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Load datasets from the Hub without requiring a dataset script
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[ "This is so cool, let us know if we can help with anything on the hub side (@Pierrci @elishowk) ๐ŸŽ‰ " ]
2021-07-12T08:45:17Z
2021-08-25T14:18:08Z
2021-08-25T14:18:08Z
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As a user I would like to be able to upload my csv/json/text/parquet/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script. Moreover I would like to be able to specify which file goes into which split using the `data_files` argument. This feature should be compatible with private repositories and dataset streaming. This can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv/json/text/parquet/etc.)
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โš›๏ธ๐Ÿ˜‡โš™๏ธ๐Ÿ”‘
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can't set verbosity for `metric.py`
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[ "Thanks @thomas-happify for reporting and thanks @mariosasko for the fix." ]
2021-07-10T20:23:45Z
2021-07-12T05:54:29Z
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## Describe the bug ``` [2021-07-10 20:13:11,528][datasets.utils.filelock][INFO] - Lock 139705371374976 acquired on /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.lock [2021-07-10 20:13:11,529][datasets.arrow_writer][INFO] - Done writing 32 examples in 6100 bytes /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow. [2021-07-10 20:13:11,531][datasets.arrow_dataset][INFO] - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns. [2021-07-10 20:13:11,543][/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric.py][INFO] - Removing /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow ``` As you can see, `datasets` logging come from different places. `filelock`, `arrow_writer` & `arrow_dataset` comes from `datasets.*` which are expected However, `metric.py` logging comes from `/conda/envs/myenv/lib/python3.8/site-packages/datasets/` So when setting `datasets.utils.logging.set_verbosity_error()`, it still logs the last message which is annoying during evaluation. I had to do ``` logging.getLogger("/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric").setLevel(logging.ERROR) ``` to fully mute these messages ## Expected results it shouldn't log these messages when setting `datasets.utils.logging.set_verbosity_error()` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: tried both 1.8.0 & 1.9.0 - Platform: Ubuntu 18.04.5 LTS - Python version: 3.8.10 - PyArrow version: 3.0.0
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Integration with AugLy
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[ "Hi,\r\n\r\nyou can define your own custom formatting with `Dataset.set_transform()` and then run the tokenizer with the batches of augmented data as follows:\r\n```python\r\ndset = load_dataset(\"imdb\", split=\"train\") # Let's say we are working with the IMDB dataset\r\ndset.set_transform(lambda ex: {\"text\": ...
2021-07-10T00:03:09Z
2023-07-20T13:18:48Z
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**Is your feature request related to a problem? Please describe.** Facebook recently launched a library, [AugLy](https://github.com/facebookresearch/AugLy) , that has a unified API for augmentations for image, video and text. It would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial. **Describe the solution you'd like** The biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader. **Describe alternatives you've considered** One possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.
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[ "Hi @liyucheng09, thanks for reporting.\r\n\r\nApparently this issue has to do with your environment setup. One question: is your data in an NFS share? Some people have reported this error when using `fcntl` to write to an NFS share... If this is the case, then it might be that your NFS just may not be set up to pr...
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## Describe the bug It seems that the `filelock.py` went error. ``` >>> ds=load_dataset('xsum') ^CTraceback (most recent call last): File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) OSError: [Errno 37] No locks available ``` According to error log, it is OSError, but there is an `except` in the `_acquire` function. ``` def _acquire(self): open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC try: fd = os.open(self._lock_file, open_mode) except (IOError, OSError): pass else: self._lock_file_fd = fd return None ``` I don't know why it stucked rather than `pass` directly. I am not quite familiar with filelock operation, so any help is highly appriciated. ## Steps to reproduce the bug ```python ds = load_dataset('xsum') ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` >>> ds=load_dataset('xsum') ^CTraceback (most recent call last): File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) OSError: [Errno 37] No locks available During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 818, in load_dataset use_auth_token=use_auth_token, File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 470, in prepare_module with FileLock(lock_path): File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 323, in __enter__ self.acquire() File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 272, in acquire self._acquire() File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) KeyboardInterrupt ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid - Python version: 3.6.13 - PyArrow version: 4.0.1
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Jsonlines export error
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[ "Thanks for reporting @TevenLeScao! I'm having a look...", "(not sure what just happened on the assignations sorry)", "For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.", "@TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it...
2021-07-09T14:02:05Z
2021-07-09T15:29:07Z
2021-07-09T15:28:33Z
CONTRIBUTOR
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## Describe the bug When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default ## Steps to reproduce the bug This what I'm running: in python: ``` from datasets import load_dataset ptb = load_dataset("ptb_text_only") ptb["train"].to_json("ptb.jsonl") ``` then out of python: ``` head -10000 ptb.jsonl ``` ## Expected results Properly separated lines ## Actual results The last line is a concatenation of two lines ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.1.dev0 - Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyArrow version: 4.0.1
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Streaming local gzip compressed JSON line files is not working
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[ "Updating to pyarrow-4.0.1 didn't fix the issue", "Here is an exemple dataset with 2 of these compressed JSON files: https://huggingface.co/datasets/thomwolf/github-python", "Hi @thomwolf, thanks for reporting.\r\n\r\nIt seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj...
2021-07-07T11:36:33Z
2021-07-20T09:50:19Z
2021-07-08T16:08:41Z
MEMBER
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## Describe the bug Using streaming to iterate on local gzip compressed JSON files raise a file not exist error ## Steps to reproduce the bug ```python from datasets import load_dataset streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True) next(iter(streamed_dataset)) ``` ## Actual results ``` FileNotFoundError Traceback (most recent call last) <ipython-input-6-27a664e29784> in <module> ----> 1 next(iter(streamed_dataset)) ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self) 336 337 def __iter__(self): --> 338 for key, example in self._iter(): 339 if self.features: 340 # we encode the example for ClassLabel feature types for example ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in _iter(self) 333 else: 334 ex_iterable = self._ex_iterable --> 335 yield from ex_iterable 336 337 def __iter__(self): ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self) 76 77 def __iter__(self): ---> 78 for key, example in self.generate_examples_fn(**self.kwargs): 79 yield key, example 80 ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in wrapper(**kwargs) 282 def wrapper(**kwargs): 283 python_formatter = PythonFormatter() --> 284 for key, table in generate_tables_fn(**kwargs): 285 batch = python_formatter.format_batch(table) 286 for i, example in enumerate(_batch_to_examples(batch)): ~/Documents/GitHub/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files, original_files) 85 file, 86 read_options=self.config.pa_read_options, ---> 87 parse_options=self.config.pa_parse_options, 88 ) 89 except pa.ArrowInvalid as err: ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json._get_reader() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_input_stream() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_native_file() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile.__cinit__() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile._open_readable() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() FileNotFoundError: [Errno 2] Failed to open local file 'gzip://file-000000000000.json::/Users/thomwolf/github-dataset/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory ``` ## Environment info - `datasets` version: 1.9.1.dev0 - Platform: Darwin-19.6.0-x86_64-i386-64bit - Python version: 3.7.7 - PyArrow version: 1.0.0
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[Metrics] addition of wiki_split metrics
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2021-07-07T10:56:04Z
2021-07-12T22:34:31Z
2021-07-12T22:34:31Z
CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** While training the model on sentence split the task in English we require to evaluate the trained model on `Exact Match`, `SARI` and `BLEU` score like this ![image](https://user-images.githubusercontent.com/26653468/124746876-ff5a3380-df3e-11eb-9a01-4b48db7a6694.png) While training we require metrics which can give all the output Currently, we don't have an exact match for text normalized data **Describe the solution you'd like** A custom metrics for wiki_split that can calculate these three values and provide it in the form of a single dictionary For exact match, we can refer to [this](https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py) **Describe alternatives you've considered** Two metrics are already present one more can be added for an exact match then we can run all three metrics in training script #self-assign
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Add option to delete temporary files (e.g. extracted files) when loading dataset
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[ "Hi !\r\nIf we want something more general, we could either\r\n1. delete the extracted files after the arrow data generation automatically, or \r\n2. delete each extracted file during the arrow generation right after it has been closed.\r\n\r\nSolution 2 is better to save disk space during the arrow generation. Is ...
2021-07-07T07:56:16Z
2021-07-19T09:08:18Z
2021-07-19T09:08:18Z
MEMBER
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I'm loading a dataset constituted of 44 GB of compressed JSON files. When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter. I can maybe tackle this one in the JSON script unless you want a more general solution.
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Crash when using multiprocessing (`num_proc` > 1) on `filter` and all samples are discarded
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2021-07-06T16:53:25Z
2021-07-07T12:50:31Z
2021-07-07T12:50:31Z
CONTRIBUTOR
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## Describe the bug If `filter` is applied to a dataset using multiprocessing (`num_proc` > 1) and all sharded datasets are empty afterwards (due to all samples being discarded), the program crashes. ## Steps to reproduce the bug ```python from datasets import Dataset data = Dataset.from_dict({'id': [0,1]}) data.filter(lambda x: False, num_proc=2) ``` ## Expected results An empty table should be returned without crashing. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/user/venv/lib/python3.8/site-packages/datasets/fingerprint.py", line 397, in wrapper out = func(self, *args, **kwargs) File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2143, in filter return self.map( File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1738, in map result = concatenate_datasets(transformed_shards) File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3267, in concatenate_datasets table = concat_tables(tables_to_concat, axis=axis) File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 853, in concat_tables return ConcatenationTable.from_tables(tables, axis=axis) File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 713, in from_tables blocks = to_blocks(tables[0]) IndexError: list index out of range ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-5.12.11-300.fc34.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.8.10 - PyArrow version: 3.0.0
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Unable to download omp dataset
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[ "Hi @erikadistefano , thanks for reporting the issue.\r\n\r\nI have created a Pull Request that should fix it. \r\n\r\nOnce merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp datas...
2021-07-06T14:00:52Z
2021-07-07T12:56:35Z
2021-07-07T12:56:35Z
NONE
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## Describe the bug The omp dataset cannot be downloaded because of a DuplicatedKeysError ## Steps to reproduce the bug from datasets import load_dataset omp = load_dataset('omp', 'posts_labeled') print(omp) ## Expected results This code should download the omp dataset and print the dictionary ## Actual results Downloading and preparing dataset omp/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to /home/erika_distefano/.cache/huggingface/datasets/omp/posts_labeled/1.1.0/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b... 0 examples [00:00, ? examples/s]2021-07-06 09:43:55.868815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0 Traceback (most recent call last): File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 990, in _prepare_split writer.write(example, key) File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 338, in write self.check_duplicate_keys() File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 3326 Keys should be unique and deterministic in nature During handling of the above exception, another exception occurred: Traceback (most recent call last): File "hf_datasets.py", line 32, in <module> omp = load_dataset('omp', 'posts_labeled') File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset use_auth_token=use_auth_token, File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 992, in _prepare_split num_examples, num_bytes = writer.finalize() File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 409, in finalize self.check_duplicate_keys() File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 3326 Keys should be unique and deterministic in nature ## Environment info - `datasets` version: 1.8.0 - Platform: Ubuntu 18.04.4 LTS - Python version: 3.6.9 - PyArrow version: 3.0.0
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Transformer Class on dataset
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[ "Hi ! Do you have an example in mind that shows how this could be useful ?", "Example:\n\nMerge 2 datasets into one datasets\n\nLabel extraction from dataset\n\ndataset(text, label)\n โ€”> dataset(text, newlabel)\n\nTextCleaning.\n\n\nFor image dataset, \nTransformation are easier (ie linear algebra).\n\n\n\n\n\n...
2021-07-06T07:27:15Z
2022-11-02T14:26:09Z
2022-11-02T14:26:09Z
NONE
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Just wondering if you have intenttion to create TransformerClass : dataset --> dataset and make determnistic transformation (ie not fit).
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ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets
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[ "Hi @profsatwinder.\r\n\r\nIt looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.", "@albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again. " ]
2021-07-06T03:20:55Z
2021-07-06T05:59:49Z
2021-07-06T05:59:49Z
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Error traceback: --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-8-a7b592d3bca0> in <module>() 1 from datasets import load_dataset, load_metric 2 ----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation") 4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test") 9 frames /root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>() 19 20 import datasets ---> 21 from datasets.tasks import AutomaticSpeechRecognition 22 23 ModuleNotFoundError: No module named 'datasets.tasks'
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Cached dataset overflowing disk space
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[ "Hi! I'm transferring this issue over to `datasets`", "I'm using the datasets concatenate dataset to combine the datasets and then train.\r\ntrain_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])\r\n\r\n", "Hi @BirgerMoell.\r\n\r\nYou have several options:\r\n- to set caching to be store...
2021-07-05T10:43:19Z
2021-07-19T09:08:19Z
2021-07-19T09:08:19Z
CONTRIBUTOR
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I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb). The cache folder is 500gb (and now my disk space is full). Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files). This might not technically be a bug, but I was unsure and I felt that the bug was the closest one. Traceback (most recent call last): File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker result = (True, func(*args, **kwds)) File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper out = func(self, *args, **kwargs) File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single writer.finalize() File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize self.pa_writer.close() File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device """ The above exception was the direct cause of the following exception:
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sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index
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[ "Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so ...
2021-07-04T15:39:49Z
2021-07-07T13:18:51Z
2021-07-07T13:18:51Z
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## Describe the bug The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start']. For example: id = '56d1f453e7d4791d009025bd' answers = {'text': ['Pure Land'], 'answer_start': [146]} However the actual text in context at location 146 is 'ure Land,' Which is an off-by-one error from the correct answer. ## Steps to reproduce the bug ```python import datasets def check_context_answer_alignment(example): for a_idx in range(len(example['answers']['text'])): # check raw dataset for answer consistency between context and answer answer_text = example['answers']['text'][a_idx] a_st_idx = example['answers']['answer_start'][a_idx] a_end_idx = a_st_idx + len(example['answers']['text'][a_idx]) answer_text_from_context = example['context'][a_st_idx:a_end_idx] if answer_text != answer_text_from_context: #print(example['id']) return False return True dataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True) start_len = len(dataset) dataset = dataset.filter(check_context_answer_alignment, num_proc=1, keep_in_memory=True) end_len = len(dataset) print('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len)) ``` ## Expected results This code should result in 0 rows being filtered out from the dataset. ## Actual results This filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location. This code will reproduce the problem and produce the following count: "258 instances contain mis-alignment between the answer text and answer index." ## Environment info Steps to rebuilt the Conda environment: ``` # create a virtual environment to stuff all these packages into conda create -n round8 python=3.8 -y # activate the virtual environment conda activate round8 # install pytorch (best done through conda to handle cuda dependencies) conda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia pip install jsonpickle transformers datasets matplotlib ``` OS: Ubuntu 20.04 Python 3.8 Result of `conda env export`: ``` name: round8 channels: - pytorch-lts - nvidia - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=4.5=1_gnu - blas=1.0=mkl - brotlipy=0.7.0=py38h27cfd23_1003 - bzip2=1.0.8=h7b6447c_0 - ca-certificates=2021.5.25=h06a4308_1 - certifi=2021.5.30=py38h06a4308_0 - cffi=1.14.5=py38h261ae71_0 - chardet=4.0.0=py38h06a4308_1003 - cryptography=3.4.7=py38hd23ed53_0 - cudatoolkit=11.1.74=h6bb024c_0 - ffmpeg=4.2.2=h20bf706_0 - freetype=2.10.4=h5ab3b9f_0 - gmp=6.2.1=h2531618_2 - gnutls=3.6.15=he1e5248_0 - idna=2.10=pyhd3eb1b0_0 - intel-openmp=2021.2.0=h06a4308_610 - jpeg=9b=h024ee3a_2 - lame=3.100=h7b6447c_0 - lcms2=2.12=h3be6417_0 - ld_impl_linux-64=2.35.1=h7274673_9 - libffi=3.3=he6710b0_2 - libgcc-ng=9.3.0=h5101ec6_17 - libgomp=9.3.0=h5101ec6_17 - libidn2=2.3.1=h27cfd23_0 - libopus=1.3.1=h7b6447c_0 - libpng=1.6.37=hbc83047_0 - libstdcxx-ng=9.3.0=hd4cf53a_17 - libtasn1=4.16.0=h27cfd23_0 - libtiff=4.2.0=h85742a9_0 - libunistring=0.9.10=h27cfd23_0 - libuv=1.40.0=h7b6447c_0 - libvpx=1.7.0=h439df22_0 - libwebp-base=1.2.0=h27cfd23_0 - lz4-c=1.9.3=h2531618_0 - mkl=2021.2.0=h06a4308_296 - mkl-service=2.3.0=py38h27cfd23_1 - mkl_fft=1.3.0=py38h42c9631_2 - mkl_random=1.2.1=py38ha9443f7_2 - ncurses=6.2=he6710b0_1 - nettle=3.7.3=hbbd107a_1 - ninja=1.10.2=hff7bd54_1 - numpy=1.20.2=py38h2d18471_0 - numpy-base=1.20.2=py38hfae3a4d_0 - olefile=0.46=py_0 - openh264=2.1.0=hd408876_0 - openssl=1.1.1k=h27cfd23_0 - pillow=8.2.0=py38he98fc37_0 - pip=21.1.2=py38h06a4308_0 - pycparser=2.20=py_2 - pyopenssl=20.0.1=pyhd3eb1b0_1 - pysocks=1.7.1=py38h06a4308_0 - python=3.8.10=h12debd9_8 - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0 - readline=8.1=h27cfd23_0 - requests=2.25.1=pyhd3eb1b0_0 - setuptools=52.0.0=py38h06a4308_0 - six=1.16.0=pyhd3eb1b0_0 - sqlite=3.35.4=hdfb4753_0 - tk=8.6.10=hbc83047_0 - torchtext=0.9.1=py38 - torchvision=0.9.1=py38_cu111 - typing_extensions=3.7.4.3=pyha847dfd_0 - urllib3=1.26.4=pyhd3eb1b0_0 - wheel=0.36.2=pyhd3eb1b0_0 - x264=1!157.20191217=h7b6447c_0 - xz=5.2.5=h7b6447c_0 - zlib=1.2.11=h7b6447c_3 - zstd=1.4.9=haebb681_0 - pip: - click==8.0.1 - cycler==0.10.0 - datasets==1.8.0 - dill==0.3.4 - filelock==3.0.12 - fsspec==2021.6.0 - huggingface-hub==0.0.8 - joblib==1.0.1 - jsonpickle==2.0.0 - kiwisolver==1.3.1 - matplotlib==3.4.2 - multiprocess==0.70.12.2 - packaging==20.9 - pandas==1.2.4 - pyarrow==3.0.0 - pyparsing==2.4.7 - python-dateutil==2.8.1 - pytz==2021.1 - regex==2021.4.4 - sacremoses==0.0.45 - tokenizers==0.10.3 - tqdm==4.49.0 - transformers==4.6.1 - xxhash==2.0.2 prefix: /home/mmajurski/anaconda3/envs/round8 ```
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Error iteration over IterableDataset using Torch DataLoader
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[ "Hi ! This is because you first need to format the dataset for pytorch:\r\n\r\n```python\r\n>>> import torch\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> torch_iterable_dataset = dataset.with_format(\"torch\")\r...
2021-07-02T19:55:58Z
2021-07-20T09:04:45Z
2021-07-05T23:48:23Z
NONE
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## Describe the bug I have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler. I am not sure if this is how it is meant to be used, but that's what seemed reasonable to me. ## Steps to reproduce the bug 1. Does not work. ```python >>> from datasets import load_dataset >>> dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True) >>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4) >>> dataloader.sampler <torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208> >>> for batch in dataloader: ... print(batch) ``` 2. Works. ```python import torch from torch.utils.data import Dataset, IterableDataset, DataLoader class CustomIterableDataset(IterableDataset): 'Characterizes a dataset for PyTorch' def __init__(self, data): 'Initialization' self.data = data def __iter__(self): return iter(self.data) data = list(range(12)) dataset = CustomIterableDataset(data) dataloader = DataLoader(dataset, batch_size=4) print("dataloader: ", dataloader.sampler) for batch in dataloader: print(batch) ``` ## Expected results To get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different. dataloader: <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50> tensor([0, 1, 2, 3]) tensor([4, 5, 6, 7]) tensor([ 8, 9, 10, 11]) ## Actual results <torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208> ... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ data = self._next_data() File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 474, in _next_data index = self._next_index() # may raise StopIteration File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index return next(self._sampler_iter) # may raise StopIteration File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 227, in __iter__ for idx in self.sampler: File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 67, in __iter__ return iter(range(len(self.data_source))) TypeError: object of type 'IterableDataset' has no len() ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: '1.8.1.dev0' - Platform: Linux - Python version: Python 3.6.8 - PyArrow version: '3.0.0'
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Support Zstandard compressed files
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[ "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.\r\n\r\n```\r\n!pip install zstandard\r\nfrom datasets import load_dataset\r\n\r\nlds = load_dataset(\r\n \"j...
2021-07-01T08:37:04Z
2023-01-03T15:34:01Z
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Add support for Zstandard compressed files: https://facebook.github.io/zstd/
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Weights of model checkpoint not initialized for RobertaModel for Bertscore
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[ "Hi @suzyahyah, thanks for reporting.\r\n\r\nThe message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:\r\n```\r\nSome weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.wei...
2021-06-29T18:55:23Z
2021-07-01T07:08:59Z
2021-06-30T07:35:49Z
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When applying bertscore out of the box, ```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']``` Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html ``` from datasets import load_metric metric = load_metric('bertscore') # Example of typical usage for batch in dataset: inputs, references = batch predictions = model(inputs) metric.add_batch(predictions=predictions, references=references) score = metric.compute(lang="en") #score = metric.compute(model_type="roberta-large") # gives the same error ``` I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo.... ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27 - Python version: 3.9.5 - PyArrow version: 3.0.0
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[ "It is probably worth noting here that the [documentation](https://huggingface.co/docs/datasets/process#concatenate) is misleading (indicating that it does work for IterableDatasets):\r\n\r\n> You can also mix several datasets together by taking alternating examples from each one to create a new dataset. This is kn...
2021-06-29T08:59:41Z
2022-06-28T21:15:04Z
2022-06-28T21:15:04Z
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Currently `concatenate_datasets` only works for map-style `Dataset`. It would be nice to have it work for `IterableDataset` objects as well. It would simply chain the iterables of the iterable datasets.
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2021-06-29T08:57:24Z
2021-07-01T09:33:33Z
2021-07-01T09:33:33Z
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Currently the `interleave_datasets` functions only works for `IterableDataset`. Let's make it work for map-style `Dataset` objects as well. It would work the same way: either alternate between the datasets in order or randomly given probabilities specified by the user.
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Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`
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[ "Hi ! I just tried to reproduce what you said:\r\n- create a local builder class\r\n- use `load_dataset`\r\n- update the builder class code\r\n- use `load_dataset` again (with or without `ignore_verifications=True`)\r\nAnd it creates a new cache, as expected.\r\n\r\nWhat modifications did you do to your builder's c...
2021-06-29T07:43:03Z
2022-08-04T11:58:36Z
2022-08-04T11:58:36Z
CONTRIBUTOR
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## Describe the bug If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets. ## Steps to reproduce the bug - Create a local dataset builder class - load the local builder class file using `load_dataset` and let the cache build - update the file's content - The cache should rebuilt. ## Expected results With `ignore_verifications=True`, `load_dataset` should pick up existing cache. ## Actual results Creates new cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.7 - PyArrow version: 3.0.0
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Memory usage consistently increases when processing a dataset with `.map`
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[ "Hi ! Can you share the function you pass to `map` ?\r\nI know you mentioned it would be hard to share some code but this would really help to understand what happened", "This is the same behavior as in #4883, so I'm closing this issue as a duplicate. " ]
2021-06-28T18:31:58Z
2023-07-20T13:34:10Z
2023-07-20T13:34:10Z
CONTRIBUTOR
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## Describe the bug I have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help. ## Steps to reproduce the bug Providing code as it is would be hard. I can provide a MVP if that helps. ## Expected results Memory usage should become consistent after some time following the launch of processing. ## Actual results Memory usage keeps on increasing. ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.7 - PyArrow version: 3.0.0
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Better DuplicateKeysError error to help the user debug the issue
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[ "excuse me, my `datasets` version is `2.2.2`, but I also just see the error info like \r\n```\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 0\r\nKeys should be unique and deterministic in nature\r\n```", "Hi ! for which dataset do you have this error ?\r\n\r\nAlso note that this is...
2021-06-28T13:50:57Z
2022-06-28T09:26:04Z
2022-06-28T09:26:04Z
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As mentioned in https://github.com/huggingface/datasets/issues/2552 it would be nice to improve the error message when a dataset fails to build because there are duplicate example keys. The current one is ```python datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 48 Keys should be unique and deterministic in nature ``` and we could have something that guides the user to debugging the issue: ```python DuplicateKeysError: both 42th and 1337th examples have the same keys `48`. Please fix the dataset script at <path/to/the/dataset/script> ```
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Multilabel metrics not supported
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[ "Hi @GuillemGSubies, thanks for reporting.\r\n\r\nI have made a PR to fix this issue and allow metrics to be computed also for multilabel classification problems.", "Looks nice, thank you very much! ๐Ÿš€ ", "Sorry for reopening but I just noticed that the `_compute` method for the F1 metric is still not good enou...
2021-06-28T11:09:46Z
2021-10-13T12:29:13Z
2021-07-08T08:40:15Z
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When I try to use a metric like F1 macro I get the following error: ``` TypeError: int() argument must be a string, a bytes-like object or a number, not 'list' ``` There is an explicit casting here: https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/src/datasets/features.py#L274 And looks like this is because here https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/metrics/f1/f1.py#L88 the features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work: ```python class F1(datasets.Metric): def _info(self): return datasets.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features( { "predictions": datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")), } ), reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"], ) def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None): return { "f1": f1_score( references, predictions, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight, ), } ```
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load_dataset("web_nlg") NonMatchingChecksumError
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[ "Hi ! Thanks for reporting. This is due to the WebNLG repository that got updated today.\r\nI just pushed a fix at #2558 - this shouldn't happen anymore in the future.", "This is fixed on `master` now :)\r\nWe'll do a new release soon !" ]
2021-06-28T09:26:46Z
2021-06-28T17:23:39Z
2021-06-28T17:23:16Z
NONE
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Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('web_nlg', name="release_v3.0_en", split="dev") ``` Gives ``` NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip'] ``` ## Environment info - `datasets` version: 1.8.0 - Platform: macOS-11.3.1-x86_64-i386-64bit - Python version: 3.9.4 - PyArrow version: 3.0.0 Also tested on Linux, with python 3.6.8
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Keys should be unique error on code_search_net
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[ "Two questions:\r\n- with `datasets-cli env` we don't have any information on the dataset script version used. Should we give access to this somehow? Either as a note in the Error message or as an argument with the name of the dataset to `datasets-cli env`?\r\n- I don't really understand why the id is duplicated in...
2021-06-28T09:15:20Z
2021-09-06T14:08:30Z
2021-09-02T08:25:29Z
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## Describe the bug Loading `code_search_net` seems not possible at the moment. ## Steps to reproduce the bug ```python >>> load_dataset('code_search_net') Downloading: 8.50kB [00:00, 3.09MB/s] Downloading: 19.1kB [00:00, 10.1MB/s] No config specified, defaulting to: code_search_net/all Downloading and preparing dataset code_search_net/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to /Users/thomwolf/.cache/huggingface/datasets/code_search_net/all/1.0.0/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a... Traceback (most recent call last): File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/builder.py", line 1067, in _prepare_split writer.write(example, key) File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 343, in write self.check_duplicate_keys() File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 354, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 48 Keys should be unique and deterministic in nature ``` ## Environment info - `datasets` version: 1.8.1.dev0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 2.0.0
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Allow for incremental cumulative metric updates in a distributed setup
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2021-06-27T15:00:58Z
2021-09-26T13:42:39Z
2021-09-26T13:42:39Z
CONTRIBUTOR
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Currently, using a metric allows for one of the following: - Per example/batch metrics - Cumulative metrics over the whole data What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation. Since most metrics are just an average of per-example metrics (which aren't?), an efficient calculation can be done as follows: `((score_cumulative * n_cumulative) + (score_new * n_new)) / (n_cumulative+ n_new)` where `n` and `score` refer to number of examples and metric score, `cumulative` refers to the cumulative metric and `new` refers to the addition of new examples. If you don't want to add this capability in the library, a simple solution exists so users can do it themselves: It is easy to implement for a single process setup, but in a distributed one there is no way to get the correct `n_new`. The solution for this is to return the number of examples that was used to compute the metrics in `.compute()` by adding the following line here: https://github.com/huggingface/datasets/blob/5a3221785311d0ce86c2785b765e86bd6997d516/src/datasets/metric.py#L402-L403 ``` output["number_of_examples"] = len(predictions) ``` and also remove the log message here so it won't spam: https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/src/datasets/metric.py#L411 If this change is ok with you, I'll open a pull request.
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Handling unlabeled datasets
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[ "Hi @nelson-liu,\r\n\r\nYou can pass the parameter `features` to `load_dataset`: https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset\r\n\r\nIf you look at the code of the MNLI script you referred in your question (https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi...
2021-06-25T04:32:23Z
2021-06-25T21:07:57Z
2021-06-25T21:07:56Z
NONE
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Hi! Is there a way for datasets to produce unlabeled instances (e.g., the `ClassLabel` can be nullable). For example, I want to use the MNLI dataset reader ( https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi_nli.py ) on a file that doesn't have the `gold_label` field. I tried setting `"label": data.get("gold_label")`, but got the following error: ``` File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset use_auth_token=use_auth_token, File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 989, in _prepare_split example = self.info.features.encode_example(record) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 953, in encode_example return encode_nested_example(self, example) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in encode_nested_example k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in <dictcomp> k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 875, in encode_nested_example return schema.encode_example(obj) File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 653, in encode_example if not -1 <= example_data < self.num_classes: TypeError: '<=' not supported between instances of 'int' and 'NoneType' ``` What's the proper way to handle reading unlabeled datasets, especially for downstream usage with Transformers?
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Field order issue in loading json
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[ "Hi @luyug, thanks for reporting.\r\n\r\nThe good news is that we fixed this issue only 9 days ago: #2507.\r\n\r\nThe patch is already in the master branch of our repository and it will be included in our next `datasets` release version 1.9.0.\r\n\r\nFeel free to reopen the issue if the problem persists." ]
2021-06-24T13:29:53Z
2021-06-24T14:36:43Z
2021-06-24T14:34:05Z
NONE
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## Describe the bug The `load_dataset` function expects columns in alphabetical order when loading json files. Similar bug was previously reported for csv in #623 and fixed in #684. ## Steps to reproduce the bug For a json file `j.json`, ``` {"c":321, "a": 1, "b": 2} ``` Running the following, ``` f= datasets.Features({'a': Value('int32'), 'b': Value('int32'), 'c': Value('int32')}) json_data = datasets.load_dataset('json', data_files='j.json', features=f) ``` ## Expected results A successful load. ## Actual results ``` File "pyarrow/table.pxi", line 1409, in pyarrow.lib.Table.cast ValueError: Target schema's field names are not matching the table's field names: ['c', 'a', 'b'], ['a', 'b', 'c'] ``` ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyArrow version: 3.0.0
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switching some low-level log.info's to log.debug?
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[ "Hi @stas00, thanks for pointing out this issue with logging.\r\n\r\nI agree that `datasets` can sometimes be too verbose... I can create a PR and we could discuss there the choice of the log levels for different parts of the code." ]
2021-06-23T19:26:55Z
2021-06-25T13:40:19Z
2021-06-25T13:40:19Z
CONTRIBUTOR
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In https://github.com/huggingface/transformers/pull/12276 we are now changing the examples to have `datasets` on the same log level as `transformers`, so that one setting can do a consistent logging across all involved components. The trouble is that now we get a ton of these: ``` 06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 acquired on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock 06/23/2021 12:15:31 - INFO - datasets.arrow_writer - Done writing 50 examples in 12280 bytes /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow. 06/23/2021 12:15:31 - INFO - datasets.arrow_dataset - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns. 06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 released on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock ``` May I suggest that these can be `log.debug` as it's no informative to the user. More examples: these are not informative - too much information: ``` 06/23/2021 12:14:26 - INFO - datasets.load - Checking /home/stas/.cache/huggingface/datasets/downloads/459933f1fe47711fad2f6ff8110014ff189120b45ad159ef5b8e90ea43a174fa.e23e7d1259a8c6274a82a42a8936dd1b87225302c6dc9b7261beb3bc2daac640.py for additional imports. 06/23/2021 12:14:27 - INFO - datasets.builder - Constructing Dataset for split train, validation, test, from /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a ``` While these are: ``` 06/23/2021 12:14:27 - INFO - datasets.info - Loading Dataset Infos from /home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt16/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a 06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a) ``` I also realize that `transformers` examples don't have do use `info` for `datasets` to let the default `warning` keep logging to less noisy. But I think currently the log levels are slightly misused and skewed by 1 level. Many `warnings` will better be `info`s and most `info`s be `debug`. e.g.: ``` 06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a) ``` why is this a warning? it is informing me that the cache is used, there is nothing to be worried about. I'd have it as `info`. Warnings are typically something that's bordering error or the first thing to check when things don't work as expected. infrequent info is there to inform of the different stages or important events. Everything else is debug. At least the way I understand things.
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`datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa/adversarialQA`
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[ "very much related: https://github.com/huggingface/datasets/pull/2333", "Hi @VictorSanh, thank you for reporting this issue with duplicated keys.\r\n\r\n- The issue with \"adversarial_qa\" was fixed 23 days ago: #2433. Current version of `datasets` (1.8.0) includes the patch.\r\n- I am investigating the issue wit...
2021-06-23T18:41:16Z
2021-06-25T21:50:05Z
2021-06-24T14:57:08Z
CONTRIBUTOR
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## Describe the bug Failure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("drop") load_dataset("adversarial_qa", "adversarialQA") ``` ## Expected results The examples keys should be unique. ## Actual results ```bash >>> load_dataset("drop") Using custom data configuration default Downloading and preparing dataset drop/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to /home/hf/.cache/huggingface/datasets/drop/default/0.1.0/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset use_auth_token=use_auth_token, File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 992, in _prepare_split num_examples, num_bytes = writer.finalize() File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 409, in finalize self.check_duplicate_keys() File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 28553293-d719-441b-8f00-ce3dc6df5398 Keys should be unique and deterministic in nature ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.7.0 - Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.10 - PyArrow version: 3.0.0
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2,536
Use `Audio` features for `AutomaticSpeechRecognition` task template
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[ "I'm just retaking and working on #2324. ๐Ÿ˜‰ ", "Resolved via https://github.com/huggingface/datasets/pull/4006." ]
2021-06-22T15:07:21Z
2022-06-01T17:18:16Z
2022-06-01T17:18:16Z
MEMBER
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In #2533 we added a task template for speech recognition that relies on the file paths to the audio files. As pointed out by @SBrandeis this is brittle as it doesn't port easily across different OS'. The solution is to use dedicated `Audio` features when casting the dataset. These features are not yet available in `datasets`, but should be included in the `AutomaticSpeechRecognition` template once they are.
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Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task
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[ "Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?", "> Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?\r\n\r\nOh, I am sorry\r\nI would reopen the post on huggingface/transformers" ]
2021-06-22T10:08:18Z
2021-06-23T05:17:25Z
2021-06-23T05:17:25Z
CONTRIBUTOR
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[This colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https://huggingface.co/transformers/custom_datasets.html#tok-ner). The pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https://en.wikipedia.org/wiki/Kana_ligature) break the alignment of `return_offsets_mapping`: ![image](https://user-images.githubusercontent.com/50871412/122904371-db192700-d382-11eb-8917-1775db76db69.png) Without the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https://colab.research.google.com/drive/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing) It is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('ใƒฟ')` return 'ใ‚ณใƒˆ'. One workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`. I guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this. p.s. **I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https://github.com/huggingface/datasets/pull/2466)** `get_dataset `is just a simple wrapping for `load_dataset` and the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained("xlm-roberta-large")`
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926,314,656
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2,528
Logging cannot be set to NOTSET similar to transformers
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[ "Hi @joshzwiebel, thanks for reporting. We are going to align with `transformers`." ]
2021-06-21T15:04:54Z
2021-06-24T14:42:47Z
2021-06-24T14:42:47Z
NONE
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## Describe the bug In the transformers library you can set the verbosity level to logging.NOTSET to work around the usage of tqdm and IPywidgets, however in Datasets this is no longer possible. This is because transformers set the verbosity level of tqdm with [this](https://github.com/huggingface/transformers/blob/b53bc55ba9bb10d5ee279eab51a2f0acc5af2a6b/src/transformers/file_utils.py#L1449) `disable=bool(logging.get_verbosity() == logging.NOTSET)` and datasets accomplishes this like [so](https://github.com/huggingface/datasets/blob/83554e410e1ab8c6f705cfbb2df7953638ad3ac1/src/datasets/utils/file_utils.py#L493) `not_verbose = bool(logger.getEffectiveLevel() > WARNING)` ## Steps to reproduce the bug ```python import datasets import logging datasets.logging.get_verbosity = lambda : logging.NOTSET datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy") ``` ## Expected results The code should download and load the dataset as normal without displaying progress bars ## Actual results ```ImportError Traceback (most recent call last) <ipython-input-4-aec65c0509c6> in <module> ----> 1 datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy") ~/venv/lib/python3.7/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, script_version, use_auth_token, task, **config_kwargs) 713 dataset=True, 714 return_resolved_file_path=True, --> 715 use_auth_token=use_auth_token, 716 ) 717 # Set the base path for downloads as the parent of the script location ~/venv/lib/python3.7/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs) 350 file_path = hf_bucket_url(path, filename=name, dataset=False) 351 try: --> 352 local_path = cached_path(file_path, download_config=download_config) 353 except FileNotFoundError: 354 raise FileNotFoundError( ~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 289 use_etag=download_config.use_etag, 290 max_retries=download_config.max_retries, --> 291 use_auth_token=download_config.use_auth_token, 292 ) 293 elif os.path.exists(url_or_filename): ~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) 668 headers=headers, 669 cookies=cookies, --> 670 max_retries=max_retries, 671 ) 672 ~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries) 493 initial=resume_size, 494 desc="Downloading", --> 495 disable=not_verbose, 496 ) 497 for chunk in response.iter_content(chunk_size=1024): ~/venv/lib/python3.7/site-packages/tqdm/notebook.py in __init__(self, *args, **kwargs) 217 total = self.total * unit_scale if self.total else self.total 218 self.container = self.status_printer( --> 219 self.fp, total, self.desc, self.ncols) 220 self.sp = self.display 221 ~/venv/lib/python3.7/site-packages/tqdm/notebook.py in status_printer(_, total, desc, ncols) 95 if IProgress is None: # #187 #451 #558 #872 96 raise ImportError( ---> 97 "IProgress not found. Please update jupyter and ipywidgets." 98 " See https://ipywidgets.readthedocs.io/en/stable" 99 "/user_install.html") ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Linux-5.4.95-42.163.amzn2.x86_64-x86_64-with-debian-10.8 - Python version: 3.7.10 - PyArrow version: 3.0.0 I am running this code on Deepnote and which important to this issue **does not** support IPywidgets
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2021-06-19T15:56:32Z
2021-06-19T18:48:23Z
2021-06-19T18:48:23Z
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__Originally posted by @lewtun in https://github.com/huggingface/datasets/pull/2469__
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Documentation Mistakes in Dataset: emotion
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[ "Hi,\r\n\r\nthis issue has been already reported in the dataset repo (https://github.com/dair-ai/emotion_dataset/issues/2), so this is a bug on their side.", "The documentation has another bug in the dataset card [here](https://huggingface.co/datasets/emotion). \r\n\r\nIn the dataset summary **six** emotions are ...
2021-06-19T07:08:57Z
2023-01-02T12:04:58Z
2023-01-02T12:04:58Z
NONE
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As per documentation, Dataset: emotion Homepage: https://github.com/dair-ai/emotion_dataset Dataset: https://github.com/huggingface/datasets/blob/master/datasets/emotion/emotion.py Permalink: https://huggingface.co/datasets/viewer/?dataset=emotion Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper. But when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust.
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Datasets with tricky task templates
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[ "The `task_templates` API is deprecated in favor of the `train-eval-index` YAML field, so I'm closing this issue." ]
2021-06-18T15:33:57Z
2023-07-20T13:20:32Z
2023-07-20T13:20:32Z
MEMBER
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I'm collecting a list of datasets here that don't follow the "standard" taxonomy and require further investigation to implement task templates for. ## Text classification * [hatexplain](https://huggingface.co/datasets/hatexplain): ostensibly a form of text classification, but not in the standard `(text, target)` format and each sample appears to be tokenized. * [muchocine](https://huggingface.co/datasets/muchocine): contains two candidate text columns (long-form and summary) which in principle requires two `TextClassification` templates which is not currently supported
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Corelation should be Correlation
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[ "Hi @colbym-MM, thanks for reporting. We are fixing it." ]
2021-06-17T17:28:48Z
2021-06-18T08:43:55Z
2021-06-18T08:43:55Z
NONE
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https://github.com/huggingface/datasets/blob/0e87e1d053220e8ecddfa679bcd89a4c7bc5af62/metrics/matthews_correlation/matthews_correlation.py#L66
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seqeval metric does not work with a recent version of sklearn: classification_report() got an unexpected keyword argument 'output_dict'
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[ "Sorry, I was using an old version of sequeval" ]
2021-06-17T15:36:02Z
2021-06-17T15:46:07Z
2021-06-17T15:46:07Z
NONE
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric seqeval = load_metric("seqeval") seqeval.compute(predictions=[['A']], references=[['A']]) ``` ## Expected results The function computes a dict with metrics ## Actual results ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-39-69a57f5cf06f> in <module> 1 from datasets import load_dataset, load_metric 2 seqeval = load_metric("seqeval") ----> 3 seqeval.compute(predictions=[['A']], references=[['A']]) ~/p3/lib/python3.7/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/81eda1ff004361d4fa48754a446ec69bb7aa9cf4d14c7215f407d1475941c5ff/seqeval.py in _compute(self, predictions, references, suffix) 95 96 def _compute(self, predictions, references, suffix=False): ---> 97 report = classification_report(y_true=references, y_pred=predictions, suffix=suffix, output_dict=True) 98 report.pop("macro avg") 99 report.pop("weighted avg") TypeError: classification_report() got an unexpected keyword argument 'output_dict' ``` ## Environment info sklearn=0.24 datasets=1.1.3
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923,762,133
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Add C4
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[ "Update on this: I'm computing the checksums of the data files. It will be available soon", "Added in #2575 :)" ]
2021-06-17T10:31:04Z
2021-07-05T12:36:58Z
2021-07-05T12:36:57Z
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## Adding a Dataset - **Name:** *C4* - **Description:** *https://github.com/allenai/allennlp/discussions/5056* - **Paper:** *https://arxiv.org/abs/1910.10683* - **Data:** *https://huggingface.co/datasets/allenai/c4* - **Motivation:** *Used a lot for pretraining* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Should fix https://github.com/huggingface/datasets/issues/1710
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Load Image Classification Dataset from Local
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[ "Hi ! Is this folder structure a standard, a bit like imagenet ?\r\nIn this case maybe we can consider having a dataset loader for cifar-like, imagenet-like, squad-like, conll-like etc. datasets ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nmy_custom_cifar = load_dataset(\"cifar_like\", data_dir=\"path...
2021-06-15T22:43:33Z
2022-03-01T16:29:44Z
2022-03-01T16:29:44Z
NONE
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**Is your feature request related to a problem? Please describe.** Yes - we would like to load an image classification dataset with datasets without having to write a custom data loader. **Describe the solution you'd like** Given a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like "cifar10". **Describe alternatives you've considered** Implement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path) Write custom data loader logic **Additional context** We're training ViT on custom dataset
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Dataset fingerprint changes after moving the cache directory, which prevent cache reload when using `map`
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2021-06-14T09:20:26Z
2021-06-21T15:05:03Z
2021-06-21T15:05:03Z
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`Dataset.map` uses the dataset fingerprint (a hash) for caching. However the fingerprint seems to change when someone moves the cache directory of the dataset. This is because it uses the default fingerprint generation: 1. the dataset path is used to get the fingerprint 2. the modification times of the arrow file is also used to get the fingerprint To fix that we could set the fingerprint of the dataset to be a hash of (<dataset_name>, <config_name>, <version>, <script_hash>), i.e. a hash of the the cache path relative to the cache directory.
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2021-06-14T08:32:07Z
2021-06-21T16:15:49Z
2021-06-21T16:15:49Z
MEMBER
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We already support pytorch, tensorflow, numpy, pandas and arrow dataset formatting. Let's add jax as well
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2,489
Allow latest pyarrow version once segfault bug is fixed
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2021-06-12T14:09:52Z
2021-06-14T07:53:23Z
2021-06-14T07:53:23Z
MEMBER
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As pointed out by @symeneses (see https://github.com/huggingface/datasets/pull/2268#issuecomment-860048613), pyarrow has fixed the segfault bug present in version 4.0.0 (see https://issues.apache.org/jira/browse/ARROW-12568): - it was fixed on 3 May 2021 - version 4.0.1 was released on 19 May 2021 with the bug fix
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Implement loading a dataset builder
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2021-06-11T18:47:22Z
2021-07-05T10:45:57Z
2021-07-05T10:45:57Z
MEMBER
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As discussed with @stas00 and @lhoestq, this would allow things like: ```python from datasets import load_dataset_builder dataset_name = "openwebtext" builder = load_dataset_builder(dataset_name) print(builder.cache_dir) ```
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Delete extracted files to save disk space
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[ "My suggestion for this would be to have this enabled by default.\r\n\r\nPlus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:\r\n\r\n1. uncompress a handful of files via a generator enough to generate one arrow file...
2021-06-11T12:21:52Z
2021-07-19T09:08:18Z
2021-07-19T09:08:18Z
MEMBER
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As discussed with @stas00 and @lhoestq, allowing the deletion of extracted files would save a great amount of disk space to typical user.
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Issue in timit_asr database
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[ "This bug was fixed in #1995. Upgrading datasets to version 1.6 fixes the issue!", "Indeed was a fixed bug.\r\nWorks on version 1.8\r\nThanks " ]
2021-06-10T18:05:29Z
2021-06-13T08:13:50Z
2021-06-13T08:13:13Z
NONE
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## Describe the bug I am trying to load the timit_asr dataset however only the first record is shown (duplicated over all the rows). I am using the next code line dataset = load_dataset(โ€œtimit_asrโ€, split=โ€œtestโ€).shuffle().select(range(10)) The above code result with the same sentence duplicated ten times. It also happens when I use the dataset viewer at Streamlit . ## Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset(โ€œtimit_asrโ€, split=โ€œtestโ€).shuffle().select(range(10)) data = dataset.to_pandas() # Sample code to reproduce the bug ``` ## Expected results table with different row information ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.4.1 (also occur in the latest version) - Platform: Linux-4.15.0-143-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyTorch version (GPU?): 1.8.1+cu102 (False) - Tensorflow version (GPU?): 1.15.3 (False) - Using GPU in script?: No - Using distributed or parallel set-up in script?: No - `datasets` version: - Platform: - Python version: - PyArrow version:
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cache_dir parameter for load_from_disk ?
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[ "Hi ! `load_from_disk` doesn't move the data. If you specify a local path to your mounted drive, then the dataset is going to be loaded directly from the arrow file in this directory. The cache files that result from `map` operations are also stored in the same directory by default.\r\n\r\nHowever note than writing...
2021-06-10T17:39:36Z
2022-02-16T14:55:01Z
2022-02-16T14:55:00Z
NONE
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**Is your feature request related to a problem? Please describe.** When using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk: ` from datasets import load_from_disk myPreprocessedData = load_from_disk("/content/gdrive/MyDrive/ASR_data/myPreprocessedData") ` I know that chaching on google drive could slow down learning. But at least it would run. **Describe the solution you'd like** Add cache_Dir parameter to the load_from_disk function. **Describe alternatives you've considered** It looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?
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Fix automatic generation of Zenodo DOI
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[ "I have received a reply from Zenodo support:\r\n> We are currently investigating and fixing this issue related to GitHub releases. As soon as we have solved it we will reach back to you.", "Other repo maintainers had the same problem with Zenodo. \r\n\r\nThere is an open issue on their GitHub repo: zenodo/zenodo...
2021-06-10T15:15:46Z
2021-06-14T16:49:42Z
2021-06-14T16:49:42Z
MEMBER
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After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as "Received", instead of in green as "Published". I have contacted Zenodo support to fix this issue. TODO: - [x] Check with Zenodo to fix the issue - [x] Check BibTeX entry is right
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Fix PermissionError on Windows when using tqdm >=4.50.0
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2021-06-10T08:31:49Z
2021-06-11T15:11:50Z
2021-06-11T15:11:50Z
MEMBER
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See: https://app.circleci.com/pipelines/github/huggingface/datasets/235/workflows/cfb6a39f-68eb-4802-8b17-2cd5e8ea7369/jobs/1111 ``` PermissionError: [WinError 32] The process cannot access the file because it is being used by another process ```
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Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`.
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[ "Hi ! It looks like the issue comes from pyarrow. What version of pyarrow are you using ? How did you install it ?", "Thank you for the quick reply! I have `pyarrow==4.0.0`, and I am installing with `pip`. It's not one of my explicit dependencies, so I assume it came along with something else.", "Could you tryi...
2021-06-09T22:40:22Z
2021-07-01T09:34:54Z
2021-07-01T09:11:13Z
NONE
null
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## Describe the bug Crash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`. I believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose. ## Steps to reproduce the bug ```python # this function will be applied with map() def tokenize_function(examples): return tokenizer( examples["text"], padding=PaddingStrategy.DO_NOT_PAD, truncation=True, ) # data_files is a Dict[str, str] mapping name -> path datasets = load_dataset("text", data_files={...}) # this is where the error happens if num_proc = 16, # but is fine if num_proc = 1 tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=num_workers, ) ``` ## Expected results The `map()` function succeeds with `num_proc` > 1. ## Actual results ![image](https://user-images.githubusercontent.com/1170062/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png) ![image](https://user-images.githubusercontent.com/1170062/121404362-be0b3f80-c910-11eb-9117-658943029aef.png) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31 - Python version: 3.9.5 - PyTorch version (GPU?): 1.8.1+cu111 (True) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: Yes, but I think N/A for this issue - Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N/A for this issue
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`Proto_qa` hosting seems to be broken
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[ "@VictorSanh , I think @mariosasko is already working on it. " ]
2021-06-08T16:16:32Z
2021-06-10T08:31:09Z
2021-06-10T08:31:09Z
CONTRIBUTOR
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## Describe the bug The hosting (on Github) of the `proto_qa` dataset seems broken. I haven't investigated more yet, just flagging it for now. @zaidalyafeai if you want to dive into it, I think it's just a matter of changing the links in `proto_qa.py` ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("proto_qa") ``` ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset use_auth_token=use_auth_token, File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 630, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/hf/.cache/huggingface/modules/datasets_modules/datasets/proto_qa/445346efaad5c5f200ecda4aa7f0fb50ff1b55edde3003be424a2112c3e8102e/proto_qa.py", line 131, in _split_generators train_fpath = dl_manager.download(_URLs[self.config.name]["train"]) File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 199, in download num_proc=download_config.num_proc, File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 195, in map_nested return function(data_struct) File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 218, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 291, in cached_path use_auth_token=download_config.use_auth_token, File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/iesl/protoqa-data/master/data/train/protoqa_train.jsonl ```
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Revert default in-memory for small datasets
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[ "cc: @krandiash (pinged in reverted PR)." ]
2021-06-08T15:51:41Z
2021-06-08T18:57:11Z
2021-06-08T17:55:43Z
MEMBER
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Users are reporting issues and confusion about setting default in-memory to True for small datasets. We see 2 clear use cases of Datasets: - the "canonical" way, where you can work with very large datasets, as they are memory-mapped and cached (after every transformation) - some edge cases (speed benchmarks, interactive/exploratory analysis,...), where default in-memory can explicitly be enabled, and no caching will be done After discussing with @lhoestq we have agreed to: - revert this feature (implemented in #2182) - explain in the docs how to optimize speed/performance by setting default in-memory cc: @stas00 https://github.com/huggingface/datasets/pull/2409#issuecomment-856210552
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MRPC test set differences between torch and tensorflow datasets
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[ "Realized that `tensorflow_datasets` is not provided by Huggingface and should therefore raise the issue there." ]
2021-06-07T14:20:26Z
2021-06-07T14:34:32Z
2021-06-07T14:34:32Z
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## Describe the bug When using `load_dataset("glue", "mrpc")` to load the MRPC dataset, the test set includes the labels. When using `tensorflow_datasets.load('glue/{}'.format('mrpc'))` to load the dataset the test set does not contain the labels. There should be consistency between torch and tensorflow ways of importing the GLUE datasets. ## Steps to reproduce the bug Minimal working code ```python from datasets import load_dataset import tensorflow as tf import tensorflow_datasets # torch dataset = load_dataset("glue", "mrpc") # tf data = tensorflow_datasets.load('glue/{}'.format('mrpc')) data = list(data['test'].as_numpy_iterator()) for i in range(40,50): tf_sentence1 = data[i]['sentence1'].decode("utf-8") tf_sentence2 = data[i]['sentence2'].decode("utf-8") tf_label = data[i]['label'] index = data[i]['idx'] print('Index {}'.format(index)) torch_sentence1 = dataset['test']['sentence1'][index] torch_sentence2 = dataset['test']['sentence2'][index] torch_label = dataset['test']['label'][index] print('Tensorflow: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(tf_sentence1, tf_sentence2, tf_label)) print('Torch: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(torch_sentence1, torch_sentence2, torch_label)) ``` Sample output ``` Index 954 Tensorflow: Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws . Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws . Label -1 Torch: Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws . Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws . Label 1 Index 711 Tensorflow: Sentence1 Others keep records sealed for as little as five years or as much as 30 . Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years . Label -1 Torch: Sentence1 Others keep records sealed for as little as five years or as much as 30 . Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years . Label 0 ``` ## Expected results I would expect the datasets to be independent of whether I am working with torch or tensorflow. ## Actual results Test set labels are provided in the `datasets.load_datasets()` for MRPC. However MRPC is the only task where the test set labels are not -1. ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.10 - PyArrow version: 3.0.0
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BLUE file not found
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[ "Hi ! The `blue` metric doesn't exist, but the `bleu` metric does.\r\nYou can get the full list of metrics [here](https://github.com/huggingface/datasets/tree/master/metrics) or by running\r\n```python\r\nfrom datasets import list_metrics\r\n\r\nprint(list_metrics())\r\n```", "Ah, my mistake. Thanks for correctin...
2021-06-06T17:01:54Z
2021-06-07T10:46:15Z
2021-06-07T10:46:15Z
NONE
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Hi, I'm having the following issue when I try to load the `blue` metric. ```shell import datasets metric = datasets.load_metric('blue') Traceback (most recent call last): File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 320, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path use_auth_token=download_config.use_auth_token, File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 332, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path use_auth_token=download_config.use_auth_token, File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/metrics/blue/blue.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<input>", line 1, in <module> File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 605, in load_metric dataset=False, File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 343, in prepare_module combined_path, github_file_path FileNotFoundError: Couldn't find file locally at blue/blue.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py. The file is also not present on the master branch on github. ``` Here is dataset installed version info ```shell pip freeze | grep datasets datasets==1.7.0 ```
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dataset adversarial_qa has no answers in the "test" set
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[ "Hi ! I'm pretty sure that the answers are not made available for the test set on purpose because it is part of the DynaBench benchmark, for which you can submit your predictions on the website.\r\nIn any case we should mention this in the dataset card of this dataset.", "Makes sense, but not intuitive for someon...
2021-06-05T14:57:38Z
2021-06-07T11:13:07Z
2021-06-07T11:13:07Z
NONE
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## Describe the bug When loading the adversarial_qa dataset the 'test' portion has no answers. Only the 'train' and 'validation' portions do. This occurs with all four of the configs ('adversarialQA', 'dbidaf', 'dbert', 'droberta') ## Steps to reproduce the bug ``` from datasets import load_dataset examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['test'] print('Loaded {:,} examples'.format(len(examples))) has_answers = 0 for e in examples: if e['answers']['text']: has_answers += 1 print('{:,} have answers'.format(has_answers)) >>> Loaded 3,000 examples >>> 0 have answers examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['validation'] <...code above...> >>> Loaded 3,000 examples >>> 3,000 have answers ``` ## Expected results If 'test' is a valid dataset, it should have answers. Also note that all of the 'train' and 'validation' sets have answers, there are no "no answer" questions with this set (not sure if this is correct or not). ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.8.0-53-generic-x86_64-with-glibc2.29 - Python version: 3.8.5 - PyArrow version: 1.0.0
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`yelp_polarity` is broken
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[ "```\r\nFile \"/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/script_runner.py\", line 332, in _run_script\r\n exec(code, module.__dict__)\r\nFile \"/home/sasha/nlp-viewer/run.py\", line 233, in <module>\r\n configs = get_confs(option)\r\nFile \"/home/sasha/.local/shar...
2021-06-04T15:44:29Z
2021-06-04T18:56:47Z
2021-06-04T18:56:47Z
CONTRIBUTOR
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![image](https://user-images.githubusercontent.com/22514219/120828150-c4a35b00-c58e-11eb-8083-a537cee4dbb3.png)
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Sentence Boundaries missing in Dataset: xtreme / udpos
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[ "Hi,\r\n\r\nThis is a known issue. More info on this issue can be found in #2061. If you are looking for an open-source contribution, there are step-by-step instructions in the linked issue that you can follow to fix it.", "Closed by #2466." ]
2021-06-04T09:10:26Z
2021-06-18T11:53:43Z
2021-06-18T11:53:43Z
CONTRIBUTOR
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I was browsing through annotation guidelines, as suggested by the datasets introduction. The guidlines saids "There must be exactly one blank line after every sentence, including the last sentence in the file. Empty sentences are not allowed." in the [Sentence Boundaries and Comments section](https://universaldependencies.org/format.html#sentence-boundaries-and-comments) But the sentence boundaries seems not to be represented by huggingface datasets features well. I found out that multiple sentence are concatenated together as a 1D array, without any delimiter. PAN-x, which is another token classification subset from xtreme do represent the sentence boundary using a 2D array. You may compare in PAN-x.en and udpos.English in the explorer: https://huggingface.co/datasets/viewer/?dataset=xtreme
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Some tests hang on Windows
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[ "Hi ! That would be nice indeed to at least have a warning, since we don't handle the max path length limit.\r\nAlso if we could have an error instead of an infinite loop I'm sure windows users will appreciate that", "Unfortunately, I know this problem very well... ๐Ÿ˜… \r\n\r\nI remember having proposed to throw a...
2021-06-03T00:27:30Z
2021-06-28T08:47:39Z
2021-06-28T08:47:39Z
COLLABORATOR
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Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220. This can be very tricky to debug, so I think now is a good time to address these issues/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.
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2,441
DuplicatedKeysError on personal dataset
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[ "Hi ! In your dataset script you must be yielding examples like\r\n```python\r\nfor line in file:\r\n ...\r\n yield key, {...}\r\n```\r\n\r\nSince `datasets` 1.7.0 we enforce the keys to be unique.\r\nHowever it looks like your examples generator creates duplicate keys: at least two examples have key 0.\r\n\r...
2021-06-01T17:59:41Z
2021-06-04T23:50:03Z
2021-06-04T23:50:03Z
NONE
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## Describe the bug Ever since today, I have been getting a DuplicatedKeysError while trying to load my dataset from my own script. Error returned when running this line: `dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py')` Note that my script was working fine with earlier versions of the Datasets library. Cannot say with 100% certainty if I have been doing something wrong with my dataset script this whole time or if this is simply a bug with the new version of datasets. ## Steps to reproduce the bug I cannot provide code to reproduce the error as I am working with my own dataset. I can however provide my script if requested. ## Expected results For my data to be loaded. ## Actual results **DuplicatedKeysError** exception is raised ``` Downloading and preparing dataset good_reads_practice_dataset/main_domain (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/good_reads_practice_dataset/main_domain/1.1.0/64ff7c3fee2693afdddea75002eb6887d4fedc3d812ae3622128c8504ab21655... --------------------------------------------------------------------------- DuplicatedKeysError Traceback (most recent call last) <ipython-input-6-c342ea0dae9d> in <module>() ----> 1 dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py') 5 frames /usr/local/lib/python3.7/dist-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, script_version, use_auth_token, task, **config_kwargs) 749 try_from_hf_gcs=try_from_hf_gcs, 750 base_path=base_path, --> 751 use_auth_token=use_auth_token, 752 ) 753 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 573 if not downloaded_from_gcs: 574 self._download_and_prepare( --> 575 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 576 ) 577 # Sync info /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 650 try: 651 # Prepare split will record examples associated to the split --> 652 self._prepare_split(split_generator, **prepare_split_kwargs) 653 except OSError as e: 654 raise OSError( /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator) 990 writer.write(example, key) 991 finally: --> 992 num_examples, num_bytes = writer.finalize() 993 994 split_generator.split_info.num_examples = num_examples /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in finalize(self, close_stream) 407 # In case current_examples < writer_batch_size, but user uses finalize() 408 if self._check_duplicates: --> 409 self.check_duplicate_keys() 410 # Re-intializing to empty list for next batch 411 self.hkey_record = [] /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self) 347 for hash, key in self.hkey_record: 348 if hash in tmp_record: --> 349 raise DuplicatedKeysError(key) 350 else: 351 tmp_record.add(hash) DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 0 Keys should be unique and deterministic in nature ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.7.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.9 - PyArrow version: 3.0.0
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2,440
Remove `extended` field from dataset tagger
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[ "The tagger also doesn't insert the value for the `size_categories` field automatically, so this should be fixed too", "Thanks for reporting. Indeed the `extended` tag doesn't exist. Not sure why we had that in the tagger.\r\nThe repo of the tagger is here if someone wants to give this a try: https://github.com/h...
2021-06-01T17:18:42Z
2021-06-09T09:06:31Z
2021-06-09T09:06:30Z
MEMBER
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## Describe the bug While working on #2435 I used the [dataset tagger](https://huggingface.co/datasets/tagging/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included: ``` dataset_name = 'arcd' @pytest.mark.parametrize("dataset_name", get_changed_datasets(repo_path)) def test_changed_dataset_card(dataset_name): card_path = repo_path / "datasets" / dataset_name / "README.md" assert card_path.exists() error_messages = [] try: ReadMe.from_readme(card_path) except Exception as readme_error: error_messages.append(f"The following issues have been found in the dataset cards:\nREADME:\n{readme_error}") try: DatasetMetadata.from_readme(card_path) except Exception as metadata_error: error_messages.append( f"The following issues have been found in the dataset cards:\nYAML tags:\n{metadata_error}" ) if error_messages: > raise ValueError("\n".join(error_messages)) E ValueError: The following issues have been found in the dataset cards: E YAML tags: E __init__() got an unexpected keyword argument 'extended' tests/test_dataset_cards.py:70: ValueError ``` Consider either removing this tag from the tagger or including it as part of the validation step in the CI. cc @yjernite
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2,434
Extend QuestionAnsweringExtractive template to handle nested columns
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[ "this is also the case for the following datasets and configurations:\r\n\r\n* `mlqa` with config `mlqa-translate-train.ar`\r\n\r\n", "The current task API is somewhat deprecated (we plan to align it with `train eval index` at some point), so I think we can close this issue." ]
2021-05-31T14:06:51Z
2022-10-05T17:06:28Z
2022-10-05T17:06:28Z
MEMBER
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Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like: * `iapp_wiki_qa_squad` * `parsinlu_reading_comprehension` where the nested features differ with those from `squad` and trigger an `ArrowNotImplementedError`: ``` --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) <ipython-input-12-50e5b8f69c20> in <module> ----> 1 ds.prepare_for_task("question-answering-extractive")[0] ~/git/datasets/src/datasets/arrow_dataset.py in prepare_for_task(self, task) 1436 # We found a template so now flush `DatasetInfo` to skip the template update in `DatasetInfo.__post_init__` 1437 dataset.info.task_templates = None -> 1438 dataset = dataset.cast(features=template.features) 1439 return dataset 1440 ~/git/datasets/src/datasets/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc) 977 format = self.format 978 dataset = self.with_format("arrow") --> 979 dataset = dataset.map( 980 lambda t: t.cast(schema), 981 batched=True, ~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 1600 1601 if num_proc is None or num_proc == 1: -> 1602 return self._map_single( 1603 function=function, 1604 with_indices=with_indices, ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 176 } 177 # apply actual function --> 178 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 179 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 180 # re-apply format to the output ~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs) 395 # Call actual function 396 --> 397 out = func(self, *args, **kwargs) 398 399 # Update fingerprint of in-place transforms + update in-place history of transforms ~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc) 1940 ) # Something simpler? 1941 try: -> 1942 batch = apply_function_on_filtered_inputs( 1943 batch, 1944 indices, ~/git/datasets/src/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 1836 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset 1837 processed_inputs = ( -> 1838 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) 1839 ) 1840 if update_data is None: ~/git/datasets/src/datasets/arrow_dataset.py in <lambda>(t) 978 dataset = self.with_format("arrow") 979 dataset = dataset.map( --> 980 lambda t: t.cast(schema), 981 batched=True, 982 batch_size=batch_size, ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/compute.py in cast(arr, target_type, safe) 241 else: 242 options = CastOptions.unsafe(target_type) --> 243 return call_function("cast", [arr], options) 244 245 ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from struct<answer_end: list<item: int32>, answer_start: list<item: int32>, text: list<item: string>> to struct using function cast_struct ```
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DuplicatedKeysError when trying to load adversarial_qa
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[ "Thanks for reporting !\r\n#2433 fixed the issue, thanks @mariosasko :)\r\n\r\nWe'll do a patch release soon of the library.\r\nIn the meantime, you can use the fixed version of adversarial_qa by adding `script_version=\"master\"` in `load_dataset`" ]
2021-05-31T12:11:19Z
2021-06-01T08:54:03Z
2021-06-01T08:52:11Z
NONE
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python dataset = load_dataset('adversarial_qa', 'adversarialQA') ``` ## Expected results The dataset should be loaded into memory ## Actual results >DuplicatedKeysError: FAILURE TO GENERATE DATASET ! >Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4 >Keys should be unique and deterministic in nature > > >During handling of the above exception, another exception occurred: > >DuplicatedKeysError Traceback (most recent call last) > >/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self) > 347 for hash, key in self.hkey_record: > 348 if hash in tmp_record: >--> 349 raise DuplicatedKeysError(key) > 350 else: > 351 tmp_record.add(hash) > >DuplicatedKeysError: FAILURE TO GENERATE DATASET ! >Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4 >Keys should be unique and deterministic in nature ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.10 - PyArrow version: 3.0.0
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Saving Graph/Structured Data in Datasets
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[ "It should probably work out of the box to save structured data. If you want to show an example we can help you.", "An example of a toy dataset is like:\r\n```json\r\n[\r\n {\r\n \"name\": \"mike\",\r\n \"friends\": [\r\n \"tom\",\r\n \"lily\"\r\n ],\r\n \"arti...
2021-05-29T13:35:21Z
2021-06-02T01:21:03Z
2021-06-02T01:21:03Z
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Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''. Although I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. Thank you very much for your help.
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load_from_disk and save_to_disk are not compatible with each other
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[ "Hi,\r\n\r\n`load_dataset` returns an instance of `DatasetDict` if `split` is not specified, so instead of `Dataset.load_from_disk`, use `DatasetDict.load_from_disk` to load the dataset from disk.", "Thanks it worked!", "Though I see a stream of issues open by people lost between datasets and datasets dicts so ...
2021-05-28T23:07:10Z
2021-06-08T19:22:32Z
2021-06-08T19:22:32Z
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## Describe the bug load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("art") dataset.save_to_disk("mydir") d = Dataset.load_from_disk("mydir") ``` ## Expected results It is expected that these two functions be the reverse of each other without more manipulation ## Actual results FileNotFoundError: [Errno 2] No such file or directory: 'mydir/art/state.json' ## Environment info - `datasets` version: 1.6.2 - Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.10 - PyTorch version (GPU?): 1.8.1+cu102 (True) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in>
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[ "It actually seems to happen all the time in above configuration:\r\n* the function `filter_by_duration` correctly loads cached processed dataset\r\n* the function `prepare_dataset` is always reexecuted\r\n\r\nI end up solving the issue by saving to disk my dataset at the end but I'm still wondering if it's a bug o...
2021-05-27T15:40:06Z
2021-06-02T13:15:47Z
2021-06-02T13:15:47Z
CONTRIBUTOR
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## Describe the bug I have a large dataset (common_voice, english) where I use several map and filter functions. Sometimes my cached datasets after specific functions are not loaded. I always use the same arguments, same functions, no seedโ€ฆ ## Steps to reproduce the bug ```python def filter_by_duration(batch): return ( batch["duration"] <= 10 and batch["duration"] >= 1 and len(batch["target_text"]) > 5 ) def prepare_dataset(batch): batch["input_values"] = processor( batch["speech"], sampling_rate=batch["sampling_rate"][0] ).input_values with processor.as_target_processor(): batch["labels"] = processor(batch["target_text"]).input_ids return batch train_dataset = train_dataset.filter( filter_by_duration, remove_columns=["duration"], num_proc=data_args.preprocessing_num_workers, ) # PROBLEM HERE -> below function is reexecuted and cache is not loaded train_dataset = train_dataset.map( prepare_dataset, remove_columns=train_dataset.column_names, batch_size=training_args.per_device_train_batch_size, batched=True, num_proc=data_args.preprocessing_num_workers, ) # Later in script set_caching_enabled(False) # apply map on trained model to eval/test sets ``` ## Expected results The cached dataset should always be reloaded. ## Actual results The function is reexecuted. I have access to cached files `cache-xxxxx.arrow`. Is there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)? ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29 - Python version: 3.8.5 - PyTorch version (GPU?): 1.8.1+cu102 (True) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: Yes - Using distributed or parallel set-up in script?: No
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AttributeError: 'DatasetInfo' object has no attribute 'task_templates'
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[ "Hi ! Can you try using a more up-to-date version ? We added the task_templates in `datasets` 1.7.0.\r\n\r\nIdeally when you're working on new datasets, you should install and use the local version of your fork of `datasets`. Here I think you tried to run the 1.7.0 tests with the 1.6.2 code" ]
2021-05-27T13:44:28Z
2021-06-01T01:05:47Z
2021-06-01T01:05:47Z
CONTRIBUTOR
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## Describe the bug Hello, I'm trying to add dataset and contribute, but test keep fail with below cli. ` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>` ## Steps to reproduce the bug It seems like a bug when I see an error with the existing dataset, not the dataset I'm trying to add. ` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<any_dataset>` ## Expected results All test passed ## Actual results ``` # check that dataset is not empty self.parent.assertListEqual(sorted(dataset_builder.info.splits.keys()), sorted(dataset)) for split in dataset_builder.info.splits.keys(): # check that loaded datset is not empty self.parent.assertTrue(len(dataset[split]) > 0) # check that we can cast features for each task template > task_templates = dataset_builder.info.task_templates E AttributeError: 'DatasetInfo' object has no attribute 'task_templates' tests/test_dataset_common.py:175: AttributeError ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Darwin-20.4.0-x86_64-i386-64bit - Python version: 3.7.7 - PyTorch version (GPU?): 1.7.0 (False) - Tensorflow version (GPU?): 2.3.0 (False) - Using GPU in script?: No - Using distributed or parallel set-up in script?: No
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Docstring mistake: dataset vs. metric
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[ "> I can provide a PR l8er...\r\n\r\nSee #2425 " ]
2021-05-27T13:39:11Z
2021-06-01T08:18:04Z
2021-06-01T08:18:04Z
CONTRIBUTOR
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This: https://github.com/huggingface/datasets/blob/d95b95f8cf3cb0cff5f77a675139b584dcfcf719/src/datasets/load.py#L582 Should better be something like: `a metric identifier on HuggingFace AWS bucket (list all available metrics and ids with ``datasets.list_metrics()``)` I can provide a PR l8er...
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.map() function got an unexpected keyword argument 'cache_file_name'
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[ "Hi @cindyxinyiwang,\r\nDid you try adding `.arrow` after `cache_file_name` argument? Here I think they're expecting something like that only for a cache file:\r\nhttps://github.com/huggingface/datasets/blob/e08362256fb157c0b3038437fc0d7a0bbb50de5c/src/datasets/arrow_dataset.py#L1556-L1558", "Hi ! `cache_file_nam...
2021-05-27T01:54:26Z
2021-05-27T13:46:40Z
2021-05-27T13:46:40Z
NONE
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## Describe the bug I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that ".map() function got an unexpected keyword argument 'cache_file_name'". I believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function. Here is the code I use ## Steps to reproduce the bug ```datasets = load_from_disk(dataset_path=my_path) [...] def tokenize_function(examples): return tokenizer(examples[text_column_name]) logger.info("Mapping dataset to tokenized dataset.") tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=True, cache_file_name="my_tokenized_file" ) ``` ## Actual results tokenized_datasets = datasets.map( TypeError: map() got an unexpected keyword argument 'cache_file_name' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:1.6.2 - Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10 - Python version:3.8.5 - PyArrow version:3.0.0
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Add guide on using task templates to documentation
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2021-05-26T16:28:26Z
2022-10-05T17:07:00Z
2022-10-05T17:07:00Z
MEMBER
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Once we have a stable API on the text classification and question answering task templates, add a guide on how to use them in the documentation.
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PermissionError on Windows when using temp dir for caching
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2021-05-24T21:22:59Z
2021-05-26T16:39:29Z
2021-05-26T16:39:29Z
COLLABORATOR
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Currently, the following code raises a PermissionError on master if working on Windows: ```python # run as a script or call exit() in REPL to initiate the temp dir cleanup from datasets import * d = load_dataset("sst", split="train", keep_in_memory=False) set_caching_enabled(False) d.map(lambda ex: ex) ``` Error stack trace: ``` Traceback (most recent call last): File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 624, in _exitfunc f() File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 548, in __call__ return info.func(*info.args, **(info.kwargs or {})) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\tempfile.py", line 799, in _cleanup _shutil.rmtree(name) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 500, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 395, in _rmtree_unsafe onerror(os.unlink, fullname, sys.exc_info()) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 393, in _rmtree_unsafe os.unlink(fullname) PermissionError: [WinError 5] Access is denied: 'C:\\Users\\Mario\\AppData\\Local\\Temp\\tmp20epyhmq\\cache-87a87ffb5a956e68.arrow' ```
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load_dataset('natural_questions') fails with "ValueError: External features info don't match the dataset"
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[ "I faced the similar problem. Downgrading datasets to 1.5.0 fixed it.", "Thanks for reporting, I'm looking into it", "I just opened #2438 to fix this :)", "Hi ! This has been fixed in the 1.8.0 release of `datasets`" ]
2021-05-24T18:38:53Z
2021-06-09T09:07:25Z
2021-06-09T09:07:25Z
NONE
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## Describe the bug load_dataset('natural_questions') throws ValueError ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset('natural_questions', split='validation[:10]') ``` ## Expected results Call to load_dataset returns data. ## Actual results ``` Using custom data configuration default Reusing dataset natural_questions (/mnt/d/huggingface/datasets/natural_questions/default/0.0.2/19bc04755018a3ad02ee74f7045cde4ba9b4162cb64450a87030ab786b123b76) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-2-d55ab8a8cc1c> in <module> ----> 1 datasets = load_dataset('natural_questions', split='validation[:10]', cache_dir='/mnt/d/huggingface/datasets') ~/miniconda3/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, script_version, use_auth_token, **config_kwargs) 756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 757 ) --> 758 ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) 759 if save_infos: 760 builder_instance._save_infos() ~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in as_dataset(self, split, run_post_process, ignore_verifications, in_memory) 735 736 # Create a dataset for each of the given splits --> 737 datasets = utils.map_nested( 738 partial( 739 self._build_single_dataset, ~/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types) 193 # Singleton 194 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 195 return function(data_struct) 196 197 disable_tqdm = bool(logger.getEffectiveLevel() > INFO) ~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _build_single_dataset(self, split, run_post_process, ignore_verifications, in_memory) 762 763 # Build base dataset --> 764 ds = self._as_dataset( 765 split=split, 766 in_memory=in_memory, ~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _as_dataset(self, split, in_memory) 838 in_memory=in_memory, 839 ) --> 840 return Dataset(**dataset_kwargs) 841 842 def _post_process(self, dataset: Dataset, resources_paths: Dict[str, str]) -> Optional[Dataset]: ~/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint) 271 assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object" 272 if self.info.features.type != inferred_features.type: --> 273 raise ValueError( 274 "External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format( 275 self.info.features, self.info.features.type, inferred_features, inferred_features.type ValueError: External features info don't match the dataset: Got {'id': Value(dtype='string', id=None), 'document': {'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'html': Value(dtype='string', id=None), 'tokens': Sequence(feature={'token': Value(dtype='string', id=None), 'is_html': Value(dtype='bool', id=None)}, length=-1, id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': Sequence(feature={'id': Value(dtype='string', id=None), 'long_answer': {'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None)}, 'short_answers': Sequence(feature={'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}, length=-1, id=None), 'yes_no_answer': ClassLabel(num_classes=2, names=['NO', 'YES'], names_file=None, id=None)}, length=-1, id=None)} with type struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<start_token: int64, end_token: int64, start_byte: int64, end_byte: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<title: string, url: string, html: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>>, id: string, question: struct<text: string, tokens: list<item: string>>> but expected something like {'id': Value(dtype='string', id=None), 'document': {'html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'tokens': {'is_html': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None), 'token': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'url': Value(dtype='string', id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': {'id': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'long_answer': [{'end_byte': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'start_token': Value(dtype='int64', id=None)}], 'short_answers': [{'end_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'end_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}], 'yes_no_answer': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}} with type struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<end_byte: int64, end_token: int64, start_byte: int64, start_token: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<html: string, title: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>, url: string>, id: string, question: struct<text: string, tokens: list<item: string>>> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10 - Python version: 3.8.3 - PyTorch version (GPU?): 1.6.0 (False) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: No - Using distributed or parallel set-up in script?: No
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Concatenate several datasets with removed columns is not working.
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[ "Hi,\r\n\r\ndid you fill out the env info section manually or by copy-pasting the output of the `datasets-cli env` command?\r\n\r\nThis code should work without issues on 1.6.2 version (I'm working on master (1.6.2.dev0 version) and can't reproduce this error).", "@mariosasko you are right I was still on `1.5.0`....
2021-05-24T17:40:15Z
2021-05-25T05:52:01Z
2021-05-25T05:51:59Z
CONTRIBUTOR
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## Describe the bug You can't concatenate datasets when you removed columns before. ## Steps to reproduce the bug ```python from datasets import load_dataset, concatenate_datasets wikiann= load_dataset("wikiann","en") wikiann["train"] = wikiann["train"].remove_columns(["langs","spans"]) wikiann["test"] = wikiann["test"].remove_columns(["langs","spans"]) assert wikiann["train"].features.type == wikiann["test"].features.type concate = concatenate_datasets([wikiann["train"],wikiann["test"]]) ``` ## Expected results Merged dataset ## Actual results ```python ValueError: External features info don't match the dataset: Got {'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=ClassLabel(num_classes=7, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], names_file=None, id=None), length=-1, id=None), 'langs': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'spans': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} with type struct<langs: list<item: string>, ner_tags: list<item: int64>, spans: list<item: string>, tokens: list<item: string>> but expected something like {'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} with type struct<ner_tags: list<item: int64>, tokens: list<item: string>> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: ~1.6.2~ 1.5.0 - Platform: macos - Python version: 3.8.5 - PyArrow version: 3.0.0
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899,511,837
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News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
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[ "These ranges seem to be valid English. Closing." ]
2021-05-24T10:03:34Z
2022-10-05T17:13:49Z
2022-10-05T17:13:49Z
NONE
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I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi. ``` train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]') val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]') # filtering out examples that are not ar-en translations but ar-hi val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True) ``` * I'm fairly new to using datasets so I might be doing something wrong
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Missing original answers in kilt-TriviaQA
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[ "That could be useful indeed! Feel free to open a PR on the dataset card if you already have some code that runs, otherwise we'll take care of it soon :) ", "I can open a PR but there is 2 details to fix:\r\n- the name for the corresponding key (e.g. `original_answer`)\r\n- how to implement it: Iโ€™m not sure what ...
2021-05-21T14:57:07Z
2021-06-14T17:29:11Z
2021-06-14T17:29:11Z
CONTRIBUTOR
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I previously opened an issue at https://github.com/facebookresearch/KILT/issues/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets ## Describe the bug The `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative answer which are accepted for the question. However it'd be nice to know the original answer to the question (the only fields in `output` are `'answer', 'meta', 'provenance'`) ## How to fix It can be fixed by retrieving the original answer from the original TriviaQA (e.g. `trivia_qa['train'][0]['answer']['value']`), perhaps at the same place as here where one retrieves the questions https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md#loading-the-kilt-knowledge-source-and-task-data cc @yjernite who previously answered to an issue about KILT and TriviaQA :)
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Incorrect URLs for some datasets
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2021-05-21T07:22:35Z
2021-06-04T17:39:45Z
2021-06-04T17:39:45Z
MEMBER
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## Describe the bug It seems that the URLs for the following datasets are invalid: - [ ] `bn_hate_speech` has been renamed: https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset/commit/c67ecfc4184911e12814f6b36901f9828df8a63a - [ ] `covid_tweets_japanese` has been renamed: http://www.db.info.gifu-u.ac.jp/covid-19-twitter-dataset/ As a result we can no longer load these datasets using `load_dataset`. The simple fix is to rename the URL in the dataset script - will do this asap. ## Steps to reproduce the bug ```python from datasets import load_dataset # pick one of the datasets from the list above ds = load_dataset("bn_hate_speech") ``` ## Expected results Dataset loads without error. ## Actual results ``` Downloading: 3.36kB [00:00, 1.07MB/s] Downloading: 2.03kB [00:00, 678kB/s] Using custom data configuration default Downloading and preparing dataset bn_hate_speech/default (download: 951.48 KiB, generated: 949.84 KiB, post-processed: Unknown size, total: 1.86 MiB) to /Users/lewtun/.cache/huggingface/datasets/bn_hate_speech/default/0.0.0/a2dc726e511a2177523301bcad196af05d4d8a2cff30d2769ba8aacc1f5fdb5c... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/load.py", line 744, in load_dataset builder_instance.download_and_prepare( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/builder.py", line 574, in download_and_prepare self._download_and_prepare( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/builder.py", line 630, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/lewtun/.cache/huggingface/modules/datasets_modules/datasets/bn_hate_speech/a2dc726e511a2177523301bcad196af05d4d8a2cff30d2769ba8aacc1f5fdb5c/bn_hate_speech.py", line 76, in _split_generators train_path = dl_manager.download_and_extract(_URL) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 287, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 195, in download downloaded_path_or_paths = map_nested( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 195, in map_nested return function(data_struct) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 218, in _download return cached_path(url_or_filename, download_config=download_config) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 281, in cached_path output_path = get_from_cache( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/rezacsedu/Bengali-Hate-Speech-Dataset/main/Bengali_%20Hate_Speech_Dataset_Subset.csv ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2.dev0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.8 - PyArrow version: 3.0.0
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datasets 1.6 ignores cache
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[ "Looks like there are multiple issues regarding this (#2386, #2322) and it's a WIP #2329. Currently these datasets are being loaded in-memory which is causing this issue. Quoting @mariosasko here for a quick fix:\r\n\r\n> set `keep_in_memory` to `False` when loading a dataset (`sst = load_dataset(\"sst\", keep_in_m...
2021-05-21T00:12:58Z
2021-05-26T16:07:54Z
2021-05-26T16:07:54Z
CONTRIBUTOR
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Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612 Quoting @VictorSanh: > > I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335): > > > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-97cf4c813e6469c6.arrow'}]}` > > while the same command with the latest version of datasets (actually starting at `1.6.0`) gives: > > `{'train': [], 'validation': []}` > I also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used. to reproduce: ``` USE_TF=0 python examples/pytorch/language-modeling/run_clm.py \ --model_name_or_path gpt2 \ --dataset_name "stas/openwebtext-10k" \ --output_dir output_dir \ --overwrite_output_dir \ --do_train \ --do_eval \ --max_train_samples 1000 \ --max_eval_samples 200 \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size 4 \ --num_train_epochs 1 \ --warmup_steps 8 \ --block_size 64 \ --fp16 \ --report_to none ``` the first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run. @lhoestq
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Accessing Arrow dataset cache_files
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[ "Thanks @bhavitvyamalik for referencing the workaround. Setting `keep_in_memory=False` is working." ]
2021-05-20T23:57:43Z
2021-05-21T19:18:03Z
2021-05-21T19:18:03Z
NONE
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## Describe the bug In datasets 1.5.0 the following code snippet would have printed the cache_files: ``` train_data = load_dataset('conll2003', split='train', cache_dir='data') print(train_data.cache_files[0]['filename']) ``` However, in the newest release (1.6.1), it prints an empty list. I also tried loading the dataset with `keep_in_memory=True` argument but still `cache_files` is empty. Was wondering if this is a bug or I need to pass additional arguments so I can access the cache_files.
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DuplicatedKeysError: FAILURE TO GENERATE DATASET ! load_dataset('head_qa', 'en')
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2021-05-19T15:49:48Z
2021-05-30T13:26:16Z
2021-05-30T13:26:16Z
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Hello everyone, I try to use head_qa dataset in [https://huggingface.co/datasets/viewer/?dataset=head_qa&config=en](url) ``` !pip install datasets from datasets import load_dataset dataset = load_dataset( 'head_qa', 'en') ``` When I write above load_dataset(.), it throws the following: ``` DuplicatedKeysError Traceback (most recent call last) <ipython-input-6-ea87002d32f0> in <module>() 2 from datasets import load_dataset 3 dataset = load_dataset( ----> 4 'head_qa', 'en') 5 frames /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self) 347 for hash, key in self.hkey_record: 348 if hash in tmp_record: --> 349 raise DuplicatedKeysError(key) 350 else: 351 tmp_record.add(hash) DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 1 Keys should be unique and deterministic in nature ``` How can I fix the error? Thanks
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894,499,909
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Loading dataset from local path
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[ "Version below works, checked again in the docs, and data_files should be a path.\r\n```\r\nds = datasets.load_dataset('my_script.py', \r\n data_files='/data/dir/corpus.txt', \r\n cache_dir='.')\r\n```" ]
2021-05-18T15:20:50Z
2021-05-18T15:36:36Z
2021-05-18T15:36:35Z
NONE
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I'm trying to load a local dataset with the code below ``` ds = datasets.load_dataset('my_script.py', data_files='corpus.txt', data_dir='/data/dir', cache_dir='.') ``` But internally a BuilderConfig is created, which tries to use getmtime on the data_files string, without using data_dir. Is this a bug or am I not using the load_dataset correctly? https://github.com/huggingface/datasets/blob/bc61954083f74e6460688202e9f77dde2475319c/src/datasets/builder.py#L153
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Align question answering tasks with sub-domains
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[ "Closing this issue as the `task_templates` API has been deprecated." ]
2021-05-18T09:47:59Z
2023-07-25T16:52:05Z
2023-07-25T16:52:04Z
MEMBER
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As pointed out by @thomwolf in #2255 we should consider breaking with the pipeline taxonomy of `transformers` to account for the various types of question-answering domains: > `question-answering` exists in two forms: abstractive and extractive question answering. > > we can keep a generic `question-answering` but then it will probably mean diferrent schema of input/output for both (abstractive will have text for both while extractive can use spans indication as well as text). > > Or we can also propose to use `abstractive-question-answering` and `extractive-question-answering` for instance. > Maybe we could have `question-answering-abstractive` and `question-answering-extractive` if somehow we can use a for a completion or search in the future (detail). > Actually I see that people are more organizing in terms of general and sub-tasks, for instance on paperwithcode: https://paperswithcode.com/area/natural-language-processing and on nlpprogress: https://github.com/sebastianruder/NLP-progress/blob/master/english/question_answering.md#squad > > Probably the best is to align with one of these in terms of denomination, PaperWithCode is probably the most active and maintained and we work with them as well. > Maybe you want to check with a few QA datasets that this schema make sense. Typically NaturalQuestions, TriviaQA and can be good second datasets to compare to and be sure of the generality of the schema. > > A good recent list of QA datasets to compare the schemas among, is for instance in the UnitedQA paper: https://arxiv.org/abs/2101.00178 Investigate which grouping of QA is best suited for `datasets` and adapt / extend the QA task template accordingly.
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Json loader fails if user-specified features don't match the json data fields order
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2021-05-17T10:26:08Z
2021-06-16T10:47:49Z
2021-06-16T10:47:49Z
MEMBER
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If you do ```python dataset = load_dataset("json", data_files=data_files, features=features) ``` Then depending on the order of the features in the json data field it fails: ```python [...] ~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 94 if self.config.schema: 95 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT ---> 96 pa_table = pa_table.cast(self.config.schema) 97 yield i, pa_table [...] ValueError: Target schema's field names are not matching the table's field names: ['tokens', 'ner_tags'], ['ner_tags', 'tokens'] ``` This is because one must first re-order the columns of the table to match the `self.config.schema` before calling cast. One way to fix the `cast` would be to replace it with: ```python # reorder the arrays if necessary + cast to schema # we can't simply use .cast here because we may need to change the order of the columns pa_table = pa.Table.from_arrays([pa_table[name] for name in schema.names], schema=schema) ```
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Missing ClassLabel encoding in Json loader
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2021-05-17T10:19:10Z
2021-06-28T15:05:34Z
2021-06-28T15:05:34Z
MEMBER
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Currently if you want to load a json dataset this way ```python dataset = load_dataset("json", data_files=data_files, features=features) ``` Then if your features has ClassLabel types and if your json data needs class label encoding (i.e. if the labels in the json files are strings and not integers), then it would fail: ```python [...] ~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 94 if self.config.schema: 95 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT ---> 96 pa_table = pa_table.cast(self.config.schema) 97 yield i, pa_table [...] ArrowInvalid: Failed to parse string: 'O' as a scalar of type int64 ``` This is because it just tries to cast the string data to integers, without applying the mapping str->int first The current workaround is to do instead ```python dataset = load_dataset("json", data_files=data_files) dataset = dataset.map(features.encode_example, features=features) ```
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Allow model labels to be passed during task preparation
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[ "We now have the `align_labels_with_mapping` method in the API for this purpose." ]
2021-05-14T13:58:28Z
2022-10-05T17:37:22Z
2022-10-05T17:37:22Z
MEMBER
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Models have a config with label2id. And we have the same for datasets with the ClassLabel feature type. At one point either the model or the dataset must sync with the other. It would be great to do that on the dataset side. For example for sentiment classification on amazon reviews with you could have these labels: - "1 star", "2 stars", "3 stars", "4 stars", "5 stars" - "1", "2", "3", "4", "5" Some models may use the first set, while other models use the second set. Here in the `TextClassification` class, the user can only specify one set of labels, while many models could actually be compatible but have different sets of labels. Should we allow users to pass a list of compatible labels sets ? Then in terms of API, users could use `dataset.prepare_for_task("text-classification", labels=model.labels)` or something like that. The label set could also be the same but not in the same order. For NLI for example, some models use `["neutral", "entailment", "contradiction"]` and some others use `["neutral", "contradiction", "entailment"]`, so we should take care of updating the order of the labels in the dataset to match the labels order of the model. Let me know what you think ! This can be done in a future PR _Originally posted by @lhoestq in https://github.com/huggingface/datasets/pull/2255#discussion_r632412792_
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Document DatasetInfo attributes
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2021-05-12T20:01:29Z
2021-05-22T09:26:14Z
2021-05-22T09:26:14Z
MEMBER
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**Is your feature request related to a problem? Please describe.** As noted in PR #2255, the attributes of `DatasetInfo` are not documented in the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=datasetinfo#datasetinfo). It would be nice to do so :)
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`FaissIndex.save` throws error on GPU
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[ "Just in case, this is a workaround that I use in my code and it seems to do the job.\r\n\r\n```python\r\nif use_gpu_index:\r\n data[\"train\"]._indexes[\"text_emb\"].faiss_index = faiss.index_gpu_to_cpu(data[\"train\"]._indexes[\"text_emb\"].faiss_index)\r\n```" ]
2021-05-12T03:41:56Z
2021-05-17T13:41:41Z
2021-05-17T13:41:41Z
CONTRIBUTOR
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## Describe the bug After training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error. ``` File "index_wikipedia.py", line 119, in <module> data["train"].save_faiss_index("text_emb", index_save_path) File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 470, in save_faiss_index index.save(file) File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 334, in save faiss.write_index(index, str(file)) File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/faiss/swigfaiss_avx2.py", line 5654, in write_index return _swigfaiss.write_index(*args) RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /root/miniconda3/conda-bld/faiss-pkg_1613235005464/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index ``` ## Steps to reproduce the bug Any dataset will do, I just selected a familiar one. ```python import numpy as np import datasets INDEX_STR = "OPQ16_128,IVF512,PQ32" INDEX_SAVE_PATH = "will_not_save.faiss" data = datasets.load_dataset("Fraser/news-category-dataset", split=f"train[:10000]") def encode(item): return {"text_emb": np.random.randn(768).astype(np.float32)} data = data.map(encode) data.add_faiss_index(column="text_emb", string_factory=INDEX_STR, train_size=10_000, device=0) data.save_faiss_index("text_emb", INDEX_SAVE_PATH) ``` ## Expected results Saving the index ## Actual results Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) ... don't know how to serialize this type of index ## Environment info - `datasets` version: 1.6.2 - Platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyTorch version (GPU?): 1.8.1+cu111 (True) - Tensorflow version (GPU?): 2.2.0 (False) - Using GPU in script?: Yes - Using distributed or parallel set-up in script?: No I will be proposing a fix in a couple of minutes
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Add an API to access the language and pretty name of a dataset
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[ "Hi ! With @bhavitvyamalik we discussed about having something like\r\n```python\r\nfrom datasets import load_dataset_card\r\n\r\ndataset_card = load_dataset_card(\"squad\")\r\nprint(dataset_card.metadata.pretty_name)\r\n# Stanford Question Answering Dataset (SQuAD)\r\nprint(dataset_card.metadata.languages)\r\n# [\...
2021-05-11T14:10:08Z
2022-10-05T17:16:54Z
2022-10-05T17:16:53Z
CONTRIBUTOR
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It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts.
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[Question] How to move and reuse preprocessed dataset?
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[ "@lhoestq @LysandreJik", "<s>Hi :) Can you share with us the code you used ?</s>\r\n\r\nEDIT: from https://github.com/huggingface/transformers/issues/11665#issuecomment-838348291 I understand you're using the run_clm.py script. Can you share your logs ?\r\n", "Also note that for the caching to work, you must re...
2021-05-11T09:09:17Z
2021-06-11T04:39:11Z
2021-06-11T04:39:11Z
NONE
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Hi, I am training a gpt-2 from scratch using run_clm.py. I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess), I tried to : copy path_to_cache_dir/datasets to new_cache_dir/datasets set export HF_DATASETS_CACHE="new_cache_dir/" but the program still re-preprocess the whole dataset without loading cache. I also tried to torch.save(lm_datasets, fw), but the saved file is only 14M. What is the proper way to do this?
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NonMatchingChecksumError for web_of_science dataset
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[ "I've raised a PR for this. Should work with `dataset = load_dataset(\"web_of_science\", \"WOS11967\", ignore_verifications=True)`once it gets merged into the main branch. Thanks for reporting this! " ]
2021-05-09T02:02:02Z
2021-05-10T13:35:53Z
2021-05-10T13:35:53Z
NONE
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NonMatchingChecksumError when trying to download the web_of_science dataset. >NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1'] Setting `ignore_verfications=True` results in OSError. >OSError: Cannot find data file. Original error: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/37ab2c42f50d553c1d0ea432baca3e9e11fedea4aeec63a81e6b7e25dd10d4e7/WOS5736/X.txt' ```python dataset = load_dataset('web_of_science', 'WOS5736') ``` There are 3 data instances and they all don't work. 'WOS5736', 'WOS11967', 'WOS46985' datasets 1.6.2 python 3.7.10 Ubuntu 18.04.5 LTS
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Index error in Dataset.map
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2021-05-08T20:44:57Z
2021-05-10T13:26:12Z
2021-05-10T13:26:12Z
COLLABORATOR
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The following code, if executed on master, raises an IndexError (due to overflow): ```python >>> from datasets import * >>> d = load_dataset("bookcorpus", split="train") Reusing dataset bookcorpus (C:\Users\Mario\.cache\huggingface\datasets\bookcorpus\plain_text\1.0.0\44662c4a114441c35200992bea923b170e6f13f2f0beb7c14e43759cec498700) 2021-05-08 21:23:46.859818: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll >>> d.map(lambda ex: ex) 0%|โ–Ž | 289430/74004228 [00:13<58:41, 20935.33ex/s]c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py:84: RuntimeWarning: overflow encountered in int_scalars k = i + ((j - i) * (x - arr[i]) // (arr[j] - arr[i])) 0%|โ–Ž | 290162/74004228 [00:13<59:11, 20757.23ex/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1498, in map new_fingerprint=new_fingerprint, File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 174, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\fingerprint.py", line 340, in wrapper out = func(self, *args, **kwargs) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1799, in _map_single for i, example in enumerate(pbar): File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\site-packages\tqdm\std.py", line 1133, in __iter__ for obj in iterable: File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1145, in __iter__ format_kwargs=format_kwargs, File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1337, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 368, in query_table pa_subtable = _query_table(table, key) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 79, in _query_table return table.fast_slice(key % table.num_rows, 1) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 128, in fast_slice i = _interpolation_search(self._offsets, offset) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 91, in _interpolation_search raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.") IndexError: Invalid query '290162' for size 74004228. ``` Tested on Windows, can run on Linux if needed. EDIT: It seems like for this to happen, the default NumPy dtype has to be np.int32.
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Allow passing `desc` to `tqdm` in `Dataset.map()`
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[ "Hi @lhoestq,\r\nShould we change `desc` in [pbar](https://github.com/huggingface/datasets/blob/81fcf88172ed5e3026ef68aed4c0ec6980372333/src/datasets/arrow_dataset.py#L1860) to something meaningful?", "I think the user could pass the `desc` parameter to `map` so that it can be displayed in the tqdm progress bar, ...
2021-05-07T05:52:54Z
2021-05-26T14:59:21Z
2021-05-26T14:59:21Z
CONTRIBUTOR
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It's normal to have many `map()` calls, and some of them can take a few minutes, it would be nice to have a description on the progress bar. Alternative solution: Print the description before/after the `map()` call.
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A syntax error in example
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[ "cc @beurkinger but I think this has been fixed internally and will soon be updated right ?", "This issue has been fixed." ]
2021-05-06T14:34:44Z
2021-05-20T03:04:19Z
2021-05-20T03:04:19Z
NONE
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![image](https://user-images.githubusercontent.com/6883957/117315905-b47a5c00-aeba-11eb-91eb-b2a4a0212a56.png) Sorry to report with an image, I can't find the template source code of this snippet.
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load_dataset("timit_asr") gives back duplicates of just one sample text
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[ "Upgrading datasets to version 1.6 fixes the issue", "This bug was fixed in #1995. Upgrading the `datasets` should work! ", "Thanks @ekeleshian for having reported.\r\n\r\nI am closing this issue once that you updated `datasets`. Feel free to reopen it if the problem persists." ]
2021-05-05T13:14:48Z
2021-05-07T10:32:30Z
2021-05-07T10:32:30Z
NONE
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## Describe the bug When you look up on key ["train"] and then ['text'], you get back a list with just one sentence duplicated 4620 times. Namely, the sentence "Would such an act of refusal be useful?". Similarly when you look up ['test'] and then ['text'], the list is one sentence repeated "The bungalow was pleasantly situated near the shore." 1680 times. I tried to work around the issue by downgrading to datasets version 1.3.0, inspired by [this post](https://www.gitmemory.com/issue/huggingface/datasets/2052/798904836) and removing the entire huggingface directory from ~/.cache, but I still get the same issue. ## Steps to reproduce the bug ```python from datasets import load_dataset timit = load_dataset("timit_asr") print(timit['train']['text']) print(timit['test']['text']) ``` ## Expected Result Rows of diverse text, like how it is shown in the [wav2vec2.0 tutorial](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) <img width="485" alt="Screen Shot 2021-05-05 at 9 09 57 AM" src="https://user-images.githubusercontent.com/33647474/117146094-d9b77f00-ad81-11eb-8306-f281850c127a.png"> ## Actual results Rows of repeated text. <img width="319" alt="Screen Shot 2021-05-05 at 9 11 53 AM" src="https://user-images.githubusercontent.com/33647474/117146231-f8b61100-ad81-11eb-834a-fc10410b0c9c.png"> ## Versions - Datasets: 1.3.0 - Python: 3.9.1 - Platform: macOS-11.2.1-x86_64-i386-64bit}
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Calls to map are not cached.
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[ "I tried upgrading to `datasets==1.6.2` and downgrading to `1.6.0`. Both versions produce the same output.\r\n\r\nDowngrading to `1.5.0` works and produces the following output for me:\r\n\r\n```bash\r\nDownloading: 9.20kB [00:00, 3.94MB/s] \r\nDownloading: 5.99kB [00:00, 3.29MB/s] ...
2021-05-05T12:11:27Z
2021-06-08T19:10:02Z
2021-06-08T19:08:21Z
NONE
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## Describe the bug Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed? ## Steps to reproduce the bug ```python import datasets datasets.set_caching_enabled(True) sst = datasets.load_dataset("sst") def foo(samples, i): print("executed", i[:10]) return samples # first call x = sst.map(foo, batched=True, with_indices=True, num_proc=2) print('\n'*3, "#" * 30, '\n'*3) # second call y = sst.map(foo, batched=True, with_indices=True, num_proc=2) # print version import sys import platform print(f""" - Datasets: {datasets.__version__} - Python: {sys.version} - Platform: {platform.platform()} """) ``` ## Actual results This code prints the following output for me: ```bash No config specified, defaulting to: sst/default Reusing dataset sst (/home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff) #0: 0%| | 0/5 [00:00<?, ?ba/s] #1: 0%| | 0/5 [00:00<?, ?ba/s] executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] executed [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281] executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009] executed [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281] executed [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009] executed [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281] executed [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009] executed [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281] executed [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009] #0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 5/5 [00:00<00:00, 59.85ba/s] executed [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281] #1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 5/5 [00:00<00:00, 60.85ba/s] #0: 0%| | 0/1 [00:00<?, ?ba/s] #1: 0%| | 0/1 [00:00<?, ?ba/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] #0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1/1 [00:00<00:00, 69.32ba/s] executed [551, 552, 553, 554, 555, 556, 557, 558, 559, 560] #1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1/1 [00:00<00:00, 70.93ba/s] #0: 0%| | 0/2 [00:00<?, ?ba/s] #1: 0%| | 0/2 [00:00<?, ?ba/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009] #0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2/2 [00:00<00:00, 63.25ba/s] executed [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114] executed [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114] #1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2/2 [00:00<00:00, 57.69ba/s] ############################## #0: 0%| | 0/5 [00:00<?, ?ba/s] #1: 0%| | 0/5 [00:00<?, ?ba/s] executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] executed [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281] executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009] executed [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281] executed [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009] executed [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281] executed [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009] executed [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009] #0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 5/5 [00:00<00:00, 58.10ba/s] executed [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281] executed [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281] #1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 5/5 [00:00<00:00, 57.19ba/s] #0: 0%| | 0/1 [00:00<?, ?ba/s] #1: 0%| | 0/1 [00:00<?, ?ba/s] executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] #0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1/1 [00:00<00:00, 60.10ba/s] executed [551, 552, 553, 554, 555, 556, 557, 558, 559, 560] #1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1/1 [00:00<00:00, 53.82ba/s] #0: 0%| | 0/2 [00:00<?, ?ba/s] #1: 0%| | 0/2 [00:00<?, ?ba/s] executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009] executed [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114] #0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2/2 [00:00<00:00, 72.76ba/s] executed [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114] #1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2/2 [00:00<00:00, 71.55ba/s] - Datasets: 1.6.1 - Python: 3.8.3 (default, May 19 2020, 18:47:26) [GCC 7.3.0] - Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10 ``` ## Expected results Caching should work.
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UnicodeDecodeError for OSCAR (Afrikaans)
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[ "Thanks for reporting, @sgraaf.\r\n\r\nI am going to have a look at it. \r\n\r\nI guess the expected codec is \"UTF-8\". Normally, when no explicitly codec is passed, Python uses one which is platform-dependent. For Linux machines, the default codec is `utf_8`, which is OK. However for Windows machine, the default ...
2021-05-05T09:22:52Z
2021-05-05T10:57:31Z
2021-05-05T10:50:55Z
NONE
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## Describe the bug When loading the [OSCAR dataset](https://huggingface.co/datasets/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("oscar", "unshuffled_deduplicated_af") ``` ## Expected results Anything but an error, really. ## Actual results ```python >>> from datasets import load_dataset >>> dataset = load_dataset("oscar", "unshuffled_deduplicated_af") Downloading: 14.7kB [00:00, 4.91MB/s] Downloading: 3.07MB [00:00, 32.6MB/s] Downloading and preparing dataset oscar/unshuffled_deduplicated_af (download: 62.93 MiB, generated: 163.38 MiB, post-processed: Unknown size, total: 226.32 MiB) to C:\Users\sgraaf\.cache\huggingface\datasets\oscar\unshuffled_deduplicated_af\1.0.0\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464... Downloading: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 81.0/81.0 [00:00<00:00, 40.5kB/s] Downloading: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 66.0M/66.0M [00:18<00:00, 3.50MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\load.py", line 745, in load_dataset builder_instance.download_and_prepare( File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\builder.py", line 574, in download_and_prepare self._download_and_prepare( File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\builder.py", line 979, in _prepare_split for key, record in utils.tqdm( File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\tqdm\std.py", line 1133, in __iter__ for obj in iterable: File "C:\Users\sgraaf\.cache\huggingface\modules\datasets_modules\datasets\oscar\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464\oscar.py", line 359, in _generate_examples for line in f: File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\encodings\cp1252.py", line 23, in decode return codecs.charmap_decode(input,self.errors,decoding_table)[0] UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 7454: character maps to <undefined> ``` ## Versions Paste the output of the following code: ```python import datasets import sys import platform print(f""" - Datasets: {datasets.__version__} - Python: {sys.version} - Platform: {platform.platform()} """) ``` - Datasets: 1.6.2 - Python: 3.9.4 (tags/v3.9.4:1f2e308, Apr 6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)] - Platform: Windows-10-10.0.19041-SP0
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[api request] API to obtain "dataset_module" dynamic path?
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[ "Hi @richardliaw, \r\n\r\nFirst, thanks for the compliments.\r\n\r\nIn relation with your request, currently, the dynamic modules path is obtained this way:\r\n```python\r\nfrom datasets.load import init_dynamic_modules, MODULE_NAME_FOR_DYNAMIC_MODULES\r\n\r\ndynamic_modules_path = init_dynamic_modules(MODULE_NAME_...
2021-05-05T08:40:48Z
2021-05-06T08:45:45Z
2021-05-06T07:57:54Z
NONE
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**Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. This is an awesome library. It seems like the dynamic module path in this library has broken some of hyperparameter tuning functionality: https://discuss.huggingface.co/t/using-hyperparameter-search-in-trainer/785/34 This is because Ray will spawn new processes, and each process will load modules by path. However, we need to explicitly inform Ray to load the right modules, or else it will error upon import. I'd like an API to obtain the dynamic paths. This will allow us to support this functionality in this awesome library while being future proof. **Describe the solution you'd like** A clear and concise description of what you want to happen. `datasets.get_dynamic_paths -> List[str]` will be sufficient for my use case. By offering this API, we will be able to address the following issues (by patching the ray integration sufficiently): https://github.com/huggingface/blog/issues/106 https://github.com/huggingface/transformers/issues/11565 https://discuss.huggingface.co/t/using-hyperparameter-search-in-trainer/785/34 https://discuss.huggingface.co/t/using-hyperparameter-search-in-trainer/785/35
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Incorrect version specification for pyarrow
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[ "Fixed by #2317." ]
2021-05-04T19:15:11Z
2021-05-05T10:10:03Z
2021-05-05T10:10:03Z
CONTRIBUTOR
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## Describe the bug The pyarrow dependency is incorrectly specified in setup.py file, in [this line](https://github.com/huggingface/datasets/blob/3a3e5a4da20bfcd75f8b6a6869b240af8feccc12/setup.py#L77). Also as a snippet: ```python "pyarrow>=1.0.0<4.0.0", ``` ## Steps to reproduce the bug ```bash pip install "pyarrow>=1.0.0<4.0.0" ``` ## Expected results It is expected to get a pyarrow version between 1.0.0 (inclusive) and 4.0.0 (exclusive). ## Actual results pip ignores the specified versions since there is a missing comma between the lower and upper limits. Therefore, pip installs the latest pyarrow version from PYPI, which is 4.0.0. This is especially problematic since "conda env export" fails due to incorrect version specification. Here is the conda error as well: ```bash conda env export InvalidVersionSpec: Invalid version '1.0.0<4.0.0': invalid character(s) ``` ## Fix suggestion Put a comma between the version limits which means replacing the line in setup.py file with the following: ```python "pyarrow>=1.0.0,<4.0.0", ``` ## Versions Paste the output of the following code: ```python - Datasets: 1.6.2 - Python: 3.7.10 (default, Feb 26 2021, 18:47:35) [GCC 7.3.0] - Platform: Linux-5.4.0-42-generic-x86_64-with-debian-buster-sid ```
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Unable to setup dev env on Windows
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[ "Hi @gchhablani, \r\n\r\nThere are some 3rd-party dependencies that require to build code in C. In this case, it is the library `python-Levenshtein`.\r\n\r\nOn Windows, in order to be able to build C code, you need to install at least `Microsoft C++ Build Tools` version 14. You can find more info here: https://visu...
2021-05-02T13:20:42Z
2021-05-03T15:18:01Z
2021-05-03T15:17:34Z
CONTRIBUTOR
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Hi I tried installing the `".[dev]"` version on Windows 10 after cloning. Here is the error I'm facing: ```bat (env) C:\testing\datasets>pip install -e ".[dev]" Obtaining file:///C:/testing/datasets Requirement already satisfied: numpy>=1.17 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.19.5) Collecting pyarrow>=0.17.1 Using cached pyarrow-4.0.0-cp37-cp37m-win_amd64.whl (13.3 MB) Requirement already satisfied: dill in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.3.1.1) Collecting pandas Using cached pandas-1.2.4-cp37-cp37m-win_amd64.whl (9.1 MB) Requirement already satisfied: requests>=2.19.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.25.1) Requirement already satisfied: tqdm<4.50.0,>=4.27 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.49.0) Requirement already satisfied: xxhash in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.0.2) Collecting multiprocess Using cached multiprocess-0.70.11.1-py37-none-any.whl (108 kB) Requirement already satisfied: fsspec in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2021.4.0) Collecting huggingface_hub<0.1.0 Using cached huggingface_hub-0.0.8-py3-none-any.whl (34 kB) Requirement already satisfied: importlib_metadata in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.0.1) Requirement already satisfied: absl-py in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.12.0) Requirement already satisfied: pytest in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (6.2.3) Collecting pytest-xdist Using cached pytest_xdist-2.2.1-py3-none-any.whl (37 kB) Collecting apache-beam>=2.24.0 Using cached apache_beam-2.29.0-cp37-cp37m-win_amd64.whl (3.7 MB) Collecting elasticsearch Using cached elasticsearch-7.12.1-py2.py3-none-any.whl (339 kB) Requirement already satisfied: boto3==1.16.43 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.16.43) Requirement already satisfied: botocore==1.19.43 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.19.43) Collecting moto[s3]==1.3.16 Using cached moto-1.3.16-py2.py3-none-any.whl (879 kB) Collecting rarfile>=4.0 Using cached rarfile-4.0-py3-none-any.whl (28 kB) Collecting tensorflow>=2.3 Using cached tensorflow-2.4.1-cp37-cp37m-win_amd64.whl (370.7 MB) Requirement already satisfied: torch in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.8.1) Requirement already satisfied: transformers in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.5.1) Collecting bs4 Using cached bs4-0.0.1-py3-none-any.whl Collecting conllu Using cached conllu-4.4-py2.py3-none-any.whl (15 kB) Collecting langdetect Using cached langdetect-1.0.8-py3-none-any.whl Collecting lxml Using cached lxml-4.6.3-cp37-cp37m-win_amd64.whl (3.5 MB) Collecting mwparserfromhell Using cached mwparserfromhell-0.6-cp37-cp37m-win_amd64.whl (101 kB) Collecting nltk Using cached nltk-3.6.2-py3-none-any.whl (1.5 MB) Collecting openpyxl Using cached openpyxl-3.0.7-py2.py3-none-any.whl (243 kB) Collecting py7zr Using cached py7zr-0.15.2-py3-none-any.whl (66 kB) Collecting tldextract Using cached tldextract-3.1.0-py2.py3-none-any.whl (87 kB) Collecting zstandard Using cached zstandard-0.15.2-cp37-cp37m-win_amd64.whl (582 kB) Collecting bert_score>=0.3.6 Using cached bert_score-0.3.9-py3-none-any.whl (59 kB) Collecting rouge_score Using cached rouge_score-0.0.4-py2.py3-none-any.whl (22 kB) Collecting sacrebleu Using cached sacrebleu-1.5.1-py3-none-any.whl (54 kB) Requirement already satisfied: scipy in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.6.3) Collecting seqeval Using cached seqeval-1.2.2-py3-none-any.whl Collecting sklearn Using cached sklearn-0.0-py2.py3-none-any.whl Collecting jiwer Using cached jiwer-2.2.0-py3-none-any.whl (13 kB) Requirement already satisfied: toml>=0.10.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.10.2) Requirement already satisfied: requests_file>=1.5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.5.1) Requirement already satisfied: texttable>=1.6.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.6.3) Requirement already satisfied: s3fs>=0.4.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.4.2) Requirement already satisfied: Werkzeug>=1.0.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.0.1) Collecting black Using cached black-21.4b2-py3-none-any.whl (130 kB) Collecting isort Using cached isort-5.8.0-py3-none-any.whl (103 kB) Collecting flake8==3.7.9 Using cached flake8-3.7.9-py2.py3-none-any.whl (69 kB) Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from boto3==1.16.43->datasets==1.5.0.dev0) (0.10.0) Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from boto3==1.16.43->datasets==1.5.0.dev0) (0.3.7) Requirement already satisfied: urllib3<1.27,>=1.25.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from botocore==1.19.43->datasets==1.5.0.dev0) (1.26.4) Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from botocore==1.19.43->datasets==1.5.0.dev0) (2.8.1) Collecting entrypoints<0.4.0,>=0.3.0 Using cached entrypoints-0.3-py2.py3-none-any.whl (11 kB) Collecting pyflakes<2.2.0,>=2.1.0 Using cached pyflakes-2.1.1-py2.py3-none-any.whl (59 kB) Collecting pycodestyle<2.6.0,>=2.5.0 Using cached pycodestyle-2.5.0-py2.py3-none-any.whl (51 kB) Collecting mccabe<0.7.0,>=0.6.0 Using cached mccabe-0.6.1-py2.py3-none-any.whl (8.6 kB) Requirement already satisfied: jsondiff>=1.1.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.3.0) Requirement already satisfied: pytz in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2021.1) Requirement already satisfied: mock in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (4.0.3) Requirement already satisfied: MarkupSafe<2.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.1.1) Requirement already satisfied: python-jose[cryptography]<4.0.0,>=3.1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.2.0) Requirement already satisfied: aws-xray-sdk!=0.96,>=0.93 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.8.0) Requirement already satisfied: cryptography>=2.3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.4.7) Requirement already satisfied: more-itertools in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (8.7.0) Requirement already satisfied: PyYAML>=5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (5.4.1) Requirement already satisfied: boto>=2.36.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.49.0) Requirement already satisfied: idna<3,>=2.5 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.10) Requirement already satisfied: sshpubkeys>=3.1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.3.1) Requirement already satisfied: responses>=0.9.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.13.3) Requirement already satisfied: xmltodict in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.12.0) Requirement already satisfied: setuptools in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (52.0.0.post20210125) Requirement already satisfied: Jinja2>=2.10.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.11.3) Requirement already satisfied: zipp in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.4.1) Requirement already satisfied: six>1.9 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.15.0) Requirement already satisfied: ecdsa<0.15 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.14.1) Requirement already satisfied: docker>=2.5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (5.0.0) Requirement already satisfied: cfn-lint>=0.4.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.49.0) Requirement already satisfied: grpcio<2,>=1.29.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (1.32.0) Collecting hdfs<3.0.0,>=2.1.0 Using cached hdfs-2.6.0-py3-none-any.whl (33 kB) Collecting pyarrow>=0.17.1 Using cached pyarrow-3.0.0-cp37-cp37m-win_amd64.whl (12.6 MB) Collecting fastavro<2,>=0.21.4 Using cached fastavro-1.4.0-cp37-cp37m-win_amd64.whl (394 kB) Requirement already satisfied: httplib2<0.18.0,>=0.8 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (0.17.4) Collecting pymongo<4.0.0,>=3.8.0 Using cached pymongo-3.11.3-cp37-cp37m-win_amd64.whl (382 kB) Collecting crcmod<2.0,>=1.7 Using cached crcmod-1.7-py3-none-any.whl Collecting avro-python3!=1.9.2,<1.10.0,>=1.8.1 Using cached avro_python3-1.9.2.1-py3-none-any.whl Requirement already satisfied: typing-extensions<3.8.0,>=3.7.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (3.7.4.3) Requirement already satisfied: future<1.0.0,>=0.18.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (0.18.2) Collecting oauth2client<5,>=2.0.1 Using cached oauth2client-4.1.3-py2.py3-none-any.whl (98 kB) Collecting pydot<2,>=1.2.0 Using cached pydot-1.4.2-py2.py3-none-any.whl (21 kB) Requirement already satisfied: protobuf<4,>=3.12.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (3.15.8) Requirement already satisfied: wrapt in c:\programdata\anaconda3\envs\env\lib\site-packages (from aws-xray-sdk!=0.96,>=0.93->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.12.1) Collecting matplotlib Using cached matplotlib-3.4.1-cp37-cp37m-win_amd64.whl (7.1 MB) Requirement already satisfied: junit-xml~=1.9 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.9) Requirement already satisfied: jsonpatch in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.32) Requirement already satisfied: jsonschema~=3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.2.0) Requirement already satisfied: networkx~=2.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.5.1) Requirement already satisfied: aws-sam-translator>=1.35.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.35.0) Requirement already satisfied: cffi>=1.12 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cryptography>=2.3.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.14.5) Requirement already satisfied: pycparser in c:\programdata\anaconda3\envs\env\lib\site-packages (from cffi>=1.12->cryptography>=2.3.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.20) Requirement already satisfied: pywin32==227 in c:\programdata\anaconda3\envs\env\lib\site-packages (from docker>=2.5.1->moto[s3]==1.3.16->datasets==1.5.0.dev0) (227) Requirement already satisfied: websocket-client>=0.32.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from docker>=2.5.1->moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.58.0) Requirement already satisfied: docopt in c:\programdata\anaconda3\envs\env\lib\site-packages (from hdfs<3.0.0,>=2.1.0->apache-beam>=2.24.0->datasets==1.5.0.dev0) (0.6.2) Requirement already satisfied: filelock in c:\programdata\anaconda3\envs\env\lib\site-packages (from huggingface_hub<0.1.0->datasets==1.5.0.dev0) (3.0.12) Requirement already satisfied: pyrsistent>=0.14.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from jsonschema~=3.0->cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.17.3) Requirement already satisfied: attrs>=17.4.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from jsonschema~=3.0->cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (20.3.0) Requirement already satisfied: decorator<5,>=4.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from networkx~=2.4->cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (4.4.2) Requirement already satisfied: rsa>=3.1.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from oauth2client<5,>=2.0.1->apache-beam>=2.24.0->datasets==1.5.0.dev0) (4.7.2) Requirement already satisfied: pyasn1-modules>=0.0.5 in c:\programdata\anaconda3\envs\env\lib\site-packages (from oauth2client<5,>=2.0.1->apache-beam>=2.24.0->datasets==1.5.0.dev0) (0.2.8) Requirement already satisfied: pyasn1>=0.1.7 in c:\programdata\anaconda3\envs\env\lib\site-packages (from oauth2client<5,>=2.0.1->apache-beam>=2.24.0->datasets==1.5.0.dev0) (0.4.8) Requirement already satisfied: pyparsing>=2.1.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pydot<2,>=1.2.0->apache-beam>=2.24.0->datasets==1.5.0.dev0) (2.4.7) Requirement already satisfied: certifi>=2017.4.17 in c:\programdata\anaconda3\envs\env\lib\site-packages (from requests>=2.19.0->datasets==1.5.0.dev0) (2020.12.5) Requirement already satisfied: chardet<5,>=3.0.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from requests>=2.19.0->datasets==1.5.0.dev0) (4.0.0) Collecting keras-preprocessing~=1.1.2 Using cached Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB) Requirement already satisfied: termcolor~=1.1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorflow>=2.3->datasets==1.5.0.dev0) (1.1.0) Requirement already satisfied: tensorboard~=2.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorflow>=2.3->datasets==1.5.0.dev0) (2.5.0) Requirement already satisfied: wheel~=0.35 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorflow>=2.3->datasets==1.5.0.dev0) (0.36.2) Collecting opt-einsum~=3.3.0 Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB) Collecting gast==0.3.3 Using cached gast-0.3.3-py2.py3-none-any.whl (9.7 kB) Collecting google-pasta~=0.2 Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB) Requirement already satisfied: tensorflow-estimator<2.5.0,>=2.4.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorflow>=2.3->datasets==1.5.0.dev0) (2.4.0) Collecting astunparse~=1.6.3 Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB) Collecting flatbuffers~=1.12.0 Using cached flatbuffers-1.12-py2.py3-none-any.whl (15 kB) Collecting h5py~=2.10.0 Using cached h5py-2.10.0-cp37-cp37m-win_amd64.whl (2.5 MB) Requirement already satisfied: markdown>=2.6.8 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (3.3.4) Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (1.8.0) Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (0.4.4) Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (0.6.0) Requirement already satisfied: google-auth<2,>=1.6.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (1.30.0) Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (4.2.2) Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (1.3.0) Requirement already satisfied: oauthlib>=3.0.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (3.1.0) Requirement already satisfied: regex!=2019.12.17 in c:\programdata\anaconda3\envs\env\lib\site-packages (from transformers->datasets==1.5.0.dev0) (2021.4.4) Requirement already satisfied: tokenizers<0.11,>=0.10.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from transformers->datasets==1.5.0.dev0) (0.10.2) Requirement already satisfied: sacremoses in c:\programdata\anaconda3\envs\env\lib\site-packages (from transformers->datasets==1.5.0.dev0) (0.0.45) Requirement already satisfied: packaging in c:\programdata\anaconda3\envs\env\lib\site-packages (from transformers->datasets==1.5.0.dev0) (20.9) Collecting pathspec<1,>=0.8.1 Using cached pathspec-0.8.1-py2.py3-none-any.whl (28 kB) Requirement already satisfied: click>=7.1.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from black->datasets==1.5.0.dev0) (7.1.2) Collecting appdirs Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB) Collecting mypy-extensions>=0.4.3 Using cached mypy_extensions-0.4.3-py2.py3-none-any.whl (4.5 kB) Requirement already satisfied: typed-ast>=1.4.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from black->datasets==1.5.0.dev0) (1.4.3) Collecting beautifulsoup4 Using cached beautifulsoup4-4.9.3-py3-none-any.whl (115 kB) Requirement already satisfied: soupsieve>1.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from 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multiprocess-0.70.9-py3-none-any.whl Requirement already satisfied: joblib in c:\programdata\anaconda3\envs\env\lib\site-packages (from nltk->datasets==1.5.0.dev0) (1.0.1) Collecting et-xmlfile Using cached et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB) Requirement already satisfied: pyzstd<0.15.0,>=0.14.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from py7zr->datasets==1.5.0.dev0) (0.14.4) Collecting pyppmd<0.13.0,>=0.12.1 Using cached pyppmd-0.12.1-cp37-cp37m-win_amd64.whl (32 kB) Collecting pycryptodome>=3.6.6 Using cached pycryptodome-3.10.1-cp35-abi3-win_amd64.whl (1.6 MB) Collecting bcj-cffi<0.6.0,>=0.5.1 Using cached bcj_cffi-0.5.1-cp37-cp37m-win_amd64.whl (21 kB) Collecting multivolumefile<0.3.0,>=0.2.0 Using cached multivolumefile-0.2.3-py3-none-any.whl (17 kB) Requirement already satisfied: iniconfig in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (1.1.1) Requirement already satisfied: py>=1.8.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (1.10.0) Requirement already satisfied: pluggy<1.0.0a1,>=0.12 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (0.13.1) Requirement already satisfied: atomicwrites>=1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (1.4.0) Requirement already satisfied: colorama in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (0.4.4) Collecting pytest-forked Using cached pytest_forked-1.3.0-py2.py3-none-any.whl (4.7 kB) Collecting execnet>=1.1 Using cached execnet-1.8.0-py2.py3-none-any.whl (39 kB) Requirement already satisfied: apipkg>=1.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from execnet>=1.1->pytest-xdist->datasets==1.5.0.dev0) (1.5) Collecting portalocker==2.0.0 Using cached portalocker-2.0.0-py2.py3-none-any.whl (11 kB) Requirement already satisfied: scikit-learn>=0.21.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from seqeval->datasets==1.5.0.dev0) (0.24.2) Requirement already satisfied: threadpoolctl>=2.0.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from scikit-learn>=0.21.3->seqeval->datasets==1.5.0.dev0) (2.1.0) Building wheels for collected packages: python-Levenshtein Building wheel for python-Levenshtein (setup.py) ... error ERROR: Command errored out with exit status 1: command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\VKC~1\AppData\Local\Temp\pip-wheel-8jh7fm18' cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\ Complete output (27 lines): running bdist_wheel running build running build_py creating build creating build\lib.win-amd64-3.7 creating build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein running egg_info writing python_Levenshtein.egg-info\PKG-INFO writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt writing entry points to python_Levenshtein.egg-info\entry_points.txt writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt writing requirements to python_Levenshtein.egg-info\requires.txt writing top-level names to python_Levenshtein.egg-info\top_level.txt reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no previously-included files matching '*pyc' found anywhere in distribution warning: no previously-included files matching '*so' found anywhere in distribution warning: no previously-included files matching '.project' found anywhere in distribution warning: no previously-included files matching '.pydevproject' found anywhere in distribution writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt' copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein running build_ext building 'Levenshtein._levenshtein' extension error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/ ---------------------------------------- ERROR: Failed building wheel for python-Levenshtein Running setup.py clean for python-Levenshtein Failed to build python-Levenshtein Installing collected packages: python-Levenshtein, pytest-forked, pyppmd, pymongo, pyflakes, pydot, pycryptodome, pycodestyle, pyarrow, portalocker, pathspec, pandas, opt-einsum, oauth2client, nltk, mypy-extensions, multivolumefile, multiprocess, moto, mccabe, matplotlib, keras-preprocessing, huggingface-hub, hdfs, h5py, google-pasta, gast, flatbuffers, fastavro, execnet, et-xmlfile, entrypoints, crcmod, beautifulsoup4, bcj-cffi, avro-python3, astunparse, appdirs, zstandard, tldextract, tensorflow, sklearn, seqeval, sacrebleu, rouge-score, rarfile, pytest-xdist, py7zr, openpyxl, mwparserfromhell, lxml, langdetect, jiwer, isort, flake8, elasticsearch, datasets, conllu, bs4, black, bert-score, apache-beam Running setup.py install for python-Levenshtein ... error ERROR: Command errored out with exit status 1: command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein' cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\ Complete output (27 lines): running install running build running build_py creating build creating build\lib.win-amd64-3.7 creating build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein running egg_info writing python_Levenshtein.egg-info\PKG-INFO writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt writing entry points to python_Levenshtein.egg-info\entry_points.txt writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt writing requirements to python_Levenshtein.egg-info\requires.txt writing top-level names to python_Levenshtein.egg-info\top_level.txt reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no previously-included files matching '*pyc' found anywhere in distribution warning: no previously-included files matching '*so' found anywhere in distribution warning: no previously-included files matching '.project' found anywhere in distribution warning: no previously-included files matching '.pydevproject' found anywhere in distribution writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt' copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein running build_ext building 'Levenshtein._levenshtein' extension error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/ ---------------------------------------- ERROR: Command errored out with exit status 1: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein' Check the logs for full command output. ``` Here are conda and python versions: ```bat (env) C:\testing\datasets>conda --version conda 4.9.2 (env) C:\testing\datasets>python --version Python 3.7.10 ``` Please help me out. Thanks.
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[ "I'm happy to take this on:) One question: The original unlabelled data is stored unsegmented (see e.g. https://github.com/facebookresearch/voxpopuli/blob/main/voxpopuli/get_unlabelled_data.py#L30), but segmenting the audio in the dataset would require a dependency on something like soundfile or torchaudio. An alte...
2021-05-02T12:17:40Z
2023-02-28T17:43:52Z
2023-02-28T17:43:51Z
CONTRIBUTOR
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## Adding a Dataset - **Name:** Voxpopuli - **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings - **Paper:** https://arxiv.org/abs/2101.00390 - **Data:** https://github.com/facebookresearch/voxpopuli - **Motivation:** biggest unlabeled speech dataset **Note**: Since the dataset is so huge, we should only add the config `10k` in the beginning. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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2021-05-02T11:11:11Z
2021-07-23T09:24:16Z
2021-05-03T08:17:38Z
NONE
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## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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