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Fix Text sample_by paragraph
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Fix #5316.
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Origin/fix missing features error
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`ImageFolder` performs poorly with large datasets
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### 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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Bug in sample_by="paragraph"
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2022-11-30T19:24:13Z
2022-12-01T07:58:47Z
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### 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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Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails
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2022-11-30T18:02:15Z
2022-12-01T07:00:44Z
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CONTRIBUTOR
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### 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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Datasets: classification_report() got an unexpected keyword argument 'suffix'
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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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Fix description of streaming in the docs
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We say that "the data is being downloaded progressively" which is not true, it's just streamed, so I fixed it. Probably I missed some other places where it is written? Also changed docstrings for `StreamingDownloadManager`'s `download` and `extract` to reflect the same, as these docstrings are displayed in the documentation cc @lhoestq
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Add DatasetDict.to_pandas
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From discussions in https://github.com/huggingface/datasets/issues/5189, for tabular data it doesn't really make sense to have to do ```python df = load_dataset(...)["train"].to_pandas() ``` because many datasets are not split. In this PR I added `to_pandas` to `DatasetDict` which returns the DataFrame of all the splits: ```python df = load_dataset(...).to_pandas() ``` EDIT: and if a dataset has multiple splits: ```python df = load_dataset(...).to_pandas(splits=["train", "test"]) # or df = load_dataset(...).to_pandas(splits="all") ``` I do have one question though @merveenoyan @adrinjalali @mariosasko: Should we raise an error if there are multiple splits and ask the user to choose one explicitly ?
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Add `features` param to `IterableDataset.map`
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## Description As suggested by @lhoestq in #3888, we should be adding the param `features` to `IterableDataset.map` so that the features can be preserved (not turned into `None` as that's the default behavior) whenever the user passes those as param, so as to be consistent with `Dataset.map`, as it provides the `features` param so that those are not inferred by default, but specified by the user, and later validated by `ArrowWriter`. This is internally handled already by the functions relying on `IterableDataset.map` such as `rename_column`, `rename_columns`, and `remove_columns` as described in #5287. ## Usage Example ```python from datasets import load_dataset, Features ds = load_dataset("rotten_tomatoes", split="validation", streaming=True) print(ds.info.features) ds = ds.map( lambda x: {"target": x["label"]}, features=Features( {"target": ds.info.features["label"], "label": ds.info.features["label"], "text": ds.info.features["text"]} ), ) print(ds.info.features) ```
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Support xPath for Windows pathnames
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This PR implements a string representation of `xPath`, which is valid for local paths (also windows) and remote URLs. Additionally, some `os.path` methods are fixed for remote URLs on Windows machines. Now, on Windows machines: ```python In [2]: str(xPath("C:\\dir\\file.txt")) Out[2]: 'C:\\dir\\file.txt' In [3]: str(xPath("http://domain.com/file.txt")) Out[3]: 'http://domain.com/file.txt' ```
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Close stream in `ArrowWriter.finalize` before inference error
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Ensure the file stream is closed in `ArrowWriter.finalize` before raising the `SchemaInferenceError` to avoid the `PermissionError` on Windows in `incomplete_dir`'s `shutil.rmtree`.
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Support `topdown` parameter in `xwalk`
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Add support for the `topdown` parameter in `xwalk` when `fsspec>=2022.11.0` is installed.
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Use correct dataset type in `from_generator` docs
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Use the correct dataset type in the `from_generator` docs (example with sharding).
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5,306
Can't use custom feature description when loading a dataset
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2022-11-28T07:55:44Z
2022-11-28T08:11:45Z
2022-11-28T08:11:44Z
CONTRIBUTOR
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### 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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5,305
Dataset joelito/mc4_legal does not work with multiple files
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No information
2
2022-11-28T00:16:16Z
2022-11-28T07:22:42Z
2022-11-28T07:22:42Z
CONTRIBUTOR
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### 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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5,304
timit_asr doesn't load the test split.
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2022-11-26T10:18:22Z
2022-11-26T10:18:22Z
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### 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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5,303
Skip dataset verifications by default
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2022-11-25T18:39:09Z
2022-11-25T18:44:23Z
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Skip the dataset verifications (split and checksum verifications, duplicate keys check) by default unless a dataset is being tested (`datasets-cli test/run_beam`). The main goal is to avoid running the checksum check in the default case due to how expensive it can be for large datasets. PS: Maybe we should deprecate `ignore_verifications`, which is `True` now by default, and give it a different name?
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Improve `use_auth_token` docstring and deprecate `use_auth_token` in `download_and_prepare`
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2022-11-25T17:09:21Z
2022-11-28T12:40:12Z
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Clarify in the docstrings what happens when `use_auth_token` is `None` and deprecate the `use_auth_token` param in `download_and_prepare`.
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Return a split Dataset in load_dataset
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...instead of a DatasetDict. ```python # now supported ds = load_dataset("squad") ds[0] for example in ds: pass # still works ds["train"] ds["validation"] # new ds.splits # Dict[str, Dataset] | None # soon to be supported (not in this PR) ds = load_dataset("dataset_with_no_splits") ds[0] for example in ds: pass ``` I implemented `Dataset.__getitem__` and `IterableDataset.__getitem__` to be able to get a split from a dataset. The splits are defined by the `ds.info.splits` dictionary. Therefore a dataset is a table that optionally has some splits defined in the dataset info. And a split dataset is the concatenation of all its splits. I made as little breaking changes as possible. Notable breaking changes: - `load_dataset("potato").keys() / .items() / .values() /` don't work anymore, since we don't return a dict - same for `for split_name in load_dataset("potato")`, since we now iterate on the examples - .. TODO: - [x] Update push_to_hub - [x] Update save_to_disk/load_from_disk - [ ] check for other breaking changes - [ ] fix existing tests - [ ] add new tests - [ ] docs This is related to https://github.com/huggingface/datasets/issues/5189, to extend `load_dataset` to return datasets without splits
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Use same `num_proc` for dataset download and generation
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Use the same `num_proc` value for data download and generation. Additionally, do not set `num_proc` to 16 in `DownloadManager` by default (`num_proc` now has to be specified explicitly).
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Fix xopen for Windows pathnames
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This PR fixes a bug in `xopen` function for Windows pathnames. Fix #5298.
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Bug in xopen with Windows pathnames
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2022-11-25T15:21:32Z
2022-11-29T08:21:25Z
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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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This PR fixes a bug in `xjoin` function with Windows pathnames. Fix #5296.
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2022-11-25T13:29:33Z
2022-11-29T08:05:13Z
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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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Extractions failed when .zip file located on read-only path (e.g., SageMaker FastFile mode)
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### 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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Support streaming datasets with pathlib.Path.with_suffix
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2022-11-24T18:04:38Z
2022-11-29T07:09:08Z
2022-11-29T07:06:32Z
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This PR extends the support in streaming mode for datasets that use `pathlib.Path.with_suffix`. Fix #5293.
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Support streaming datasets with pathlib.Path.with_suffix
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2022-11-24T17:52:08Z
2022-11-29T07:06:33Z
2022-11-29T07:06:33Z
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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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Missing documentation build for versions 2.7.1 and 2.6.2
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2022-11-24T09:42:10Z
2022-11-24T10:10:02Z
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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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[build doc] for v2.7.1 & v2.6.2
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2022-11-24T08:54:47Z
2022-11-24T09:14:10Z
2022-11-24T09:11:15Z
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Do NOT merge. Using this PR to build docs for [v2.7.1](https://github.com/huggingface/datasets/pull/5291/commits/f4914af20700f611b9331a9e3ba34743bbeff934) & [v2.6.2](https://github.com/huggingface/datasets/pull/5291/commits/025f85300a0874eeb90a20393c62f25ac0accaa0)
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fix error where reading breaks when batch missing an assigned column feature
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5,289
Added support for JXL images.
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2022-11-29T18:49:46Z
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JPEG-XL is the most advanced of the next-generation of image codecs, supporting both lossless and lossy files — with better compression and quality than PNG and JPG respectively. It has reduced the disk sizes and bandwidth required for many of the datasets I use. Pillow does not yet support JXL, but there's a plugin as a separate Python library that does (`pip install jxlpy`), and I've tested that this change works as expected when the plugin is imported. Dataset used for testing, you must `git pull` as loading it from Python won't work until `datasets-server` is also changed to support JXL files: https://huggingface.co/datasets/texturedesign/td01_natural-ground-textures The case where the plugin is not imported first raises an error: ``` PIL.UnidentifiedImageError: cannot identify image file 'td01/train/set01/01_145523.jxl' ``` In order to enable support for JXL even before pillow supports this, should this exception be handled with a better error message? I'd expect/hope JXL support to follow in one of the pillow quarterly releases in the next 6-9 months.
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Lossy json serialization - deserialization of dataset info
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2022-11-25T12:53:51Z
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### 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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Fix methods using `IterableDataset.map` that lead to `features=None`
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2022-11-23T15:33:25Z
2022-11-28T15:43:14Z
2022-11-28T12:53:22Z
CONTRIBUTOR
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As currently `IterableDataset.map` is setting the `info.features` to `None` every time as we don't know the output of the dataset in advance, `IterableDataset` methods such as `rename_column`, `rename_columns`, and `remove_columns`. that internally use `map` lead to the features being `None`. This PR is related to #3888, #5245, and #5284 ## ✅ Current solution The code in this PR is basically making sure that if the features were there since the beginning and a `rename_column`/`rename_columns` happens, those are kept and the rename is applied to the `Features` too. Also, if the features were not there before applying `rename_column`, `rename_columns` or `remove_columns`, a batch is prefetched and the features are being inferred (that could potentially be part of `IterableDataset.__init__` in case the `info.features` value is `None`). ## 💡 Ideas Some ideas were proposed in https://github.com/huggingface/datasets/issues/3888, but probably the most consistent solution even though it may take some time is to actually do the type inferencing during the `IterableDataset.__init__` in case the provided `info.features` is `None`, otherwise, we can just use the provided features. Additionally, as mentioned at https://github.com/huggingface/datasets/issues/3888, we could also include a `features` parameter to the `map` function, but that's probably more tedious. Also thanks to @lhoestq for sharing some ideas in both https://github.com/huggingface/datasets/issues/3888 and https://github.com/huggingface/datasets/issues/5245 :hugs:
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FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
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### 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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Save file name in embed_storage
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2022-11-24T14:11:41Z
2022-11-24T14:08:37Z
MEMBER
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Having the file name is useful in case we need to check the extension of the file (e.g. mp3), or in general in case it includes some metadata information (track id, image id etc.) Related to https://github.com/huggingface/datasets/issues/5276
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Features of IterableDataset set to None by remove column
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8
2022-11-23T10:54:59Z
2022-11-28T15:18:08Z
2022-11-28T12:53:24Z
CONTRIBUTOR
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### 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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Support cloud storage in load_dataset
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2022-11-22T14:00:10Z
2022-11-25T15:54:18Z
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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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Import error
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2022-11-22T12:56:43Z
2022-11-22T13:57:49Z
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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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5,279
Warn about checksums
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2022-11-22T10:58:48Z
2022-11-23T11:43:50Z
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It takes a lot of time on big datasets to compute the checksums, we should at least add a warning to notify the user about this step. I also mentioned how to disable it, and added a tqdm bar (delay=5 seconds) cc @ola13
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load_dataset does not read jsonl metadata file properly
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2022-11-22T10:24:46Z
2022-11-23T11:38:35Z
2022-11-23T11:38:35Z
NONE
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### 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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Remove YAML integer keys from class_label metadata
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Fix partially #5275.
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1,459,363,442
I_kwDODunzps5W_B5y
5,276
Bug in downloading common_voice data and snall chunk of it to one's own hub
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2022-11-22T08:17:53Z
2022-11-30T16:59:49Z
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### Describe the bug I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset Help please? ![image](https://user-images.githubusercontent.com/48530104/203260511-26df766f-6013-4eaf-be26-8aa13794def2.png) ### Steps to reproduce the bug So here is what I have done: 1. Download common_voice data 2. Trim part of it and publish it to my own repo. 3. Download data from my own repo, but am getting this error. ### Expected behavior There shouldn't be an error in downloading part of the data and publishing it to one's own repo ### Environment info common_voice 11
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1,459,358,919
I_kwDODunzps5W_AzH
5,275
YAML integer keys are not preserved Hub server-side
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2022-11-22T08:14:47Z
2022-11-23T08:44:16Z
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After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563): - YAML integer keys are not preserved server-side: they are transformed to strings - See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files - Original: ```yaml class_label: names: 0: B-long 1: B-short ``` - Returned by the server: ```yaml class_label: names: '0': B-long '1': B-short ``` - They are planning to enforce only string keys - Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json ```yaml "id2label": { "0": "CONTRADICTION", "1": "NEUTRAL", "2": "ENTAILMENT" } ``` On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`. Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched: ```python In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}]) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-18-974f07eea526> in <module> ----> 1 Features._from_yaml_list(ry) ~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data) 1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}") 1744 -> 1745 return cls.from_dict(from_yaml_inner(yaml_data)) 1746 1747 def encode_example(self, example): ~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj) 1739 elif isinstance(obj, list): 1740 names = [_feature.pop("name") for _feature in obj] -> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)} 1742 else: 1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}") ~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0) 1739 elif isinstance(obj, list): 1740 names = [_feature.pop("name") for _feature in obj] -> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)} 1742 else: 1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}") ~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj) 1734 return {"_type": snakecase_to_camelcase(obj["dtype"])} 1735 else: -> 1736 return from_yaml_inner(obj["dtype"]) 1737 else: 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]} ~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj) 1736 return from_yaml_inner(obj["dtype"]) 1737 else: -> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]} 1739 elif isinstance(obj, list): 1740 names = [_feature.pop("name") for _feature in obj] ~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature) 1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict): 1705 label_ids = sorted(feature["class_label"]["names"]) -> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)): 1707 raise ValueError( 1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing." TypeError: can only concatenate str (not "int") to str ``` TODO: - [x] Remove YAML integer keys from `dataset_info` metadata - [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7 - [ ] Communicate on the fix - [ ] Wait for adoption - [ ] Bulk edit the Hub to fix this in all canonical datasets
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1,458,646,455
I_kwDODunzps5W8S23
5,274
load_dataset possibly broken for gated datasets?
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2022-11-21T21:59:53Z
2022-11-28T02:50:42Z
2022-11-28T02:50:42Z
MEMBER
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### Describe the bug When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub: ``` [/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id) 165 if repo_id.count("/") > 1: 166 raise HFValidationError( --> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':" 168 f" '{repo_id}'. Use `repo_type` argument if needed." 169 ) HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed ``` ### Steps to reproduce the bug Install requirements: ``` pip install transformers pip install datasets # It works if you uncomment the following line, rolling back huggingface hub: # pip install huggingface-hub==0.10.1 ``` Then: ``` from datasets import load_dataset auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"] ``` ### Expected behavior Downloading of the datset ### Environment info Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing
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1,458,018,050
I_kwDODunzps5W55cC
5,273
download_mode="force_redownload" does not refresh cached dataset
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2022-11-21T14:12:43Z
2022-11-21T14:13:03Z
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### Describe the bug `load_datasets` does not refresh dataset when features are imported from external file, even with `download_mode="force_redownload"`. The bug is not limited to nested fields, however it is more likely to occur with nested fields. ### Steps to reproduce the bug To reproduce the bug 3 files are needed: `dataset.py` (contains dataset loading script), `schema.py` (contains features of dataset) and `main.py` (to run `load_datasets`) `dataset.py` ```python import datasets from schema import features class NewDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( features=features ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN ) ] def _generate_examples(self): data = [ {"id": 0, "nested": []}, {"id": 1, "nested": []} ] for key, example in enumerate(data): yield key, example ``` `schema.py` ```python import datasets features = datasets.Features( { "id": datasets.Value("int32"), "nested": [ {"text": datasets.Value("string")} ] } ) ``` `main.py` ```python import datasets a = datasets.load_dataset("dataset.py") print(a["train"].info.features) ``` Now if `main.py` is run it prints the following correct output: `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`. However, if f.e. the label of the feature "text" is changed to something else, f.e. to `schema.py` ```python import datasets features = datasets.Features( { "id": datasets.Value("int32"), "nested": [ {"textfoo": datasets.Value("string")} ] } ) ``` `main.py` still prints `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`, even if run with `download_mode="force_redownload"`. The only fix is to delete the folder in the cache. ### Expected behavior The cached dataset is deleted and refreshed when using `load_datasets` with `download_mode="force_redownload"`. ### Environment info - `datasets` version: 2.7.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.9 - PyArrow version: 10.0.0 - Pandas version: 1.3.5
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YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

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