main.py", line 121, in init pydantic.main TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers' ``` See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565 ### Steps to reproduce the bug ```shell pip install .[tests,metrics-tests] python -m spacy download en_core_web_sm ``` ### Expected behavior No error. ### Environment info See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565

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I_kwDODunzps5aBh5A

Whisper Event - RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it]

open

NONE

Done in a VM with a GPU (Ubuntu) following the [Whisper Event - PYTHON](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#python-script) instructions. Attempted using [RuntimeError: he size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 - WEB](https://discuss.huggingface.co/t/trainer-runtimeerror-the-size-of-tensor-a-462-must-match-the-size-of-tensor-b-448-at-non-singleton-dimension-1/26010/10 ) - another person experiencing the same issue. But could not resolve the issue with the google/fleurs data. __Not clear what can be modified in the PY code to resolve the input data size mismatch, as the training data is already very small__. Tried posting on Discord, @sanchit-gandhi and @vaibhavs10. Was hoping that the event is over and some input/help is now available. [Hugging Face - whisper-small-amet](https://huggingface.co/drmeeseeks/whisper-small-amet). The paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) am_et is a low resource language (Table E), with the WER results ranging from 120-229, based on model size. (Whisper small WER=120.2). # ---> Initial Training Output /usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn( [INFO|trainer.py:1641] 2022-12-18 05:23:28,799 >> ***** Running training ***** [INFO|trainer.py:1642] 2022-12-18 05:23:28,799 >> Num examples = 446 [INFO|trainer.py:1643] 2022-12-18 05:23:28,799 >> Num Epochs = 72 [INFO|trainer.py:1644] 2022-12-18 05:23:28,799 >> Instantaneous batch size per device = 16 [INFO|trainer.py:1645] 2022-12-18 05:23:28,799 >> Total train batch size (w. parallel, distributed & accumulation) = 32 [INFO|trainer.py:1646] 2022-12-18 05:23:28,799 >> Gradient Accumulation steps = 2 [INFO|trainer.py:1647] 2022-12-18 05:23:28,800 >> Total optimization steps = 1000 [INFO|trainer.py:1648] 2022-12-18 05:23:28,801 >> Number of trainable parameters = 241734912 # ---> Error 14% 9/65 [07:07<48:34, 52.04s/it][INFO|configuration_utils.py:523] 2022-12-18 05:03:07,941 >> Generate config GenerationConfig { "begin_suppress_tokens": [ 220, 50257 ], "bos_token_id": 50257, "decoder_start_token_id": 50258, "eos_token_id": 50257, "max_length": 448, "pad_token_id": 50257, "transformers_version": "4.26.0.dev0", "use_cache": false } Traceback (most recent call last): File "run_speech_recognition_seq2seq_streaming.py", line 629, in <module> main() File "run_speech_recognition_seq2seq_streaming.py", line 578, in main train_result = trainer.train(resume_from_checkpoint=checkpoint) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1534, in train return inner_training_loop( File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1859, in _inner_training_loop self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2122, in _maybe_log_save_evaluate metrics = self.evaluate(ignore_keys=ignore_keys_for_eval) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 78, in evaluate return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2818, in evaluate output = eval_loop( File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 3000, in evaluation_loop loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys) File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 213, in prediction_step outputs = model(**inputs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1197, in forward outputs = self.model( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1066, in forward decoder_outputs = self.decoder( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 873, in forward hidden_states = inputs_embeds + positions RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it]

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I_kwDODunzps5Z9vfx

Error when pushing to the CI hub

closed

CONTRIBUTOR

### Describe the bug Note that it's a special case where the Hub URL is "https://hub-ci.huggingface.co", which does not appear if we do the same on the Hub (https://huggingface.co). The call to `dataset.push_to_hub(` fails: ``` Pushing dataset shards to the dataset hub: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.93s/it] Traceback (most recent call last): File "reproduce_hubci.py", line 16, in <module> dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True) File "/home/slesage/hf/datasets/src/datasets/arrow_dataset.py", line 5025, in push_to_hub HfApi(endpoint=config.HF_ENDPOINT).upload_file( File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1346, in upload_file raise err File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1337, in upload_file r.raise_for_status() File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_DATASETS_SERVER_USER__/bug-16718047265472/upload/main/README.md ``` ### Steps to reproduce the bug ```python # reproduce.py from datasets import Dataset import time USER = "__DUMMY_DATASETS_SERVER_USER__" USER_TOKEN = "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" dataset = Dataset.from_dict({"a": [1, 2, 3]}) repo_id = f"{USER}/bug-{int(time.time() * 10e3)}" dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True) ``` ```bash $ HF_ENDPOINT="https://hub-ci.huggingface.co" python reproduce.py ``` ### Expected behavior No error and the dataset should be uploaded to the Hub with the README file (which generates the error). ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.15.0-1026-aws-x86_64-with-glibc2.35 - Python version: 3.9.15 - PyArrow version: 7.0.0 - Pandas version: 1.5.2

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I_kwDODunzps5Z8iis

Getting Value Error while loading a dataset..

closed

NONE

### Describe the bug I am trying to load a dataset using Hugging Face Datasets load_dataset method. I am getting the value error as show below. Can someone help with this? I am using Windows laptop and Google Colab notebook. ``` WARNING:datasets.builder:Using custom data configuration default-a1d9e8eaedd958cd --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [<ipython-input-12-5b4fdcb8e6d5>](https://localhost:8080/#) in <module> 6 ) 7 ----> 8 next(iter(law_dataset_streamed)) 17 frames [/usr/local/lib/python3.8/dist-packages/fsspec/core.py](https://localhost:8080/#) in get_compression(urlpath, compression) 485 compression = infer_compression(urlpath) 486 if compression is not None and compression not in compr: --> 487 raise ValueError("Compression type %s not supported" % compression) 488 return compression 489 ValueError: Compression type zstd not supported ``` ### Steps to reproduce the bug ``` !pip install zstandard from datasets import load_dataset lds = load_dataset( "json", data_files="https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst", split="train", streaming=True, ) ``` ### Expected behavior I expect an iterable object as the output 'lds' to be created. ### Environment info Windows laptop with Google Colab notebook

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I_kwDODunzps5Z7YxR

Missing documentation page : improve-performance

closed

NONE

### Describe the bug Trying to access https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/cache#improve-performance, the page is missing. The link is in here : https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/loading_methods#datasets.load_dataset.keep_in_memory ### Steps to reproduce the bug Access the page and see it's missing. ### Expected behavior Not missing page ### Environment info Doesn't matter

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I_kwDODunzps5Z600W

`max_shard_size` in `datasets.push_to_hub()` breaks with large files

closed

NONE

### Describe the bug `max_shard_size` parameter for `datasets.push_to_hub()` works unreliably with large files, generating shard files that are way past the specified limit. In my private dataset, which contains unprocessed images of all sizes (up to `~100MB` per file), I've encountered cases where `max_shard_size='100MB'` results in shard files that are `>2GB` in size. Setting `max_shard_size` to another value, such as `1GB` or `500MB` does not fix this problem. **The real problem is this:** When the shard file size grows too big, the entire dataset breaks because of #4721 and ultimately https://issues.apache.org/jira/browse/ARROW-5030. Since `max_shard_size` does not let one accurately control the size of the shard files, it becomes very easy to build a large dataset without any warnings that it will be broken -- even when you think you are mitigating this problem by setting `max_shard_size`. ``` File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/builder.py", line 1763, in _prepare_split_single for _, table in generator: File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/packaged_modules/parquet/parquet.py", line 69, in _generate_tables for batch_idx, record_batch in enumerate( File "pyarrow/_parquet.pyx", line 1323, in iter_batches File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs ``` ### Steps to reproduce the bug 1. Clone [example repo](https://github.com/salieri/hf-dataset-shard-size-bug) 2. Follow steps in [README.md](https://github.com/salieri/hf-dataset-shard-size-bug/blob/main/README.md) 3. After uploading the dataset, you will see that the shard file size varies between `30MB` and `200MB` -- way beyond the `max_shard_size='75MB'` limit (example: `train-00003-of-00131...` is `155MB` in [here](https://huggingface.co/datasets/slri/shard-size-test/tree/main/data)) (Note that this example repo does not generate shard files that are so large that they would trigger #4721) ### Expected behavior The shard file size should remain below or equal to `max_shard_size`. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.10.157-139.675.amzn2.aarch64-aarch64-with-glibc2.17 - Python version: 3.7.15 - PyArrow version: 10.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5Z6mzs

Is `fs=` deprecated in `load_from_disk()` as well?

closed

CONTRIBUTOR

### Describe the bug The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec: https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340 Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ? https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779 ### Steps to reproduce the bug n/a ### Expected behavior n/a ### Environment info n/a

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I_kwDODunzps5Z13sQ

IterableDataset missing column_names, differs from Dataset interface

open

NONE

### Describe the bug The documentation on [Stream](https://huggingface.co/docs/datasets/v1.18.2/stream.html) seems to imply that IterableDataset behaves just like a Dataset. However, examples like ``` dataset.map(augment_data, batched=True, remove_columns=dataset.column_names, ...) ``` will not work because `.column_names` does not exist on IterableDataset. I cannot find any clear explanation on why this is not available, is it an oversight? We do have `iterable_ds.features` available. ### Steps to reproduce the bug See above ### Expected behavior Dataset and IterableDataset would be expected to have the same interface, with any differences noted in the documentation. ### Environment info n/a

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I_kwDODunzps5ZrNLT

Wrong URL for the_pile dataset

open

NONE

### Describe the bug When trying to load `the_pile` dataset from the library, I get a `FileNotFound` error. ### Steps to reproduce the bug Steps to reproduce: Run: ``` from datasets import load_dataset dataset = load_dataset("the_pile") ``` I get the output: "name": "FileNotFoundError", "message": "Unable to resolve any data file that matches '['**']' at /storage/store/work/lgrinszt/memorization/the_pile 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', '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', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ircam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'OGG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']" ### Expected behavior `the_pile` dataset should be dowloaded. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-4.15.0-112-generic-x86_64-with-glibc2.27 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

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I_kwDODunzps5Zq2JL

Improve dataset `.skip()` speed in streaming mode

open

CONTRIBUTOR

### Feature request Add extra information to the `dataset_infos.json` file to include the number of samples/examples in each shard, for example in a new field `num_examples` alongside `num_bytes`. The `.skip()` function could use this information to ignore the download of a shard when in streaming mode, which AFAICT it should speed up the skipping process. ### Motivation When resuming from a checkpoint after a crashed run, using `dataset.skip()` is very convenient to recover the exact state of the data and to not train again over the same examples (assuming same seed, no shuffling). However, I have noticed that for audio datasets in streaming mode this is very costly in terms of time, as shards need to be downloaded every time before skipping the right number of examples. ### Your contribution I took a look already at the code, but it seems a change like this is way deeper than I am able to manage, as it touches the library in several parts. I could give it a try but might need some guidance on the internals.

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I_kwDODunzps5Zo4CU

The dataset "the_pile", subset "enron_emails" , load_dataset() failure

closed

NONE

### Describe the bug When run "datasets.load_dataset("the_pile","enron_emails")" failure  ### Steps to reproduce the bug Run below code in python cli: >>> import datasets >>> datasets.load_dataset("the_pile","enron_emails") ### Expected behavior Load dataset "the_pile", "enron_emails" successfully. ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.7.1 - Platform: Linux-5.15.0-53-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - PyArrow version: 10.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5ZhMMx

Using too many threads results in: Got disconnected from remote data host. Retrying in 5sec

open

CONTRIBUTOR

### Describe the bug `streaming_download_manager` seems to disconnect if too many runs access the same underlying dataset 🧐 The code works fine for me if I have ~100 runs in parallel, but disconnects once scaling to 200. Possibly related: - https://github.com/huggingface/datasets/pull/3100 - https://github.com/huggingface/datasets/pull/3050 ### Steps to reproduce the bug Running ```python c4 = datasets.load_dataset("c4", "en", split="train", streaming=True).skip(args.start).take(args.end-args.start) df = pd.DataFrame(c4, index=None) ``` with different start & end arguments on 200 CPUs in parallel yields: ``` WARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4. WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [1/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [2/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [3/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [4/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [5/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [6/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [7/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [8/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [9/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [10/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [11/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [12/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [13/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [14/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [15/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [16/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [17/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [18/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [19/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [20/20] ╭───────────────────── Traceback (most recent call last) ──────────────────────╮ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/dec-2022-tasky/inference │ │ _c4.py:68 in <module> │ │ │ │ 65 │ model.eval() │ │ 66 │ │ │ 67 │ c4 = datasets.load_dataset("c4", "en", split="train", streaming=Tru │ │ ❱ 68 │ df = pd.DataFrame(c4, index=None) │ │ 69 │ texts = df["text"].to_list() │ │ 70 │ preds = batch_inference(texts, batch_size=args.batch_size) │ │ 71 │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/site-packages/pandas/core/frame.p │ │ y:684 in __init__ │ │ │ │ 681 │ │ # For data is list-like, or Iterable (will consume into list │ │ 682 │ │ elif is_list_like(data): │ │ 683 │ │ │ if not isinstance(data, (abc.Sequence, ExtensionArray)): │ │ ❱ 684 │ │ │ │ data = list(data) │ │ 685 │ │ │ if len(data) > 0: │ │ 686 │ │ │ │ if is_dataclass(data[0]): │ │ 687 │ │ │ │ │ data = dataclasses_to_dicts(data) │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:751 in __iter__ │ │ │ │ 748 │ │ yield from ex_iterable.shard_data_sources(shard_idx) │ │ 749 │ │ │ 750 │ def __iter__(self): │ │ ❱ 751 │ │ for key, example in self._iter(): │ │ 752 │ │ │ if self.features: │ │ 753 │ │ │ │ # `IterableDataset` automatically fills missing colum │ │ 754 │ │ │ │ # This is done with `_apply_feature_types`. │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:741 in _iter │ │ │ │ 738 │ │ │ ex_iterable = self._ex_iterable.shuffle_data_sources(self │ │ 739 │ │ else: │ │ 740 │ │ │ ex_iterable = self._ex_iterable │ │ ❱ 741 │ │ yield from ex_iterable │ │ 742 │ │ │ 743 │ def _iter_shard(self, shard_idx: int): │ │ 744 │ │ if self._shuffling: │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:617 in __iter__ │ │ │ │ 614 │ │ self.n = n │ │ 615 │ │ │ 616 │ def __iter__(self): │ │ ❱ 617 │ │ yield from islice(self.ex_iterable, self.n) │ │ 618 │ │ │ 619 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │ │ 620 │ │ """Doesn't shuffle the wrapped examples iterable since it wou │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:594 in __iter__ │ │ │ │ 591 │ │ │ 592 │ def __iter__(self): │ │ 593 │ │ #ex_iterator = iter(self.ex_iterable) │ │ ❱ 594 │ │ yield from islice(self.ex_iterable, self.n, None) │ │ 595 │ │ #for _ in range(self.n): │ │ 596 │ │ # next(ex_iterator) │ │ 597 │ │ #yield from islice(ex_iterator, self.n, None) │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:106 in __iter__ │ │ │ │ 103 │ │ self.kwargs = kwargs │ │ 104 │ │ │ 105 │ def __iter__(self): │ │ ❱ 106 │ │ yield from self.generate_examples_fn(**self.kwargs) │ │ 107 │ │ │ 108 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │ │ 109 │ │ return ShardShuffledExamplesIterable(self.generate_examples_f │ │ │ │ /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/d │ │ f532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01/c4.py:89 in │ │ _generate_examples │ │ │ │ 86 │ │ for filepath in filepaths: │ │ 87 │ │ │ logger.info("generating examples from = %s", filepath) │ │ 88 │ │ │ with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8" │ │ ❱ 89 │ │ │ │ for line in f: │ │ 90 │ │ │ │ │ if line: │ │ 91 │ │ │ │ │ │ example = json.loads(line) │ │ 92 │ │ │ │ │ │ yield id_, example │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:313 in read1 │ │ │ │ 310 │ │ │ │ 311 │ │ if size < 0: │ │ 312 │ │ │ size = io.DEFAULT_BUFFER_SIZE │ │ ❱ 313 │ │ return self._buffer.read1(size) │ │ 314 │ │ │ 315 │ def peek(self, n): │ │ 316 │ │ self._check_not_closed() │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/_compression.py:68 in readinto │ │ │ │ 65 │ │ │ 66 │ def readinto(self, b): │ │ 67 │ │ with memoryview(b) as view, view.cast("B") as byte_view: │ │ ❱ 68 │ │ │ data = self.read(len(byte_view)) │ │ 69 │ │ │ byte_view[:len(data)] = data │ │ 70 │ │ return len(data) │ │ 71 │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:493 in read │ │ │ │ 490 │ │ │ │ self._new_member = False │ │ 491 │ │ │ │ │ 492 │ │ │ # Read a chunk of data from the file │ │ ❱ 493 │ │ │ buf = self._fp.read(io.DEFAULT_BUFFER_SIZE) │ │ 494 │ │ │ │ │ 495 │ │ │ uncompress = self._decompressor.decompress(buf, size) │ │ 496 │ │ │ if self._decompressor.unconsumed_tail != b"": │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:96 in read │ │ │ │ 93 │ │ │ read = self._read │ │ 94 │ │ │ self._read = None │ │ 95 │ │ │ return self._buffer[read:] + \ │ │ ❱ 96 │ │ │ │ self.file.read(size-self._length+read) │ │ 97 │ │ │ 98 │ def prepend(self, prepend=b''): │ │ 99 │ │ if self._read is None: │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/download/streaming_download_manager.py: │ │ 365 in read_with_retries │ │ │ │ 362 │ │ │ │ ) │ │ 363 │ │ │ │ time.sleep(config.STREAMING_READ_RETRY_INTERVAL) │ │ 364 │ │ else: │ │ ❱ 365 │ │ │ raise ConnectionError("Server Disconnected") │ │ 366 │ │ return out │ │ 367 │ │ │ 368 │ file_obj.read = read_with_retries │ ╰──────────────────────────────────────────────────────────────────────────────╯ ConnectionError: Server Disconnected ``` ### Expected behavior There should be no disconnect I think. ### Environment info ``` datasets=2.7.0 Python 3.9.12 ```

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I_kwDODunzps5ZfRLM

Add a robustness benchmark dataset for vision

open

MEMBER

### Name ImageNet-C ### Paper Benchmarking Neural Network Robustness to Common Corruptions and Perturbations ### Data https://github.com/hendrycks/robustness ### Motivation It's a known fact that vision models are brittle when they meet with slightly corrupted and perturbed data. This is also correlated to the robustness aspects of vision models. Researchers use different benchmark datasets to evaluate the robustness aspects of vision models. ImageNet-C is one of them. Having this dataset in 🤗 Datasets would allow researchers to evaluate and study the robustness aspects of vision models. Since the metric associated with these evaluations is top-1 accuracy, researchers should be able to easily take advantage of the evaluation benchmarks on the Hub and perform comprehensive reporting. ImageNet-C is a large dataset. Once it's in, it can act as a reference and we can also reach out to the authors of the other robustness benchmark datasets in vision, such as ObjectNet, WILDS, Metashift, etc. These datasets cater to different aspects. For example, ObjectNet is related to assessing how well a model performs under sub-population shifts. Related thread: https://huggingface.slack.com/archives/C036H4A5U8Z/p1669994598060499

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I_kwDODunzps5ZTEe1

Dataset.from_generator() crashes on simple example

closed

NONE

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I_kwDODunzps5ZRDrg

Run 'GPT-J' failure due to download dataset fail (' ConnectionError: Couldn't reach http://eaidata.bmk.sh/data/enron_emails.jsonl.zst ' )

closed

NONE

### Describe the bug Run model "GPT-J" with dataset "the_pile" fail. The fail out is as below:  Looks like which is due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst" unreachable . ### Steps to reproduce the bug Steps to reproduce this issue: git clone https://github.com/huggingface/transformers cd transformers python examples/pytorch/language-modeling/run_clm.py --model_name_or_path EleutherAI/gpt-j-6B --dataset_name the_pile --dataset_config_name enron_emails --do_eval --output_dir /tmp/output --overwrite_output_dir ### Expected behavior This issue looks like due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst " couldn't be reached. Is there another way to download the dataset "the_pile" ? Is there another way to cache the dataset "the_pile" but not let the hg to download it when runtime ? ### Environment info huggingface_hub version: 0.11.1 Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35 Python version: 3.9.12 Running in iPython ?: No Running in notebook ?: No Running in Google Colab ?: No Token path ?: /home/taosy/.huggingface/token Has saved token ?: False Configured git credential helpers: FastAI: N/A Tensorflow: N/A Torch: N/A Jinja2: N/A Graphviz: N/A Pydot: N/A

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I_kwDODunzps5ZPMFh

How concatenate `Audio` elements using batch mapping

open

NONE

### Describe the bug I am trying to do concatenate audios in a dataset e.g. `google/fleurs`. ```python print(dataset) # Dataset({ # features: ['path', 'audio'], # num_rows: 24 # }) def mapper_function(batch): # to merge every 3 audio # np.concatnate(audios[i: i+3]) for i in range(i, len(batch), 3) dataset = dataset.map(mapper_function, batch=True, batch_size=24) print(dataset) # Expected output: # Dataset({ # features: ['path', 'audio'], # num_rows: 8 # }) ``` I tried to construct `result={}` dictionary inside the mapper function, I just found it will not work because it needs `byte` also needed :(( I'd appreciate if your share any use cases similar to my problem or any solutions really. Thanks! cc: @lhoestq ### Steps to reproduce the bug 1. load audio dataset 2. try to merge every k audios and return as one ### Expected behavior Merged dataset with a fewer rows. If we merge every 3 rows, then `n // 3` number of examples. ### Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 8.0.0 - Pandas version: 1.3.5

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I_kwDODunzps5ZOZnp

IterableDataset returns duplicated data using PyTorch DDP

closed

MEMBER

As mentioned in https://github.com/huggingface/datasets/issues/3423, when using PyTorch DDP the dataset ends up with duplicated data. We already check for the PyTorch `worker_info` for single node, but we should also check for `torch.distributed.get_world_size()` and `torch.distributed.get_rank()`

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I_kwDODunzps5Y8MUt

Consider using "Sequence" instead of "List"

open

NONE

### Feature request Hi, please consider using `Sequence` type annotation instead of `List` in function arguments such as in [`Dataset.from_parquet()`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L1088). It leads to type checking errors, see below. **How to reproduce** ```py list_of_filenames = ["foo.parquet", "bar.parquet"] ds = Dataset.from_parquet(list_of_filenames) ``` **Expected mypy output:** ``` Success: no issues found ``` **Actual mypy output:** ```py test.py:19: error: Argument 1 to "from_parquet" of "Dataset" has incompatible type "List[str]"; expected "Union[Union[str, bytes, PathLike[Any]], List[Union[str, bytes, PathLike[Any]]]]" [arg-type] test.py:19: note: "List" is invariant -- see https://mypy.readthedocs.io/en/stable/common_issues.html#variance test.py:19: note: Consider using "Sequence" instead, which is covariant ``` **Env:** mypy 0.991, Python 3.10.0, datasets 2.7.1

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I_kwDODunzps5Y7Eo0

Support remote file systems for `Audio`

closed

NONE

### Feature request Hi there! It would be super cool if `Audio()`, and potentially other features, could read files from a remote file system. ### Motivation Large amounts of data is often stored in buckets. `load_from_disk` is able to retrieve data from cloud storage but to my knowledge actually copies the datasets across first, so if you're working off a system with smaller disk specs (like a VM), you can run out of space very quickly. ### Your contribution Something like this (for Google Cloud Platform in this instance): ```python from datasets import Dataset, Audio import gcsfs fs = gcsfs.GCSFileSystem() list_of_audio_fp = {'audio': ['1', '2', '3']} ds = Dataset.from_dict(list_of_audio_fp) ds = ds.cast_column("audio", Audio(sampling_rate=16000, fs=fs)) ``` Under the hood: ```python import librosa from io import BytesIO def load_audio(fp, sampling_rate=None, fs=None): if fs is not None: with fs.open(fp, 'rb') as f: arr, sr = librosa.load(BytesIO(f), sr=sampling_rate) else: # Perform existing io operations ``` Written from memory so some things could be wrong.

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I_kwDODunzps5Y279-

__init__() got an unexpected keyword argument 'input_size'

open

NONE

### Describe the bug I try to define a custom configuration with a input_size attribute following the instructions by "Specifying several dataset configurations" in https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html But when I load the dataset, I got an error "__init__() got an unexpected keyword argument 'input_size'" ### Steps to reproduce the bug Following is the code to define the dataset: class CsvConfig(datasets.BuilderConfig): """BuilderConfig for CSV.""" input_size: int = 2048 class MRF(datasets.ArrowBasedBuilder): """Archival MRF data""" BUILDER_CONFIG_CLASS = CsvConfig VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ CsvConfig(name="default", version=VERSION, description="MRF data", input_size=2048), ] ... def _generate_examples(self): input_size = self.config.input_size if input_size > 1000: numin = 10000 else: numin = 15000 Below is the code to load the dataset: reader = load_dataset("default", input_size=1024) ### Expected behavior I hope to pass the "input_size" parameter to MRF datasets, and change "input_size" to any value when loading the datasets. ### Environment info - `datasets` version: 2.5.1 - Platform: Linux-4.18.0-305.3.1.el8.x86_64-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 9.0.0 - Pandas version: 1.5.0

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I_kwDODunzps5Y2aiw

Do we need to implement `_prepare_split`?

closed

NONE

### Describe the bug I'm not sure this is a bug or if it's just missing in the documentation, or i'm not doing something correctly, but I'm subclassing `DatasetBuilder` and getting the following error because on the `DatasetBuilder` class the `_prepare_split` method is abstract (as are the others we are required to implement, hence the genesis of my question): ``` Traceback (most recent call last): File "/home/jason/source/python/prism_machine_learning/examples/create_hf_datasets.py", line 28, in <module> dataset_builder.download_and_prepare() File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 1124, in _prepare_split raise NotImplementedError() NotImplementedError ``` ### Steps to reproduce the bug I will share implementation if it turns out that everything should be working (i.e. we only need to implement those 3 methods the docs mention), but I don't want to distract from the original question. ### Expected behavior I just need to know if there are additional methods we need to implement when subclassing `DatasetBuilder` besides what the documentation specifies -> `_info`, `_split_generators` and `_generate_examples` ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-135-generic-x86_64-with-glibc2.2.5 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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I_kwDODunzps5YoXKK

The data downloaded in the download folder of the cache does not respect `umask`

open

NONE

### Describe the bug For a project on a cluster we are several users to share the same cache for the datasets library. And we have a problem with the permissions on the data downloaded in the cache. Indeed, it seems that the data is downloaded by giving read and write permissions only to the user launching the command (and no permissions to the group). In our case, those permissions don't respect the `umask` of this user, which was `0007`. Traceback: ``` Using custom data configuration default Downloading and preparing dataset text_caps/default to /gpfswork/rech/cnw/commun/datasets/HuggingFaceM4___text_caps/default/1.0.0/2b9ad220cd90fcf2bfb454645bc54364711b83d6d39401ffdaf8cc40882e9141... Downloading data files: 100%|████████████████████| 3/3 [00:00<00:00, 921.62it/s] --------------------------------------------------------------------------- PermissionError Traceback (most recent call last) Cell In [3], line 1 ----> 1 ds = load_dataset(dataset_name) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/load.py:1746, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1745 # Download and prepare data -> 1746 builder_instance.download_and_prepare( 1747 download_config=download_config, 1748 download_mode=download_mode, 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, 1751 use_auth_token=use_auth_token, 1752 ) 1754 # Build dataset for splits 1755 keep_in_memory = ( 1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1757 ) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info 708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos) 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: File /gpfswork/rech/cnw/commun/modules/datasets_modules/datasets/HuggingFaceM4--TextCaps/2b9ad220cd90fcf2bfb454645bc54364711b83d6d39401ffdaf8cc40882e9141/TextCaps.py:125, in TextCapsDataset._split_generators(self, dl_manager) 123 def _split_generators(self, dl_manager): 124 # urls = _URLS[self.config.name] # TODO later --> 125 data_dir = dl_manager.download_and_extract(_URLS) 126 gen_kwargs = { 127 split_name: { 128 f"{dir_name}_path": Path(data_dir[dir_name][split_name]) (...) 133 for split_name in ["train", "val", "test"] 134 } 136 for split_name in ["train", "val", "test"]: File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls) 415 def download_and_extract(self, url_or_urls): 416 """Download and extract given url_or_urls. 417 418 Is roughly equivalent to: (...) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/download/download_manager.py:324, in DownloadManager.download(self, url_or_urls) 321 self.downloaded_paths.update(dict(zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten()))) 323 start_time = datetime.now() --> 324 self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths) 325 duration = datetime.now() - start_time 326 logger.info(f"Checksum Computation took {duration.total_seconds() // 60} min") File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/download/download_manager.py:229, in DownloadManager._record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths) 226 """Record size/checksum of downloaded files.""" 227 for url, path in zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten()): 228 # call str to support PathLike objects --> 229 self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict( 230 path, record_checksum=self.record_checksums 231 ) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/utils/info_utils.py:82, in get_size_checksum_dict(path, record_checksum) 80 if record_checksum: 81 m = sha256() ---> 82 with open(path, "rb") as f: 83 for chunk in iter(lambda: f.read(1 << 20), b""): 84 m.update(chunk) PermissionError: [Errno 13] Permission denied: '/gpfswork/rech/cnw/commun/datasets/downloads/1e6aa6d23190c30885194fabb193dce3874d902d7636b66315ee8aaa584e80d6' ``` ### Steps to reproduce the bug I think the following will reproduce the bug. Given 2 users belonging to the same group with `umask` set to `0007` - first run with User 1: ```python from datasets import load_dataset ds_name = "HuggingFaceM4/VQAv2" ds = load_dataset(ds_name) ``` - then run with User 2: ```python from datasets import load_dataset ds_name = "HuggingFaceM4/TextCaps" ds = load_dataset(ds_name) ``` ### Expected behavior No `PermissionError` ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-4.18.0-305.65.1.el8_4.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2

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I_kwDODunzps5YoBB3

[Quick poll] Give your opinion on the future of the Hugging Face Open Source ecosystem!

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MEMBER

Thanks to all of you, Datasets is just about to pass 15k stars! Since the last survey, a lot has happened: the [diffusers](https://github.com/huggingface/diffusers), [evaluate](https://github.com/huggingface/evaluate) and [skops](https://github.com/skops-dev/skops) libraries were born. `timm` joined the Hugging Face ecosystem. There were 25 new releases of `transformers`, 21 new releases of `datasets`, 13 new releases of `accelerate`. If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts: [**hf.co/oss-survey**](https://docs.google.com/forms/d/e/1FAIpQLSf4xFQKtpjr6I_l7OfNofqiR8s-WG6tcNbkchDJJf5gYD72zQ/viewform?usp=sf_link) (please reply in the above feedback form rather than to this thread) Thank you all on behalf of the HuggingFace team! 🤗

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I_kwDODunzps5Ymwj4

Wrong dtype for array in audio features

open

NONE

### Describe the bug When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datasets cannot be merged. ### Steps to reproduce the bug For example, for `facebook/voxpopuli` and `mozilla-foundation/common_voice_11_0`: ``` from datasets import load_dataset, interleave_datasets covost = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="train", streaming=True) voxpopuli = datasets.load_dataset("facebook/voxpopuli", "nl", split="train", streaming=True) sample_cv, = covost.take(1) sample_vp, = voxpopuli.take(1) assert sample_cv["audio"]["array"].dtype == sample_vp["audio"]["array"].dtype # Fails dataset = interleave_datasets([covost, voxpopuli]) # ValueError: The features can't be aligned because the key audio of features {'audio_id': Value(dtype='string', id=None), 'language': Value(dtype='int64', id=None), 'audio': {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)}, 'normalized_text': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'speaker_id': Value(dtype='string', id=None), 'is_gold_transcript': Value(dtype='bool', id=None), 'accent': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None)} has unexpected type - {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)} (expected either Audio(sampling_rate=16000, mono=True, decode=True, id=None) or Value("null"). ``` ### Expected behavior The audio should be loaded to arrays with a unique dtype (I guess `float32`) ### Environment info ``` - `datasets` version: 2.7.1.dev0 - Platform: Linux-4.18.0-425.3.1.el8.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.15 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 ```

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I_kwDODunzps5Yh9if

T5 for Q&A produces truncated sentence

closed

NONE

Dear all, I am fine-tuning T5 for Q&A task using the MedQuAD ([GitHub - abachaa/MedQuAD: Medical Question Answering Dataset of 47,457 QA pairs created from 12 NIH websites](https://github.com/abachaa/MedQuAD)) dataset. In the dataset, there are many long answers with thousands of words. I have used pytorch_lightning to train the T5-large model. I have two questions. For example, I set both the max_length, max_input_length, max_output_length to 128. How to deal with those long answers? I just left them as is and the T5Tokenizer can automatically handle. I would assume the tokenizer just truncates an answer at the position of 128th word (or 127th). Is it possible that I manually split an answer into different parts, each part has 128 words; and then all these sub-answers serve as a separate answer to the same question? Another question is that I get incomplete (truncated) answers when using the fine-tuned model in inference, even though the predicted answer is shorter than 128 words. I found a message posted 2 years ago saying that one should add at the end of texts when fine-tuning T5. I followed that but then got a warning message that duplicated were found. I am assuming that this is because the tokenizer truncates an answer text, thus is missing in the truncated answer, such that the end token is not produced in predicted answer. However, I am not sure. Can anybody point out how to address this issue? Any suggestions are highly appreciated. Below is some code snippet. ` import pytorch_lightning as pl from torch.utils.data import DataLoader import torch import numpy as np import time from pathlib import Path from transformers import ( Adafactor, T5ForConditionalGeneration, T5Tokenizer, get_linear_schedule_with_warmup ) from torch.utils.data import RandomSampler from question_answering.utils import * class T5FineTuner(pl.LightningModule): def __init__(self, hyparams): super(T5FineTuner, self).__init__() self.hyparams = hyparams self.model = T5ForConditionalGeneration.from_pretrained(hyparams.model_name_or_path) self.tokenizer = T5Tokenizer.from_pretrained(hyparams.tokenizer_name_or_path) if self.hyparams.freeze_embeds: self.freeze_embeds() if self.hyparams.freeze_encoder: self.freeze_params(self.model.get_encoder()) # assert_all_frozen() self.step_count = 0 self.output_dir = Path(self.hyparams.output_dir) n_observations_per_split = { 'train': self.hyparams.n_train, 'validation': self.hyparams.n_val, 'test': self.hyparams.n_test } self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()} self.em_score_list = [] self.subset_score_list = [] data_folder = r'C:\Datasets\MedQuAD-master' self.train_data, self.val_data, self.test_data = load_medqa_data(data_folder) def freeze_params(self, model): for param in model.parameters(): param.requires_grad = False def freeze_embeds(self): try: self.freeze_params(self.model.model.shared) for d in [self.model.model.encoder, self.model.model.decoder]: self.freeze_params(d.embed_positions) self.freeze_params(d.embed_tokens) except AttributeError: self.freeze_params(self.model.shared) for d in [self.model.encoder, self.model.decoder]: self.freeze_params(d.embed_tokens) def lmap(self, f, x): return list(map(f, x)) def is_logger(self): return self.trainer.proc_rank <= 0 def forward(self, input_ids, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, labels=None): return self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, labels=labels ) def _step(self, batch): labels = batch['target_ids'] labels[labels[:, :] == self.tokenizer.pad_token_id] = -100 outputs = self( input_ids = batch['source_ids'], attention_mask=batch['source_mask'], labels=labels, decoder_attention_mask=batch['target_mask'] ) loss = outputs[0] return loss def ids_to_clean_text(self, generated_ids): gen_text = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) return self.lmap(str.strip, gen_text) def _generative_step(self, batch): t0 = time.time() generated_ids = self.model.generate( batch["source_ids"], attention_mask=batch["source_mask"], use_cache=True, decoder_attention_mask=batch['target_mask'], max_length=128, num_beams=2, early_stopping=True ) preds = self.ids_to_clean_text(generated_ids) targets = self.ids_to_clean_text(batch["target_ids"]) gen_time = (time.time() - t0) / batch["source_ids"].shape[0] loss = self._step(batch) base_metrics = {'val_loss': loss} summ_len = np.mean(self.lmap(len, generated_ids)) base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=targets) em_score, subset_match_score = calculate_scores(preds, targets) self.em_score_list.append(em_score) self.subset_score_list.append(subset_match_score) em_score = torch.tensor(em_score, dtype=torch.float32) subset_match_score = torch.tensor(subset_match_score, dtype=torch.float32) base_metrics.update(em_score=em_score, subset_match_score=subset_match_score) # rouge_results = self.rouge_metric.compute() # rouge_dict = self.parse_score(rouge_results) return base_metrics def training_step(self, batch, batch_idx): loss = self._step(batch) tensorboard_logs = {'train_loss': loss} return {'loss': loss, 'log': tensorboard_logs} def training_epoch_end(self, outputs): avg_train_loss = torch.stack([x['loss'] for x in outputs]).mean() tensorboard_logs = {'avg_train_loss': avg_train_loss} # return {'avg_train_loss': avg_train_loss, 'log': tensorboard_logs, 'progress_bar': tensorboard_logs} def validation_step(self, batch, batch_idx): return self._generative_step(batch) def validation_epoch_end(self, outputs): avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean() tensorboard_logs = {'val_loss': avg_loss} if len(self.em_score_list) <= 2: average_em_score = sum(self.em_score_list) / len(self.em_score_list) average_subset_match_score = sum(self.subset_score_list) / len(self.subset_score_list) else: latest_em_score = self.em_score_list[:-2] latest_subset_score = self.subset_score_list[:-2] average_em_score = sum(latest_em_score) / len(latest_em_score) average_subset_match_score = sum(latest_subset_score) / len(latest_subset_score) average_em_score = torch.tensor(average_em_score, dtype=torch.float32) average_subset_match_score = torch.tensor(average_subset_match_score, dtype=torch.float32) tensorboard_logs.update(em_score=average_em_score, subset_match_score=average_subset_match_score) self.target_gen = [] self.prediction_gen = [] return { 'avg_val_loss': avg_loss, 'em_score': average_em_score, 'subset_match_socre': average_subset_match_score, 'log': tensorboard_logs, 'progress_bar': tensorboard_logs } def configure_optimizers(self): model = self.model no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": self.hyparams.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = Adafactor(optimizer_grouped_parameters, lr=self.hyparams.learning_rate, scale_parameter=False, relative_step=False) self.opt = optimizer return [optimizer] def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure=None, on_tpu=False, using_native_amp=False, using_lbfgs=False): optimizer.step(closure=optimizer_closure) optimizer.zero_grad() self.lr_scheduler.step() def get_tqdm_dict(self): tqdm_dict = {"loss": "{:.3f}".format(self.trainer.avg_loss), "lr": self.lr_scheduler.get_last_lr()[-1]} return tqdm_dict def train_dataloader(self): n_samples = self.n_obs['train'] train_dataset = get_dataset(tokenizer=self.tokenizer, data=self.train_data, num_samples=n_samples, args=self.hyparams) sampler = RandomSampler(train_dataset) dataloader = DataLoader(train_dataset, sampler=sampler, batch_size=self.hyparams.train_batch_size, drop_last=True, num_workers=4) # t_total = ( # (len(dataloader.dataset) // (self.hyparams.train_batch_size * max(1, self.hyparams.n_gpu))) # // self.hyparams.gradient_accumulation_steps # * float(self.hyparams.num_train_epochs) # ) t_total = 100000 scheduler = get_linear_schedule_with_warmup( self.opt, num_warmup_steps=self.hyparams.warmup_steps, num_training_steps=t_total ) self.lr_scheduler = scheduler return dataloader def val_dataloader(self): n_samples = self.n_obs['validation'] validation_dataset = get_dataset(tokenizer=self.tokenizer, data=self.val_data, num_samples=n_samples, args=self.hyparams) sampler = RandomSampler(validation_dataset) return DataLoader(validation_dataset, shuffle=False, batch_size=self.hyparams.eval_batch_size, sampler=sampler, num_workers=4) def test_dataloader(self): n_samples = self.n_obs['test'] test_dataset = get_dataset(tokenizer=self.tokenizer, data=self.test_data, num_samples=n_samples, args=self.hyparams) return DataLoader(test_dataset, batch_size=self.hyparams.eval_batch_size, num_workers=4) def on_save_checkpoint(self, checkpoint): save_path = self.output_dir.joinpath("best_tfmr") self.model.config.save_step = self.step_count self.model.save_pretrained(save_path) self.tokenizer.save_pretrained(save_path) import os import argparse import pytorch_lightning as pl from question_answering.t5_closed_book import T5FineTuner if __name__ == '__main__': args_dict = dict( output_dir="", # path to save the checkpoints model_name_or_path='t5-large', tokenizer_name_or_path='t5-large', max_input_length=128, max_output_length=128, freeze_encoder=False, freeze_embeds=False, learning_rate=1e-5, weight_decay=0.0, adam_epsilon=1e-8, warmup_steps=0, train_batch_size=4, eval_batch_size=4, num_train_epochs=2, gradient_accumulation_steps=10, n_gpu=1, resume_from_checkpoint=None, val_check_interval=0.5, n_val=4000, n_train=-1, n_test=-1, early_stop_callback=False, fp_16=False, opt_level='O1', max_grad_norm=1.0, seed=101, ) args_dict.update({'output_dir': 't5_large_MedQuAD_256', 'num_train_epochs': 100, 'train_batch_size': 16, 'eval_batch_size': 16, 'learning_rate': 1e-3}) args = argparse.Namespace(**args_dict) checkpoint_callback = pl.callbacks.ModelCheckpoint(dirpath=args.output_dir, monitor="em_score", mode="max", save_top_k=1) ## If resuming from checkpoint, add an arg resume_from_checkpoint train_params = dict( accumulate_grad_batches=args.gradient_accumulation_steps, gpus=args.n_gpu, max_epochs=args.num_train_epochs, # early_stop_callback=False, precision=16 if args.fp_16 else 32, # amp_level=args.opt_level, # resume_from_checkpoint=args.resume_from_checkpoint, gradient_clip_val=args.max_grad_norm, checkpoint_callback=checkpoint_callback, val_check_interval=args.val_check_interval, # accelerator='dp' # logger=wandb_logger, # callbacks=[LoggingCallback()], ) model = T5FineTuner(args) trainer = pl.Trainer(**train_params) trainer.fit(model) `

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I_kwDODunzps5YhwcS

Emotion dataset cannot be downloaded

closed

NONE

### Describe the bug The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`. It was working yesterday (December 7, 2022), but stopped working today (December 8, 2022). ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("emotion") ``` ### Expected behavior The dataset should load properly. ### Environment info - `datasets` version: 2.7.1 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.9.13 - PyArrow version: 10.0.1 - Pandas version: 1.5.1

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I_kwDODunzps5YX2KH

`map()` stops every 1000 steps

closed

NONE

### Describe the bug I am passing the following `prepare_dataset` function to `Dataset.map` (code is inspired from [here](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py#L454)) ```python3 def prepare_dataset(batch): # load and resample audio data from 48 to 16kHz audio = batch["audio"] # compute log-Mel input features from input audio array batch["input_features"] = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0] # encode target text to label ids batch["labels"] = tokenizer(batch[text_column]).input_ids return batch ... train_ds = train_ds.map(prepare_dataset) ``` Here is the exact code I am running https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets/blob/main/train.py#L70-L71 It starts using all the cores (I am not sure why because I did not pass `num_proc`) then progress bar stops at every 1k steps. (starts using a single core) then come back to using all the cores again. link to [screen record](https://youtu.be/jPQpQQGp6Gc) Can someone explain this process and maybe provide a way to improve this pipeline? cc: @lhoestq ### Steps to reproduce the bug 1. load the dataset 2. create a Whisper processor 3. create a `prepare_dataset` function 4. pass the function to `dataset.map(prepare_dataset)` ### Expected behavior - Use a single core per a function - not to stop at some point? ### Environment info - `datasets` version: 2.7.1.dev0 - Platform: Linux-5.4.0-109-generic-x86_64-with-glibc2.27 - Python version: 3.8.10 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

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I_kwDODunzps5YUNP8

Support webdataset format

open

MEMBER

Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234. In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local directory, or from a dataset on the Hugging Face Hub). Some datasets on the Hub are already in webdataset format. It terms of implementation, we can have something similar to the Parquet loader. I also think it's fine to have webdataset as an optional dependency.

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I_kwDODunzps5YAc0w

Passing numpy array to ClassLabel names causes ValueError

closed

CONTRIBUTOR

### Describe the bug If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error. ### Steps to reproduce the bug https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX TLDR: If I define my classes as: ``` my_classes = np.array(['one', 'two', 'three']) ``` Then this errors: ```py features = Features({'value': Value('string'), 'label': ClassLabel(names=my_classes)}) dataset = Dataset.from_list(my_data, features=features) ``` ``` ValueError Traceback (most recent call last) [<ipython-input-8-a8a9d53ec82f>](https://localhost:8080/#) in <module> ----> 1 dataset = Dataset.from_list(my_data, features=features) 11 frames [/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in _asdict_inner(obj) 183 for f in fields(obj): 184 value = _asdict_inner(getattr(obj, f.name)) --> 185 if not f.init or value != f.default or f.metadata.get("include_in_asdict_even_if_is_default", False): 186 result[f.name] = value 187 return result ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() ``` But this works: ``` features2 = Features({'value': Value('string'), 'label': ClassLabel(names=list(my_classes))}) dataset2 = Dataset.from_list(my_data, features=features2) ``` ### Expected behavior If I provide a numpy array of class names, I would expect either an error that the names list is the wrong type, or for it to be cast internally. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.10 - Python version: 3.8.15 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 Additionally: - Numpy version: 1.23.5

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I_kwDODunzps5Xt1r4

No documentation for main branch is built

closed

MEMBER

Since: - #5250 - Commit: 703b84311f4ead83c7f79639f2dfa739295f0be6 the docs for main branch are no longer built. The change introduced only triggers the docs building for releases.

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I_kwDODunzps5Xtd62

map(...batch_size=None) for IterableDataset

closed

CONTRIBUTOR

### Feature request Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too. ### Motivation Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple of reasons why this might be nice. One is that load_dataset(...) can return either IterableDataset or Dataset. mypy will then complain if batch_size=None even if we know it is Dataset. Of course we can do: assert isinstance(d, datasets.DatasetDict) But it is a mild inconvenience. What's more annoying is that whenever we use something like e.g. `combine_datasets(...)`, we end up with the union again, and so have to do the assert again. Another is that we could actually end up with an IterableDataset small enough for memory in normal/correct usage, e.g. by filtering a massive IterableDataset. For practical usages, an alternative to this would be to convert from an iterable dataset to a map-style dataset, but it is not obvious how to do this. ### Your contribution Not this time.

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I_kwDODunzps5Xta6g

Fix docstrings and types in documentation that appears on the website

open

CONTRIBUTOR

While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://huggingface.co/docs/datasets/package_reference/builder_classes) of the documentation on the website. Would be nice someday, maybe before releasing datasets 3.0.0, to unify it......

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I_kwDODunzps5XtZhT

Duplicated Keys in Taskmaster-2 Dataset

closed

NONE

### Describe the bug Loading certain splits () of the taskmaster-2 dataset fails because of a DuplicatedKeysError. This occurs for the following domains: `'hotels', 'movies', 'music', 'sports'`. The domains `'flights', 'food-ordering', 'restaurant-search'` load fine. Output: ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("taskmaster2", "music") ``` Output: ``` --------------------------------------------------------------------------- DuplicatedKeysError Traceback (most recent call last) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1532, in GeneratorBasedBuilder._prepare_split_single(self, arg) [1531](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1530) example = self.info.features.encode_example(record) if self.info.features is not None else record -> [1532](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1531) writer.write(example, key) [1533](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1532) num_examples_progress_update += 1 File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:475, in ArrowWriter.write(self, example, key, writer_batch_size) [474](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=473) if self._check_duplicates: --> [475](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=474) self.check_duplicate_keys() [476](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=475) # Re-intializing to empty list for next batch File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:492, in ArrowWriter.check_duplicate_keys(self) [486](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=485) duplicate_key_indices = [ [487](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=486) str(self._num_examples + index) [488](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=487) for index, (duplicate_hash, _) in enumerate(self.hkey_record) [489](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=488) if duplicate_hash == hash [490](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=489) ] --> [492](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=491) raise DuplicatedKeysError(key, duplicate_key_indices) [493](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=492) else: DuplicatedKeysError: Found multiple examples generated with the same key The examples at index 858, 859 have the key dlg-89174425-d57a-4db7-a92b-165c3bff6735 During handling of the above exception, another exception occurred: DuplicatedKeysError Traceback (most recent call last) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1541, in GeneratorBasedBuilder._prepare_split_single(self, arg) [1540](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1539) num_shards = shard_id + 1 -> [1541](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1540) num_examples, num_bytes = writer.finalize() [1542](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1541) writer.close() File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:563, in ArrowWriter.finalize(self, close_stream) [562](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=561) if self._check_duplicates: --> [563](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=562) self.check_duplicate_keys() [564](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=563) # Re-intializing to empty list for next batch File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:492, in ArrowWriter.check_duplicate_keys(self) [486](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=485) duplicate_key_indices = [ [487](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=486) str(self._num_examples + index) [488](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=487) for index, (duplicate_hash, _) in enumerate(self.hkey_record) [489](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=488) if duplicate_hash == hash [490](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=489) ] --> [492](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=491) raise DuplicatedKeysError(key, duplicate_key_indices) [493](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=492) else: DuplicatedKeysError: Found multiple examples generated with the same key The examples at index 858, 859 have the key dlg-89174425-d57a-4db7-a92b-165c3bff6735 The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[23], line 1 ----> 1 dataset = load_dataset("taskmaster2", "music") File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py:1741, 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, num_proc, **config_kwargs) [1738](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1737) try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES [1740](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1739) # Download and prepare data -> [1741](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1740) builder_instance.download_and_prepare( [1742](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1741) download_config=download_config, [1743](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1742) download_mode=download_mode, [1744](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1743) ignore_verifications=ignore_verifications, [1745](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1744) try_from_hf_gcs=try_from_hf_gcs, [1746](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1745) use_auth_token=use_auth_token, [1747](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1746) num_proc=num_proc, [1748](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1747) ) [1750](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1749) # Build dataset for splits [1751](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1750) keep_in_memory = ( [1752](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1751) keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) [1753](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1752) ) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:822, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) [820](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=819) if num_proc is not None: [821](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=820) prepare_split_kwargs["num_proc"] = num_proc --> [822](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=821) self._download_and_prepare( [823](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=822) dl_manager=dl_manager, [824](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=823) verify_infos=verify_infos, [825](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=824) **prepare_split_kwargs, [826](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=825) **download_and_prepare_kwargs, [827](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=826) ) [828](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=827) # Sync info [829](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=828) self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1555, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs) [1554](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1553) def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs): -> [1555](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1554) super()._download_and_prepare( [1556](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1555) dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs [1557](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1556) ) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:913, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) [909](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=908) split_dict.add(split_generator.split_info) [911](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=910) try: [912](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=911) # Prepare split will record examples associated to the split --> [913](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=912) self._prepare_split(split_generator, **prepare_split_kwargs) [914](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=913) except OSError as e: [915](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=914) raise OSError( [916](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=915) "Cannot find data file. " [917](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=916) + (self.manual_download_instructions or "") [918](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=917) + "\nOriginal error:\n" [919](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=918) + str(e) [920](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=919) ) from None File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1396, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size) [1394](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1393) gen_kwargs = split_generator.gen_kwargs [1395](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1394) job_id = 0 -> [1396](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1395) for job_id, done, content in self._prepare_split_single( [1397](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1396) {"gen_kwargs": gen_kwargs, "job_id": job_id, **_prepare_split_args} [1398](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1397) ): [1399](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1398) if done: [1400](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1399) result = content File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1550, in GeneratorBasedBuilder._prepare_split_single(self, arg) [1548](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1547) if isinstance(e, SchemaInferenceError) and e.__context__ is not None: [1549](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1548) e = e.__context__ -> [1550](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1549) raise DatasetGenerationError("An error occurred while generating the dataset") from e [1552](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1551) yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Loads the dataset ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

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I_kwDODunzps5XpF-U

`ImageFolder` performs poorly with large datasets

open

NONE

### Describe the bug While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images. ## Setup * Nested directories (5 levels deep) * 3M+ images * 1 `metadata.jsonl` file ## Performance Degradation Point 1 Degradation occurs because [`get_data_files_patterns`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L231-L243) runs the exact same scan for many different types of patterns, and there doesn't seem to be a way to easily limit this. It's controlled by the definition of [`ALL_DEFAULT_PATTERNS`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L82-L85). One scan with 3M+ files takes about 10-15 minutes to complete on my setup, so having those extra scans really slows things down – from 10 minutes to 60+. Most of the scans return no matches, but they still take a significant amount of time to complete – hence the poor performance. As a side effect, when this scan is run on 3M+ image files, Python also consumes up to 12 GB of RAM, which is not ideal. ## Performance Degradation Point 2 The second performance bottleneck is in [`PackagedDatasetModuleFactory.get_module`](https://github.com/huggingface/datasets/blob/d7dfbc83d68e87ba002c5eb2555f7a932e59038a/src/datasets/load.py#L707-L711), which calls `DataFilesDict.from_local_or_remote`. It runs for a long time (60min+), consuming significant amounts of RAM – even more than the point 1 above. Based on `iostat -d 2`, it performs **zero** disk operations, which to me suggests that there is a code based bottleneck there that could be sorted out. ### Steps to reproduce the bug ```python from datasets import load_dataset import os import huggingface_hub dataset = load_dataset( 'imagefolder', data_dir='/some/path', # just to spell it out: split=None, drop_labels=True, keep_in_memory=False ) dataset.push_to_hub('account/dataset', private=True) ``` ### Expected behavior While it's certainly possible to write a custom loader to replace `ImageFolder` with, it'd be great if the off-the-shelf `ImageFolder` would by default have a setup that can scale to large datasets. Or perhaps there could be a dedicated loader just for large datasets that trades off flexibility for performance? As in, maybe you have to define explicitly how you want it to work rather than it trying to guess your data structure like `_get_data_files_patterns()` does? ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-4.14.296-222.539.amzn2.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.7.10 - PyArrow version: 10.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5XoC9h

Bug in sample_by="paragraph"

closed

NONE

### Describe the bug I think [this line](https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/text/text.py#L96) is wrong and should be `batch = f.read(self.config.chunksize)`. Otherwise it will never terminate because even when `f` is finished reading, `batch` will still be truthy from the last iteration. ### Steps to reproduce the bug ``` > cat test.txt a b c d e f ```` ```python >>> import datasets >>> datasets.load_dataset("text", data_files={"train":"test.txt"}, sample_by="paragraph") ``` This will go on forever. ### Expected behavior Terminates very quickly. ### Environment info `version = "2.6.1"` but I think the bug is still there on main.

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I_kwDODunzps5XntQt

Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails

open

CONTRIBUTOR

### Describe the bug If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails. That's what happened in https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/discussions/2#6385fd1269634850f8ddff48. ### Steps to reproduce the bug 1. create a dataset with a custom split that returns, for example, only `"train"` split in `_splits_generators'`. specifically, if really want to reproduce, copy `https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/food_vision_199_classes.py 2. run `datasets-cli test dataset_script.py --save_info --all_configs` - this would generate metadata yaml in `README.md` that would contain info about splits, for example, like this: ``` splits: - name: train num_bytes: 2973286 num_examples: 19747 ``` 3. make changes to your script so that it returns another set of splits, for example, `"train"` and `"test"` (uncomment [these lines](https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/food_vision_199_classes.py#L271)) 4. run `load_dataset` and get the following error: ```python Traceback (most recent call last): File "/home/daniel/code/pytorch/env/bin/datasets-cli", line 8, in <module> sys.exit(main()) File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/commands/datasets_cli.py", line 39, in main service.run() File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/commands/test.py", line 141, in run builder.download_and_prepare( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare self._download_and_prepare( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare super()._download_and_prepare( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 913, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 1356, in _prepare_split split_info = self.info.splits[split_generator.name] File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/splits.py", line 525, in __getitem__ instructions = make_file_instructions( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/arrow_reader.py", line 111, in make_file_instructions name2filenames = { File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/arrow_reader.py", line 112, in <dictcomp> info.name: filenames_for_dataset_split( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/naming.py", line 78, in filenames_for_dataset_split prefix = filename_prefix_for_split(dataset_name, split) File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/naming.py", line 57, in filename_prefix_for_split if os.path.basename(name) != name: File "/home/daniel/code/pytorch/env/lib/python3.8/posixpath.py", line 143, in basename p = os.fspath(p) TypeError: expected str, bytes or os.PathLike object, not NoneType ``` 5. bonus: try to regenerate metadata in `README.md` with `datasets-cli` as in step 2 and get the same error. This is because `dataset.info.splits` contains only `"train"` split so when we are doing `self.info.splits[split_generator.name]` it tries to infer smth like `info.splits['train[50%]']` and that's not the case and it fails. ### Expected behavior to be discussed? This can be solved by removing splits information from metadata file first. But I wonder if there is a better way. ### Environment info - Datasets version: 2.7.1 - Python version: 3.8.13

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I_kwDODunzps5XmZ1-

Datasets: classification_report() got an unexpected keyword argument 'suffix'

open

NONE

https://github.com/huggingface/datasets/blob/main/metrics/seqeval/seqeval.py > import datasets predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] seqeval = datasets.load_metric("seqeval") results = seqeval.compute(predictions=predictions, references=references) print(list(results.keys())) print(results["overall_f1"]) print(results["PER"]["f1"]) It raises the error: > TypeError: classification_report() got an unexpected keyword argument 'suffix' For context, versions on my pip list -v > datasets 1.12.1 seqeval 1.2.2

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I_kwDODunzps5XYOf_

Can't use custom feature description when loading a dataset

closed

CONTRIBUTOR

### Describe the bug I have created a feature dictionary to describe my datasets' column types, to use when loading the dataset, following [the doc](https://huggingface.co/docs/datasets/main/en/about_dataset_features). It crashes at dataset load. ### Steps to reproduce the bug ```python # Creating features task_list = [f"motif_G{i}" for i in range(19, 53)] features = {t: Sequence(feature=Value(dtype="float64")) for t in task_list} for col_name in ["class_label"]: features[col_name] = Sequence(feature=Value(dtype="int64")) for col_name in ["num_nodes"]: features[col_name] = Value(dtype="int64") for col_name in ["num_bridges", "num_cycles", "avg_shortest_path_len"]: features[col_name] = Sequence(feature=Value(dtype="float64")) for col_name in ["edge_attr", "node_feat", "edge_index"]: features[col_name] = Sequence(feature=Sequence(feature=Value(dtype="int64"))) print(features) dataset = load_dataset(path=f"graphs-datasets/unbalanced-motifs-500K", split="train", features=features) ``` Last line will crash and say 'TypeError: argument of type 'Sequence' is not iterable'. Full stack: ``` Traceback (most recent call last): File "pretrain_tokengt.py", line 131, in <module> main(output_folder = "../workspace/pretraining", File "pretrain_tokengt.py", line 52, in main dataset = load_dataset(path=f"graphs-datasets/{dataset_name}", split="train", features=features) File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1718, in load_dataset builder_instance = load_dataset_builder( File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1514, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "huggingface_env/lib/python3.8/site-packages/datasets/builder.py", line 321, in __init__ info.update(self._info()) File "huggingface_env/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 62, in _info return datasets.DatasetInfo(features=self.config.features) File "<string>", line 20, in __init__ File "huggingface_env/lib/python3.8/site-packages/datasets/info.py", line 155, in __post_init__ self.features = Features.from_dict(self.features) File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1599, in from_dict obj = generate_from_dict(dic) File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1281, in generate_from_dict if "_type" not in obj or isinstance(obj["_type"], dict): TypeError: argument of type 'Sequence' is not iterable ``` ### Expected behavior For it not to crash. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5XW7Sy

Dataset joelito/mc4_legal does not work with multiple files

closed

CONTRIBUTOR

### Describe the bug The dataset https://huggingface.co/datasets/joelito/mc4_legal works for languages like bg with a single data file, but not for languages with multiple files like de. It shows zero rows for the de dataset. joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main) [1]> python test_mc4_legal.py (debug) Found cached dataset mc4_legal (/Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/de/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f) Dataset({ features: ['index', 'url', 'timestamp', 'matches', 'text'], num_rows: 0 }) joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main)> python test_mc4_legal.py (debug) Downloading and preparing dataset mc4_legal/bg to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f... Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1240.55it/s] Dataset mc4_legal downloaded and prepared to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f. Subsequent calls will reuse this data. Dataset({ features: ['index', 'url', 'timestamp', 'matches', 'text'], num_rows: 204 }) ### Steps to reproduce the bug import datasets from datasets import load_dataset, get_dataset_config_names language = "bg" test = load_dataset("joelito/mc4_legal", language, split='train') ### Expected behavior It should display the correct number of rows for the de dataset which should be a large number (thousands or more). ### Environment info Package Version ------------------------ -------------- absl-py 1.3.0 aiohttp 3.8.1 aiosignal 1.2.0 astunparse 1.6.3 async-timeout 4.0.2 attrs 22.1.0 beautifulsoup4 4.11.1 blinker 1.4 blis 0.7.8 Bottleneck 1.3.4 brotlipy 0.7.0 cachetools 5.2.0 catalogue 2.0.7 certifi 2022.5.18.1 cffi 1.15.1 chardet 4.0.0 charset-normalizer 2.1.0 click 8.0.4 conllu 4.5.2 cryptography 38.0.1 cymem 2.0.6 datasets 2.6.1 dill 0.3.5.1 docker-pycreds 0.4.0 fasttext 0.9.2 fasttext-langdetect 1.0.3 filelock 3.0.12 flatbuffers 20210226132247 frozenlist 1.3.0 fsspec 2022.5.0 gast 0.4.0 gcloud 0.18.3 gitdb 4.0.9 GitPython 3.1.27 google-auth 2.9.0 google-auth-oauthlib 0.4.6 google-pasta 0.2.0 googleapis-common-protos 1.57.0 grpcio 1.47.0 h5py 3.7.0 httplib2 0.21.0 huggingface-hub 0.8.1 idna 3.4 importlib-metadata 4.12.0 Jinja2 3.1.2 joblib 1.0.1 keras 2.9.0 Keras-Preprocessing 1.1.2 langcodes 3.3.0 lxml 4.9.1 Markdown 3.3.7 MarkupSafe 2.1.1 mkl-fft 1.3.1 mkl-random 1.2.2 mkl-service 2.4.0 multidict 6.0.2 multiprocess 0.70.13 murmurhash 1.0.7 numexpr 2.8.1 numpy 1.22.3 oauth2client 4.1.3 oauthlib 3.2.1 opt-einsum 3.3.0 packaging 21.3 pandas 1.4.2 pathtools 0.1.2 pathy 0.6.1 pip 21.1.2 preshed 3.0.6 promise 2.3 protobuf 4.21.9 psutil 5.9.1 pyarrow 8.0.0 pyasn1 0.4.8 pyasn1-modules 0.2.8 pybind11 2.9.2 pycountry 22.3.5 pycparser 2.21 pydantic 1.8.2 PyJWT 2.4.0 pylzma 0.5.0 pyOpenSSL 22.0.0 pyparsing 3.0.4 PySocks 1.7.1 python-dateutil 2.8.2 pytz 2021.3 PyYAML 6.0 regex 2021.4.4 requests 2.28.1 requests-oauthlib 1.3.1 responses 0.18.0 rsa 4.8 sacremoses 0.0.45 scikit-learn 1.1.1 scipy 1.8.1 sentencepiece 0.1.96 sentry-sdk 1.6.0 setproctitle 1.2.3 setuptools 65.5.0 shortuuid 1.0.9 six 1.16.0 smart-open 5.2.1 smmap 5.0.0 soupsieve 2.3.2.post1 spacy 3.3.1 spacy-legacy 3.0.9 spacy-loggers 1.0.2 srsly 2.4.3 tabulate 0.8.9 tensorboard 2.9.1 tensorboard-data-server 0.6.1 tensorboard-plugin-wit 1.8.1 tensorflow 2.9.1 tensorflow-estimator 2.9.0 termcolor 2.1.0 thinc 8.0.17 threadpoolctl 3.1.0 tokenizers 0.12.1 torch 1.13.0 tqdm 4.64.0 transformers 4.20.1 typer 0.4.1 typing-extensions 4.3.0 Unidecode 1.3.6 urllib3 1.26.12 wandb 0.12.20 wasabi 0.9.1 web-anno-tsv 0.0.1 Werkzeug 2.1.2 wget 3.2 wheel 0.35.1 wrapt 1.14.1 xxhash 3.0.0 yarl 1.8.1 zipp 3.8.0 Python 3.8.10

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I_kwDODunzps5XU89f

timit_asr doesn't load the test split.

open

NONE

### Describe the bug When I use the function ```timit = load_dataset('timit_asr', data_dir=data_dir)```, it only loads train split, not test split. I tried to change the directory and filename to lower case to upper case for the test split, but it does not work at all. ```python DatasetDict({ train: Dataset({ features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'], num_rows: 4620 }) test: Dataset({ features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'], num_rows: 0 }) }) ``` The directory structure of both splits are same. (DIALECT_REGION / SPEAKER_CODE / DATA_FILES) ### Steps to reproduce the bug 1. just use ```timit = load_dataset('timit_asr', data_dir=data_dir)``` ### Expected behavior ```python DatasetDict({ train: Dataset({ features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'], num_rows: 4620 }) test: Dataset({ features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'], num_rows: 1680 }) }) ``` ### Environment info - ubuntu 20.04 - python 3.9.13 - datasets 2.7.1

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I_kwDODunzps5XTUWP

Bug in xopen with Windows pathnames

closed

MEMBER

Currently, `xopen` function has a bug with local Windows pathnames: From its implementation: ```python def xopen(file: str, mode="r", *args, **kwargs): file = _as_posix(PurePath(file)) main_hop, *rest_hops = file.split("::") if is_local_path(main_hop): return open(file, mode, *args, **kwargs) ``` On a Windows machine, if we pass the argument: ```python xopen("C:\\Users\\USERNAME\\filename.txt") ``` it returns ```python open("C:/Users/USERNAME/filename.txt") ```

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I_kwDODunzps5XS1Bs

Bug in xjoin with Windows pathnames

closed

MEMBER

Currently, `xjoin` function has a bug with local Windows pathnames: instead of returning the OS-dependent join pathname, it always returns it in POSIX format. ```python from datasets.download.streaming_download_manager import xjoin path = xjoin("C:\\Users\\USERNAME", "filename.txt") ``` Join path should be: ```python "C:\\Users\\USERNAME\\filename.txt" ``` However it is: ```python "C:/Users/USERNAME/filename.txt" ```

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I_kwDODunzps5XQvhX

Extractions failed when .zip file located on read-only path (e.g., SageMaker FastFile mode)

open

NONE

### Describe the bug Hi, `load_dataset()` does not work .zip files located on a read-only directory. Looks like it's because Dataset creates a lock file in the [same directory](https://github.com/huggingface/datasets/blob/df4bdd365f2abb695f113cbf8856a925bc70901b/src/datasets/utils/extract.py) as the .zip file. Encountered this when attempting `load_dataset()` on a datadir with SageMaker FastFile mode. ### Steps to reproduce the bug ```python # Showing relevant lines only. hyperparameters = { "dataset_name": "ydshieh/coco_dataset_script", "dataset_config_name": 2017, "data_dir": "/opt/ml/input/data/coco", "cache_dir": "/tmp/huggingface-cache", # Fix dataset complains out-of-space. ... } estimator = PyTorch( base_job_name="clip", source_dir="../src/sm-entrypoint", entry_point="run_clip.py", # Transformers/src/examples/pytorch/contrastive-image-text/run_clip.py framework_version="1.12", py_version="py38", hyperparameters=hyperparameters, instance_count=1, instance_type="ml.p3.16xlarge", volume_size=100, distribution={"smdistributed": {"dataparallel": {"enabled": True}}}, ) fast_file = lambda x: TrainingInput(x, input_mode='FastFile') estimator.fit( { "pre-trained": fast_file("s3://vm-sagemakerr-us-east-1/clip/pre-trained-checkpoint/"), "coco": fast_file("s3://vm-sagemakerr-us-east-1/clip/coco-zip-files/"), } ) ``` Error message: ```text ErrorMessage "OSError: [Errno 30] Read-only file system: '/opt/ml/input/data/coco/image_info_test2017.zip.lock' """ The above exception was the direct cause of the following exception Traceback (most recent call last) File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/opt/conda/lib/python3.8/site-packages/mpi4py/__main__.py", line 7, in <module> main() File "/opt/conda/lib/python3.8/site-packages/mpi4py/run.py", line 198, in main run_command_line(args) File "/opt/conda/lib/python3.8/site-packages/mpi4py/run.py", line 47, in run_command_line run_path(sys.argv[0], run_name='__main__') File "/opt/conda/lib/python3.8/runpy.py", line 265, in run_path return _run_module_code(code, init_globals, run_name, File "/opt/conda/lib/python3.8/runpy.py", line 97, in _run_module_code _run_code(code, mod_globals, init_globals, File "run_clip_smddp.py", line 594, in <module> File "run_clip_smddp.py", line 327, in main dataset = load_dataset( File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1741, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare super()._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 891, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/ydshieh--coco_dataset_script/e033205c0266a54c10be132f9264f2a39dcf893e798f6756d224b1ff5078998f/coco_dataset_script.py", line 123, in _split_generators archive_path = dl_manager.download_and_extract(_DL_URLS) File "/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py", line 447, in download_and_extract return self.extract(self.download(url_or_urls)) File "/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py", line 419, in extract extracted_paths = map_nested( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 472, in map_nested mapped = pool.map(_single_map_nested, split_kwds) File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 364, in map return self._map_async(func, iterable, mapstar, chunksize).get() File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 771, in get raise self._value OSError: [Errno 30] Read-only file system: '/opt/ml/input/data/coco/image_info_test2017.zip.lock'" ``` ### Expected behavior `load_dataset()` to succeed, just like when .zip file is passed in SageMaker File mode. ### Environment info * datasets-2.7.1 * transformers-4.24.0 * python-3.8 * torch-1.12 * SageMaker PyTorch DLC

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I_kwDODunzps5XPdHR

Support streaming datasets with pathlib.Path.with_suffix

closed

MEMBER

Extend support for streaming datasets that use `pathlib.Path.with_suffix`. This feature will be useful e.g. for datasets containing text files and annotated files with the same name but different extension.

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I_kwDODunzps5XNG4I

Missing documentation build for versions 2.7.1 and 2.6.2

closed

MEMBER

After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered). There was a fix by: - #5291 However, both documentations were built from main branch, instead of their corresponding version branch. We are rebuilding them.

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I_kwDODunzps5XJmUz

Lossy json serialization - deserialization of dataset info

open

NONE

### Describe the bug Saving a dataset to disk as json (using `to_json`) and then loading it again (using `load_dataset`) results in features whose labels are not type-cast correctly. In the code snippet below, `features.label` should have a label of type `ClassLabel` but has type `Value` instead. ### Steps to reproduce the bug ``` from datasets import load_dataset def test_serdes_from_json(d): dataset = load_dataset(d, split="train") dataset.to_json('_test') dataset_loaded = load_dataset("json", data_files='_test', split='train') try: assert dataset_loaded.info.features == dataset.info.features, "features unequal!" except Exception as ex: print(f'{ex}') print(f'expected {dataset.info.features}, \nactual { dataset_loaded.info.features }') test_serdes_from_json('rotten_tomatoes') ``` Output ``` features unequal! expected {'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['neg', 'pos'], id=None)}, actual {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)} ``` ### Expected behavior The deserialized `features.label` should have type `ClassLabel`. ### Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.10.144-127.601.amzn2.x86_64-x86_64-with-glibc2.17 - Python version: 3.7.13 - PyArrow version: 7.0.0 - Pandas version: 1.2.3

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I_kwDODunzps5XIvJ3

FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json

closed

NONE

### Describe the bug I follow the steps provided on the website [https://huggingface.co/datasets/wikipedia](https://huggingface.co/datasets/wikipedia) $ pip install apache_beam mwparserfromhell >>> from datasets import load_dataset >>> load_dataset("wikipedia", "20220301.en") however this results in the following error: raise MissingBeamOptions( datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/ If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). Example of usage: `load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')` If I then prompt the system with: >>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner') the following error occurs: raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json Here is the exact code: Python 3.10.6 (main, Nov 2 2022, 18:53:38) [GCC 11.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> load_dataset('wikipedia', '20220301.en') Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559... Downloading: 100%|████████████████████████████████████████████████████████████████████████████| 15.3k/15.3k [00:00<00:00, 22.2MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1879, in _download_and_prepare raise MissingBeamOptions( datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/ If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). Example of usage: `load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')` >>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner') Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559... Downloading: 100%|████████████████████████████████████████████████████████████████████████████| 15.3k/15.3k [00:00<00:00, 18.8MB/s] Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1909, in _download_and_prepare super()._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 891, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/rorytol/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py", line 945, in _split_generators downloaded_files = dl_manager.download_and_extract({"info": info_url}) File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 447, in download_and_extract return self.extract(self.download(url_or_urls)) File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 311, in download downloaded_path_or_paths = map_nested( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 444, in map_nested mapped = [ File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 445, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 338, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 183, in cached_path output_path = get_from_cache( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 530, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json ### Steps to reproduce the bug $ pip install apache_beam mwparserfromhell >>> from datasets import load_dataset >>> load_dataset("wikipedia", "20220301.en") >>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner') ### Expected behavior Download the dataset ### Environment info Running linux on a remote workstation operated through a macbook terminal Python 3.10.6

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I_kwDODunzps5XHQV1

Features of IterableDataset set to None by remove column

closed

CONTRIBUTOR

### Describe the bug The `remove_column` method of the IterableDataset sets the dataset features to None. ### Steps to reproduce the bug ```python from datasets import Audio, load_dataset # load LS in streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # check original features print("Original features: ", dataset.features.keys()) # define features to remove: we KEEP audio and text COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id'] dataset = dataset.remove_columns(COLUMNS_TO_REMOVE) # check processed features, uh-oh! print("Processed features: ", dataset.features) # streaming the first audio sample still works print("First sample:", next(iter(ds))) ``` **Print Output:** ``` Original features: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id']) Processed features: None First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157, 0.0005188 , 0.00024414, ..., -0.00097656, -0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"} ``` ### Expected behavior The features should be those **not** removed by the `remove_column` method, i.e. audio and text. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.15 - PyArrow version: 9.0.0 - Pandas version: 1.3.5 (Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml) cc @polinaeterna @lhoestq

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I_kwDODunzps5XBMSf

Support cloud storage in load_dataset

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MEMBER

Would be nice to be able to do ```python data_files=["s3://..."] storage_options = {...} load_dataset(..., data_files=data_files, storage_options=storage_options) ``` or even ```python load_dataset("gs://...") ``` The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`. This has been requested several times already. Some users want to use their data from private cloud storage to train models related: https://github.com/huggingface/datasets/issues/3490 https://github.com/huggingface/datasets/issues/5244 [forum](https://discuss.huggingface.co/t/how-to-use-s3-path-with-load-dataset-with-streaming-true/25739/2)

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I_kwDODunzps5XAyJL

Import error

closed

NONE

https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28 Hy, I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong)

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I_kwDODunzps5W_1ba

load_dataset does not read jsonl metadata file properly

closed

NONE

### Describe the bug Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features. Below is code to reproduce my exact example/problem. ### Steps to reproduce the bug ```ruby dataset_link="19Unu89Ih_kP6zsE7f9Mkw8dy3NwHopRF" id = dataset_link output = 'Godardv01.zip' gdown.download(id=id, output=output, quiet=False) ds = load_dataset("imagefolder", data_dir="/kaggle/working/Volumes/TOSHIBA/Godard_imgs/Volumes/TOSHIBA/Godard_imgs/Full/train", split="train", drop_labels=False) print(ds) ``` ### Expected behavior I would expect that it returned "image" and "text" columns from the code above. ### Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 5.0.0 - Pandas version: 1.3.5

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I_kwDODunzps5W_B5y

Bug in downloading common_voice data and snall chunk of it to one's own hub

open