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https://api.github.com/repos/huggingface/datasets/issues/5385 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5385/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5385/comments | https://api.github.com/repos/huggingface/datasets/issues/5385/events | https://github.com/huggingface/datasets/issues/5385 | 1,508,535,532 | I_kwDODunzps5Z6mzs | 5,385 | Is `fs=` deprecated in `load_from_disk()` as well? | {
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"Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ",
"> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? I’ll look around for anywh... | 2022-12-22T21:00:45Z | 2023-01-23T10:50:05Z | 2023-01-23T10:50:04Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec:
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340
Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ?
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779
### Steps to reproduce the bug
n/a
### Expected behavior
n/a
### Environment info
n/a | {
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https://api.github.com/repos/huggingface/datasets/issues/5383 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5383/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5383/comments | https://api.github.com/repos/huggingface/datasets/issues/5383/events | https://github.com/huggingface/datasets/issues/5383 | 1,507,293,968 | I_kwDODunzps5Z13sQ | 5,383 | IterableDataset missing column_names, differs from Dataset interface | {
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"Another example is that `IterableDataset.map` does not have `fn_kwargs`, among other arguments. It makes it harder to convert code from Dataset to IterableDataset.",
"Hi! `fn_kwargs` was added to `IterableDataset.map` in `datasets 2.5.0`, so please update your installation (`pip install -U datasets`) to use it.\... | 2022-12-22T05:27:02Z | 2023-03-13T19:03:33Z | 2023-03-13T19:03:33Z | NONE | null | null | {
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} | ### Describe the bug
The documentation on [Stream](https://huggingface.co/docs/datasets/v1.18.2/stream.html) seems to imply that IterableDataset behaves just like a Dataset. However, examples like
```
dataset.map(augment_data, batched=True, remove_columns=dataset.column_names, ...)
```
will not work because `.column_names` does not exist on IterableDataset. I cannot find any clear explanation on why this is not available, is it an oversight? We do have `iterable_ds.features` available.
### Steps to reproduce the bug
See above
### Expected behavior
Dataset and IterableDataset would be expected to have the same interface, with any differences noted in the documentation.
### Environment info
n/a | {
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https://api.github.com/repos/huggingface/datasets/issues/5381 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5381/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5381/comments | https://api.github.com/repos/huggingface/datasets/issues/5381/events | https://github.com/huggingface/datasets/issues/5381 | 1,504,498,387 | I_kwDODunzps5ZrNLT | 5,381 | Wrong URL for the_pile dataset | {
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"Hi! This error can happen if there is a local file/folder with the same name as the requested dataset. And to avoid it, rename the local file/folder.\r\n\r\nSoon, it will be possible to explicitly request a Hub dataset as follows:https://github.com/huggingface/datasets/issues/5228#issuecomment-1313494020"
] | 2022-12-20T12:40:14Z | 2023-02-15T16:24:57Z | 2023-02-15T16:24:57Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
When trying to load `the_pile` dataset from the library, I get a `FileNotFound` error.
### Steps to reproduce the bug
Steps to reproduce:
Run:
```
from datasets import load_dataset
dataset = load_dataset("the_pile")
```
I get the output:
"name": "FileNotFoundError",
"message": "Unable to resolve any data file that matches '['**']' at /storage/store/work/lgrinszt/memorization/the_pile with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'GRIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG', 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF', 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ircam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'OGG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']"
### Expected behavior
`the_pile` dataset should be dowloaded.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-4.15.0-112-generic-x86_64-with-glibc2.27
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5378 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5378/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5378/comments | https://api.github.com/repos/huggingface/datasets/issues/5378/events | https://github.com/huggingface/datasets/issues/5378 | 1,503,887,508 | I_kwDODunzps5Zo4CU | 5,378 | The dataset "the_pile", subset "enron_emails" , load_dataset() failure | {
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"Thanks for reporting @shaoyuta. We are investigating it.\r\n\r\nWe are transferring the issue to \"the_pile\" Community tab on the Hub: https://huggingface.co/datasets/the_pile/discussions/4"
] | 2022-12-20T02:19:13Z | 2022-12-20T07:52:54Z | 2022-12-20T07:52:54Z | NONE | null | null | {
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} | ### Describe the bug
When run
"datasets.load_dataset("the_pile","enron_emails")" failure
### Steps to reproduce the bug
Run below code in python cli:
>>> import datasets
>>> datasets.load_dataset("the_pile","enron_emails")
### Expected behavior
Load dataset "the_pile", "enron_emails" successfully.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.7.1
- Platform: Linux-5.15.0-53-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.0
- Pandas version: 1.4.3
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"The data files are hosted on HF at https://huggingface.co/datasets/allenai/c4/tree/main\r\n\r\nYou have 200 runs streaming the same files in parallel. So this is probably a Hub limitation. Maybe rate limiting ? cc @julien-c \r\n\r\nMaybe you can also try to reduce the number of HTTP requests by increasing the bloc... | 2022-12-18T11:38:58Z | 2023-07-24T15:23:07Z | 2023-07-24T15:23:07Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
`streaming_download_manager` seems to disconnect if too many runs access the same underlying dataset 🧐
The code works fine for me if I have ~100 runs in parallel, but disconnects once scaling to 200.
Possibly related:
- https://github.com/huggingface/datasets/pull/3100
- https://github.com/huggingface/datasets/pull/3050
### Steps to reproduce the bug
Running
```python
c4 = datasets.load_dataset("c4", "en", split="train", streaming=True).skip(args.start).take(args.end-args.start)
df = pd.DataFrame(c4, index=None)
```
with different start & end arguments on 200 CPUs in parallel yields:
```
WARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4.
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [1/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [2/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [3/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [4/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [5/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [6/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [7/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [8/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [9/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [10/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [11/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [12/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [13/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [14/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [15/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [16/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [17/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [18/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [19/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [20/20]
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/dec-2022-tasky/inference │
│ _c4.py:68 in <module> │
│ │
│ 65 │ model.eval() │
│ 66 │ │
│ 67 │ c4 = datasets.load_dataset("c4", "en", split="train", streaming=Tru │
│ ❱ 68 │ df = pd.DataFrame(c4, index=None) │
│ 69 │ texts = df["text"].to_list() │
│ 70 │ preds = batch_inference(texts, batch_size=args.batch_size) │
│ 71 │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/site-packages/pandas/core/frame.p │
│ y:684 in __init__ │
│ │
│ 681 │ │ # For data is list-like, or Iterable (will consume into list │
│ 682 │ │ elif is_list_like(data): │
│ 683 │ │ │ if not isinstance(data, (abc.Sequence, ExtensionArray)): │
│ ❱ 684 │ │ │ │ data = list(data) │
│ 685 │ │ │ if len(data) > 0: │
│ 686 │ │ │ │ if is_dataclass(data[0]): │
│ 687 │ │ │ │ │ data = dataclasses_to_dicts(data) │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:751 in __iter__ │
│ │
│ 748 │ │ yield from ex_iterable.shard_data_sources(shard_idx) │
│ 749 │ │
│ 750 │ def __iter__(self): │
│ ❱ 751 │ │ for key, example in self._iter(): │
│ 752 │ │ │ if self.features: │
│ 753 │ │ │ │ # `IterableDataset` automatically fills missing colum │
│ 754 │ │ │ │ # This is done with `_apply_feature_types`. │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:741 in _iter │
│ │
│ 738 │ │ │ ex_iterable = self._ex_iterable.shuffle_data_sources(self │
│ 739 │ │ else: │
│ 740 │ │ │ ex_iterable = self._ex_iterable │
│ ❱ 741 │ │ yield from ex_iterable │
│ 742 │ │
│ 743 │ def _iter_shard(self, shard_idx: int): │
│ 744 │ │ if self._shuffling: │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:617 in __iter__ │
│ │
│ 614 │ │ self.n = n │
│ 615 │ │
│ 616 │ def __iter__(self): │
│ ❱ 617 │ │ yield from islice(self.ex_iterable, self.n) │
│ 618 │ │
│ 619 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │
│ 620 │ │ """Doesn't shuffle the wrapped examples iterable since it wou │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:594 in __iter__ │
│ │
│ 591 │ │
│ 592 │ def __iter__(self): │
│ 593 │ │ #ex_iterator = iter(self.ex_iterable) │
│ ❱ 594 │ │ yield from islice(self.ex_iterable, self.n, None) │
│ 595 │ │ #for _ in range(self.n): │
│ 596 │ │ # next(ex_iterator) │
│ 597 │ │ #yield from islice(ex_iterator, self.n, None) │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:106 in __iter__ │
│ │
│ 103 │ │ self.kwargs = kwargs │
│ 104 │ │
│ 105 │ def __iter__(self): │
│ ❱ 106 │ │ yield from self.generate_examples_fn(**self.kwargs) │
│ 107 │ │
│ 108 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │
│ 109 │ │ return ShardShuffledExamplesIterable(self.generate_examples_f │
│ │
│ /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/d │
│ f532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01/c4.py:89 in │
│ _generate_examples │
│ │
│ 86 │ │ for filepath in filepaths: │
│ 87 │ │ │ logger.info("generating examples from = %s", filepath) │
│ 88 │ │ │ with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8" │
│ ❱ 89 │ │ │ │ for line in f: │
│ 90 │ │ │ │ │ if line: │
│ 91 │ │ │ │ │ │ example = json.loads(line) │
│ 92 │ │ │ │ │ │ yield id_, example │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:313 in read1 │
│ │
│ 310 │ │ │
│ 311 │ │ if size < 0: │
│ 312 │ │ │ size = io.DEFAULT_BUFFER_SIZE │
│ ❱ 313 │ │ return self._buffer.read1(size) │
│ 314 │ │
│ 315 │ def peek(self, n): │
│ 316 │ │ self._check_not_closed() │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/_compression.py:68 in readinto │
│ │
│ 65 │ │
│ 66 │ def readinto(self, b): │
│ 67 │ │ with memoryview(b) as view, view.cast("B") as byte_view: │
│ ❱ 68 │ │ │ data = self.read(len(byte_view)) │
│ 69 │ │ │ byte_view[:len(data)] = data │
│ 70 │ │ return len(data) │
│ 71 │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:493 in read │
│ │
│ 490 │ │ │ │ self._new_member = False │
│ 491 │ │ │ │
│ 492 │ │ │ # Read a chunk of data from the file │
│ ❱ 493 │ │ │ buf = self._fp.read(io.DEFAULT_BUFFER_SIZE) │
│ 494 │ │ │ │
│ 495 │ │ │ uncompress = self._decompressor.decompress(buf, size) │
│ 496 │ │ │ if self._decompressor.unconsumed_tail != b"": │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:96 in read │
│ │
│ 93 │ │ │ read = self._read │
│ 94 │ │ │ self._read = None │
│ 95 │ │ │ return self._buffer[read:] + \ │
│ ❱ 96 │ │ │ │ self.file.read(size-self._length+read) │
│ 97 │ │
│ 98 │ def prepend(self, prepend=b''): │
│ 99 │ │ if self._read is None: │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/download/streaming_download_manager.py: │
│ 365 in read_with_retries │
│ │
│ 362 │ │ │ │ ) │
│ 363 │ │ │ │ time.sleep(config.STREAMING_READ_RETRY_INTERVAL) │
│ 364 │ │ else: │
│ ❱ 365 │ │ │ raise ConnectionError("Server Disconnected") │
│ 366 │ │ return out │
│ 367 │ │
│ 368 │ file_obj.read = read_with_retries │
╰──────────────────────────────────────────────────────────────────────────────╯
ConnectionError: Server Disconnected
```
### Expected behavior
There should be no disconnect I think.
### Environment info
```
datasets=2.7.0
Python 3.9.12
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"Thanks for reporting, @shaoyuta.\r\n\r\nWe have checked and yes, apparently there is an issue with the server hosting the data of the \"enron_emails\" subset of \"the_pile\" dataset: http://eaidata.bmk.sh/data/enron_emails.jsonl.zst\r\nIt seems to be down: The connection has timed out.\r\n\r\nPlease note that at t... | 2022-12-15T01:23:03Z | 2022-12-15T07:45:54Z | 2022-12-15T07:45:53Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Run model "GPT-J" with dataset "the_pile" fail.
The fail out is as below:
Looks like which is due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst" unreachable .
### Steps to reproduce the bug
Steps to reproduce this issue:
git clone https://github.com/huggingface/transformers
cd transformers
python examples/pytorch/language-modeling/run_clm.py --model_name_or_path EleutherAI/gpt-j-6B --dataset_name the_pile --dataset_config_name enron_emails --do_eval --output_dir /tmp/output --overwrite_output_dir
### Expected behavior
This issue looks like due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst " couldn't be reached.
Is there another way to download the dataset "the_pile" ?
Is there another way to cache the dataset "the_pile" but not let the hg to download it when runtime ?
### Environment info
huggingface_hub version: 0.11.1
Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Python version: 3.9.12
Running in iPython ?: No
Running in notebook ?: No
Running in Google Colab ?: No
Token path ?: /home/taosy/.huggingface/token
Has saved token ?: False
Configured git credential helpers:
FastAI: N/A
Tensorflow: N/A
Torch: N/A
Jinja2: N/A
Graphviz: N/A
Pydot: N/A | {
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"You can try something like this ?\r\n```python\r\ndef mapper_function(batch):\r\n return {\"concatenated_audio\": [np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]])]}\r\n\r\ndataset = dataset.map(\r\n mapper_function,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.... | 2022-12-14T18:13:55Z | 2023-07-21T14:30:51Z | 2023-07-21T14:30:51Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I am trying to do concatenate audios in a dataset e.g. `google/fleurs`.
```python
print(dataset)
# Dataset({
# features: ['path', 'audio'],
# num_rows: 24
# })
def mapper_function(batch):
# to merge every 3 audio
# np.concatnate(audios[i: i+3]) for i in range(i, len(batch), 3)
dataset = dataset.map(mapper_function, batch=True, batch_size=24)
print(dataset)
# Expected output:
# Dataset({
# features: ['path', 'audio'],
# num_rows: 8
# })
```
I tried to construct `result={}` dictionary inside the mapper function, I just found it will not work because it needs `byte` also needed :((
I'd appreciate if your share any use cases similar to my problem or any solutions really. Thanks!
cc: @lhoestq
### Steps to reproduce the bug
1. load audio dataset
2. try to merge every k audios and return as one
### Expected behavior
Merged dataset with a fewer rows. If we merge every 3 rows, then `n // 3` number of examples.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5360 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5360/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5360/comments | https://api.github.com/repos/huggingface/datasets/issues/5360/events | https://github.com/huggingface/datasets/issues/5360 | 1,496,947,177 | I_kwDODunzps5ZOZnp | 5,360 | IterableDataset returns duplicated data using PyTorch DDP | {
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"If you use huggingface trainer, you will find the trainer has wrapped a `IterableDatasetShard` to avoid duplication.\r\nSee:\r\nhttps://github.com/huggingface/transformers/blob/dfd818420dcbad68e05a502495cf666d338b2bfb/src/transformers/trainer.py#L835\r\n",
"If you want to support it by datasets natively, maybe w... | 2022-12-14T16:06:19Z | 2023-06-15T09:51:13Z | 2023-01-16T13:33:33Z | MEMBER | null | null | {
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} | As mentioned in https://github.com/huggingface/datasets/issues/3423, when using PyTorch DDP the dataset ends up with duplicated data. We already check for the PyTorch `worker_info` for single node, but we should also check for `torch.distributed.get_world_size()` and `torch.distributed.get_rank()` | {
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https://api.github.com/repos/huggingface/datasets/issues/5353 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5353/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5353/comments | https://api.github.com/repos/huggingface/datasets/issues/5353/events | https://github.com/huggingface/datasets/issues/5353 | 1,491,880,500 | I_kwDODunzps5Y7Eo0 | 5,353 | Support remote file systems for `Audio` | {
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"Just seen https://github.com/huggingface/datasets/issues/5281"
] | 2022-12-12T13:22:13Z | 2022-12-12T13:37:14Z | 2022-12-12T13:37:14Z | NONE | null | null | {
"completed": 0,
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} | ### Feature request
Hi there!
It would be super cool if `Audio()`, and potentially other features, could read files from a remote file system.
### Motivation
Large amounts of data is often stored in buckets. `load_from_disk` is able to retrieve data from cloud storage but to my knowledge actually copies the datasets across first, so if you're working off a system with smaller disk specs (like a VM), you can run out of space very quickly.
### Your contribution
Something like this (for Google Cloud Platform in this instance):
```python
from datasets import Dataset, Audio
import gcsfs
fs = gcsfs.GCSFileSystem()
list_of_audio_fp = {'audio': ['1', '2', '3']}
ds = Dataset.from_dict(list_of_audio_fp)
ds = ds.cast_column("audio", Audio(sampling_rate=16000, fs=fs))
```
Under the hood:
```python
import librosa
from io import BytesIO
def load_audio(fp, sampling_rate=None, fs=None):
if fs is not None:
with fs.open(fp, 'rb') as f:
arr, sr = librosa.load(BytesIO(f), sr=sampling_rate)
else:
# Perform existing io operations
```
Written from memory so some things could be wrong. | {
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"Hi! `DatasetBuilder` is a parent class for concrete builders: `GeneratorBasedBuilder`, `ArrowBasedBuilder` and `BeamBasedBuilder`. When writing a builder script, these classes are the ones you should inherit from. And since all of them implement `_prepare_split`, you only have to implement the three methods mentio... | 2022-12-12T01:38:54Z | 2022-12-20T18:20:57Z | 2022-12-12T16:48:56Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I'm not sure this is a bug or if it's just missing in the documentation, or i'm not doing something correctly, but I'm subclassing `DatasetBuilder` and getting the following error because on the `DatasetBuilder` class the `_prepare_split` method is abstract (as are the others we are required to implement, hence the genesis of my question):
```
Traceback (most recent call last):
File "/home/jason/source/python/prism_machine_learning/examples/create_hf_datasets.py", line 28, in <module>
dataset_builder.download_and_prepare()
File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 1124, in _prepare_split
raise NotImplementedError()
NotImplementedError
```
### Steps to reproduce the bug
I will share implementation if it turns out that everything should be working (i.e. we only need to implement those 3 methods the docs mention), but I don't want to distract from the original question.
### Expected behavior
I just need to know if there are additional methods we need to implement when subclassing `DatasetBuilder` besides what the documentation specifies -> `_info`, `_split_generators` and `_generate_examples`
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-135-generic-x86_64-with-glibc2.2.5
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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https://api.github.com/repos/huggingface/datasets/issues/5346 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5346/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5346/comments | https://api.github.com/repos/huggingface/datasets/issues/5346/events | https://github.com/huggingface/datasets/issues/5346 | 1,486,884,983 | I_kwDODunzps5YoBB3 | 5,346 | [Quick poll] Give your opinion on the future of the Hugging Face Open Source ecosystem! | {
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"As the survey is finished, can we close this issue, @LysandreJik ?",
"Yes! I'll post a public summary on the forums shortly.",
"Is the summary available? I would be interested in reading your findings."
] | 2022-12-09T14:48:02Z | 2023-06-02T20:24:44Z | 2023-01-25T19:35:40Z | MEMBER | null | null | {
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} | Thanks to all of you, Datasets is just about to pass 15k stars!
Since the last survey, a lot has happened: the [diffusers](https://github.com/huggingface/diffusers), [evaluate](https://github.com/huggingface/evaluate) and [skops](https://github.com/skops-dev/skops) libraries were born. `timm` joined the Hugging Face ecosystem. There were 25 new releases of `transformers`, 21 new releases of `datasets`, 13 new releases of `accelerate`.
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Thank you all on behalf of the HuggingFace team! 🤗 | {
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https://api.github.com/repos/huggingface/datasets/issues/5343 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5343/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5343/comments | https://api.github.com/repos/huggingface/datasets/issues/5343/events | https://github.com/huggingface/datasets/issues/5343 | 1,485,297,823 | I_kwDODunzps5Yh9if | 5,343 | T5 for Q&A produces truncated sentence | {
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} | Dear all, I am fine-tuning T5 for Q&A task using the MedQuAD ([GitHub - abachaa/MedQuAD: Medical Question Answering Dataset of 47,457 QA pairs created from 12 NIH websites](https://github.com/abachaa/MedQuAD)) dataset. In the dataset, there are many long answers with thousands of words. I have used pytorch_lightning to train the T5-large model. I have two questions.
For example, I set both the max_length, max_input_length, max_output_length to 128.
How to deal with those long answers? I just left them as is and the T5Tokenizer can automatically handle. I would assume the tokenizer just truncates an answer at the position of 128th word (or 127th). Is it possible that I manually split an answer into different parts, each part has 128 words; and then all these sub-answers serve as a separate answer to the same question?
Another question is that I get incomplete (truncated) answers when using the fine-tuned model in inference, even though the predicted answer is shorter than 128 words. I found a message posted 2 years ago saying that one should add at the end of texts when fine-tuning T5. I followed that but then got a warning message that duplicated were found. I am assuming that this is because the tokenizer truncates an answer text, thus is missing in the truncated answer, such that the end token is not produced in predicted answer. However, I am not sure. Can anybody point out how to address this issue?
Any suggestions are highly appreciated.
Below is some code snippet.
`
import pytorch_lightning as pl
from torch.utils.data import DataLoader
import torch
import numpy as np
import time
from pathlib import Path
from transformers import (
Adafactor,
T5ForConditionalGeneration,
T5Tokenizer,
get_linear_schedule_with_warmup
)
from torch.utils.data import RandomSampler
from question_answering.utils import *
class T5FineTuner(pl.LightningModule):
def __init__(self, hyparams):
super(T5FineTuner, self).__init__()
self.hyparams = hyparams
self.model = T5ForConditionalGeneration.from_pretrained(hyparams.model_name_or_path)
self.tokenizer = T5Tokenizer.from_pretrained(hyparams.tokenizer_name_or_path)
if self.hyparams.freeze_embeds:
self.freeze_embeds()
if self.hyparams.freeze_encoder:
self.freeze_params(self.model.get_encoder())
# assert_all_frozen()
self.step_count = 0
self.output_dir = Path(self.hyparams.output_dir)
n_observations_per_split = {
'train': self.hyparams.n_train,
'validation': self.hyparams.n_val,
'test': self.hyparams.n_test
}
self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
self.em_score_list = []
self.subset_score_list = []
data_folder = r'C:\Datasets\MedQuAD-master'
self.train_data, self.val_data, self.test_data = load_medqa_data(data_folder)
def freeze_params(self, model):
for param in model.parameters():
param.requires_grad = False
def freeze_embeds(self):
try:
self.freeze_params(self.model.model.shared)
for d in [self.model.model.encoder, self.model.model.decoder]:
self.freeze_params(d.embed_positions)
self.freeze_params(d.embed_tokens)
except AttributeError:
self.freeze_params(self.model.shared)
for d in [self.model.encoder, self.model.decoder]:
self.freeze_params(d.embed_tokens)
def lmap(self, f, x):
return list(map(f, x))
def is_logger(self):
return self.trainer.proc_rank <= 0
def forward(self, input_ids, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, labels=None):
return self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
labels=labels
)
def _step(self, batch):
labels = batch['target_ids']
labels[labels[:, :] == self.tokenizer.pad_token_id] = -100
outputs = self(
input_ids = batch['source_ids'],
attention_mask=batch['source_mask'],
labels=labels,
decoder_attention_mask=batch['target_mask']
)
loss = outputs[0]
return loss
def ids_to_clean_text(self, generated_ids):
gen_text = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
return self.lmap(str.strip, gen_text)
def _generative_step(self, batch):
t0 = time.time()
generated_ids = self.model.generate(
batch["source_ids"],
attention_mask=batch["source_mask"],
use_cache=True,
decoder_attention_mask=batch['target_mask'],
max_length=128,
num_beams=2,
early_stopping=True
)
preds = self.ids_to_clean_text(generated_ids)
targets = self.ids_to_clean_text(batch["target_ids"])
gen_time = (time.time() - t0) / batch["source_ids"].shape[0]
loss = self._step(batch)
base_metrics = {'val_loss': loss}
summ_len = np.mean(self.lmap(len, generated_ids))
base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=targets)
em_score, subset_match_score = calculate_scores(preds, targets)
self.em_score_list.append(em_score)
self.subset_score_list.append(subset_match_score)
em_score = torch.tensor(em_score, dtype=torch.float32)
subset_match_score = torch.tensor(subset_match_score, dtype=torch.float32)
base_metrics.update(em_score=em_score, subset_match_score=subset_match_score)
# rouge_results = self.rouge_metric.compute()
# rouge_dict = self.parse_score(rouge_results)
return base_metrics
def training_step(self, batch, batch_idx):
loss = self._step(batch)
tensorboard_logs = {'train_loss': loss}
return {'loss': loss, 'log': tensorboard_logs}
def training_epoch_end(self, outputs):
avg_train_loss = torch.stack([x['loss'] for x in outputs]).mean()
tensorboard_logs = {'avg_train_loss': avg_train_loss}
# return {'avg_train_loss': avg_train_loss, 'log': tensorboard_logs, 'progress_bar': tensorboard_logs}
def validation_step(self, batch, batch_idx):
return self._generative_step(batch)
def validation_epoch_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
tensorboard_logs = {'val_loss': avg_loss}
if len(self.em_score_list) <= 2:
average_em_score = sum(self.em_score_list) / len(self.em_score_list)
average_subset_match_score = sum(self.subset_score_list) / len(self.subset_score_list)
else:
latest_em_score = self.em_score_list[:-2]
latest_subset_score = self.subset_score_list[:-2]
average_em_score = sum(latest_em_score) / len(latest_em_score)
average_subset_match_score = sum(latest_subset_score) / len(latest_subset_score)
average_em_score = torch.tensor(average_em_score, dtype=torch.float32)
average_subset_match_score = torch.tensor(average_subset_match_score, dtype=torch.float32)
tensorboard_logs.update(em_score=average_em_score, subset_match_score=average_subset_match_score)
self.target_gen = []
self.prediction_gen = []
return {
'avg_val_loss': avg_loss,
'em_score': average_em_score,
'subset_match_socre': average_subset_match_score,
'log': tensorboard_logs,
'progress_bar': tensorboard_logs
}
def configure_optimizers(self):
model = self.model
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": self.hyparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
optimizer = Adafactor(optimizer_grouped_parameters, lr=self.hyparams.learning_rate, scale_parameter=False,
relative_step=False)
self.opt = optimizer
return [optimizer]
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure=None,
on_tpu=False, using_native_amp=False, using_lbfgs=False):
optimizer.step(closure=optimizer_closure)
optimizer.zero_grad()
self.lr_scheduler.step()
def get_tqdm_dict(self):
tqdm_dict = {"loss": "{:.3f}".format(self.trainer.avg_loss), "lr": self.lr_scheduler.get_last_lr()[-1]}
return tqdm_dict
def train_dataloader(self):
n_samples = self.n_obs['train']
train_dataset = get_dataset(tokenizer=self.tokenizer, data=self.train_data, num_samples=n_samples,
args=self.hyparams)
sampler = RandomSampler(train_dataset)
dataloader = DataLoader(train_dataset, sampler=sampler, batch_size=self.hyparams.train_batch_size,
drop_last=True, num_workers=4)
# t_total = (
# (len(dataloader.dataset) // (self.hyparams.train_batch_size * max(1, self.hyparams.n_gpu)))
# // self.hyparams.gradient_accumulation_steps
# * float(self.hyparams.num_train_epochs)
# )
t_total = 100000
scheduler = get_linear_schedule_with_warmup(
self.opt, num_warmup_steps=self.hyparams.warmup_steps, num_training_steps=t_total
)
self.lr_scheduler = scheduler
return dataloader
def val_dataloader(self):
n_samples = self.n_obs['validation']
validation_dataset = get_dataset(tokenizer=self.tokenizer, data=self.val_data, num_samples=n_samples,
args=self.hyparams)
sampler = RandomSampler(validation_dataset)
return DataLoader(validation_dataset, shuffle=False, batch_size=self.hyparams.eval_batch_size, sampler=sampler, num_workers=4)
def test_dataloader(self):
n_samples = self.n_obs['test']
test_dataset = get_dataset(tokenizer=self.tokenizer, data=self.test_data, num_samples=n_samples, args=self.hyparams)
return DataLoader(test_dataset, batch_size=self.hyparams.eval_batch_size, num_workers=4)
def on_save_checkpoint(self, checkpoint):
save_path = self.output_dir.joinpath("best_tfmr")
self.model.config.save_step = self.step_count
self.model.save_pretrained(save_path)
self.tokenizer.save_pretrained(save_path)
import os
import argparse
import pytorch_lightning as pl
from question_answering.t5_closed_book import T5FineTuner
if __name__ == '__main__':
args_dict = dict(
output_dir="", # path to save the checkpoints
model_name_or_path='t5-large',
tokenizer_name_or_path='t5-large',
max_input_length=128,
max_output_length=128,
freeze_encoder=False,
freeze_embeds=False,
learning_rate=1e-5,
weight_decay=0.0,
adam_epsilon=1e-8,
warmup_steps=0,
train_batch_size=4,
eval_batch_size=4,
num_train_epochs=2,
gradient_accumulation_steps=10,
n_gpu=1,
resume_from_checkpoint=None,
val_check_interval=0.5,
n_val=4000,
n_train=-1,
n_test=-1,
early_stop_callback=False,
fp_16=False,
opt_level='O1',
max_grad_norm=1.0,
seed=101,
)
args_dict.update({'output_dir': 't5_large_MedQuAD_256', 'num_train_epochs': 100,
'train_batch_size': 16, 'eval_batch_size': 16, 'learning_rate': 1e-3})
args = argparse.Namespace(**args_dict)
checkpoint_callback = pl.callbacks.ModelCheckpoint(dirpath=args.output_dir, monitor="em_score", mode="max", save_top_k=1)
## If resuming from checkpoint, add an arg resume_from_checkpoint
train_params = dict(
accumulate_grad_batches=args.gradient_accumulation_steps,
gpus=args.n_gpu,
max_epochs=args.num_train_epochs,
# early_stop_callback=False,
precision=16 if args.fp_16 else 32,
# amp_level=args.opt_level,
# resume_from_checkpoint=args.resume_from_checkpoint,
gradient_clip_val=args.max_grad_norm,
checkpoint_callback=checkpoint_callback,
val_check_interval=args.val_check_interval,
# accelerator='dp'
# logger=wandb_logger,
# callbacks=[LoggingCallback()],
)
model = T5FineTuner(args)
trainer = pl.Trainer(**train_params)
trainer.fit(model)
` | {
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https://api.github.com/repos/huggingface/datasets/issues/5342 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5342/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5342/comments | https://api.github.com/repos/huggingface/datasets/issues/5342/events | https://github.com/huggingface/datasets/issues/5342 | 1,485,244,178 | I_kwDODunzps5YhwcS | 5,342 | Emotion dataset cannot be downloaded | {
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"Hi @cbarond there's already an open issue at https://github.com/dair-ai/emotion_dataset/issues/5, as the data seems to be missing now, so check that issue instead 👍🏻 ",
"Thanks @cbarond for reporting and @alvarobartt for pointing to the issue we opened in the author's repo.\r\n\r\nIndeed, this issue was first ... | 2022-12-08T19:07:09Z | 2023-02-23T19:13:19Z | 2022-12-09T10:46:11Z | NONE | null | null | {
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} | ### Describe the bug
The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`.
It was working yesterday (December 7, 2022), but stopped working today (December 8, 2022).
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("emotion")
```
### Expected behavior
The dataset should load properly.
### Environment info
- `datasets` version: 2.7.1
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.13
- PyArrow version: 10.0.1
- Pandas version: 1.5.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5338 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5338/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5338/comments | https://api.github.com/repos/huggingface/datasets/issues/5338/events | https://github.com/huggingface/datasets/issues/5338 | 1,482,646,151 | I_kwDODunzps5YX2KH | 5,338 | `map()` stops every 1000 steps | {
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"Hi !\r\n\r\n> It starts using all the cores (I am not sure why because I did not pass num_proc)\r\n\r\nThe tokenizer uses Rust code that is multithreaded. And maybe the `feature_extractor` might run some things in parallel as well - but I'm not super familiar with its internals.\r\n\r\n> then progress bar stops at... | 2022-12-07T19:09:40Z | 2025-02-14T18:10:07Z | 2022-12-10T00:39:28Z | NONE | null | null | {
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} | ### Describe the bug
I am passing the following `prepare_dataset` function to `Dataset.map` (code is inspired from [here](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py#L454))
```python3
def prepare_dataset(batch):
# load and resample audio data from 48 to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
# encode target text to label ids
batch["labels"] = tokenizer(batch[text_column]).input_ids
return batch
...
train_ds = train_ds.map(prepare_dataset)
```
Here is the exact code I am running https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets/blob/main/train.py#L70-L71
It starts using all the cores (I am not sure why because I did not pass `num_proc`)
then progress bar stops at every 1k steps. (starts using a single core)
then come back to using all the cores again.
link to [screen record](https://youtu.be/jPQpQQGp6Gc)
Can someone explain this process and maybe provide a way to improve this pipeline? cc: @lhoestq
### Steps to reproduce the bug
1. load the dataset
2. create a Whisper processor
3. create a `prepare_dataset` function
4. pass the function to `dataset.map(prepare_dataset)`
### Expected behavior
- Use a single core per a function
- not to stop at some point?
### Environment info
- `datasets` version: 2.7.1.dev0
- Platform: Linux-5.4.0-109-generic-x86_64-with-glibc2.27
- Python version: 3.8.10
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5337 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5337/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5337/comments | https://api.github.com/repos/huggingface/datasets/issues/5337/events | https://github.com/huggingface/datasets/issues/5337 | 1,481,692,156 | I_kwDODunzps5YUNP8 | 5,337 | Support webdataset format | {
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"I like the idea of having `webdataset` as an optional dependency to ensure our loader generates web datasets the same way as the main project.",
"Webdataset is the one of the most popular dataset formats for large scale computer vision tasks. Upvote for this issue. ",
"Any updates on this?",
"We haven't had ... | 2022-12-07T11:32:25Z | 2024-03-06T14:39:29Z | 2024-03-06T14:39:28Z | MEMBER | null | null | {
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} | Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234.
In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local directory, or from a dataset on the Hugging Face Hub). Some datasets on the Hub are already in webdataset format.
It terms of implementation, we can have something similar to the Parquet loader.
I also think it's fine to have webdataset as an optional dependency. | {
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https://api.github.com/repos/huggingface/datasets/issues/5332 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5332/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5332/comments | https://api.github.com/repos/huggingface/datasets/issues/5332/events | https://github.com/huggingface/datasets/issues/5332 | 1,476,513,072 | I_kwDODunzps5YAc0w | 5,332 | Passing numpy array to ClassLabel names causes ValueError | {
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"Should `datasets` allow `ClassLabel` input parameter to be an `np.array` even though internally we need to cast it to a Python list? @lhoestq @mariosasko ",
"Hi! No, I don't think so. The `names` parameter is [annotated](https://github.com/huggingface/datasets/blob/582236640b9109988e5f7a16a8353696ffa09a16/src/d... | 2022-12-05T12:59:03Z | 2022-12-22T16:32:50Z | 2022-12-22T16:32:50Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
If I define my classes as:
```
my_classes = np.array(['one', 'two', 'three'])
```
Then this errors:
```py
features = Features({'value': Value('string'), 'label': ClassLabel(names=my_classes)})
dataset = Dataset.from_list(my_data, features=features)
```
```
ValueError Traceback (most recent call last)
[<ipython-input-8-a8a9d53ec82f>](https://localhost:8080/#) in <module>
----> 1 dataset = Dataset.from_list(my_data, features=features)
11 frames
[/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in _asdict_inner(obj)
183 for f in fields(obj):
184 value = _asdict_inner(getattr(obj, f.name))
--> 185 if not f.init or value != f.default or f.metadata.get("include_in_asdict_even_if_is_default", False):
186 result[f.name] = value
187 return result
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
But this works:
```
features2 = Features({'value': Value('string'), 'label': ClassLabel(names=list(my_classes))})
dataset2 = Dataset.from_list(my_data, features=features2)
```
### Expected behavior
If I provide a numpy array of class names, I would expect either an error that the names list is the wrong type, or for it to be cast internally.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.10
- Python version: 3.8.15
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
Additionally:
- Numpy version: 1.23.5
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} | Since:
- #5250
- Commit: 703b84311f4ead83c7f79639f2dfa739295f0be6
the docs for main branch are no longer built.
The change introduced only triggers the docs building for releases. | {
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https://api.github.com/repos/huggingface/datasets/issues/5325 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5325/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5325/comments | https://api.github.com/repos/huggingface/datasets/issues/5325/events | https://github.com/huggingface/datasets/issues/5325 | 1,471,536,822 | I_kwDODunzps5Xtd62 | 5,325 | map(...batch_size=None) for IterableDataset | {
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"Hi! I agree it makes sense for `IterableDataset.map` to support the `batch_size=None` case. This should be super easy to fix.",
"@mariosasko as this is something simple maybe I can include it as part of https://github.com/huggingface/datasets/pull/5311? Let me know :+1:",
"#self-assign",
"Feel free to close ... | 2022-12-01T15:43:42Z | 2022-12-07T15:54:43Z | 2022-12-07T15:54:42Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ### Feature request
Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too.
### Motivation
Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple of reasons why this might be nice.
One is that load_dataset(...) can return either IterableDataset or Dataset. mypy will then complain if batch_size=None even if we know it is Dataset. Of course we can do:
assert isinstance(d, datasets.DatasetDict)
But it is a mild inconvenience. What's more annoying is that whenever we use something like e.g. `combine_datasets(...)`, we end up with the union again, and so have to do the assert again.
Another is that we could actually end up with an IterableDataset small enough for memory in normal/correct usage, e.g. by filtering a massive IterableDataset.
For practical usages, an alternative to this would be to convert from an iterable dataset to a map-style dataset, but it is not obvious how to do this.
### Your contribution
Not this time. | {
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https://api.github.com/repos/huggingface/datasets/issues/5323 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5323/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5323/comments | https://api.github.com/repos/huggingface/datasets/issues/5323/events | https://github.com/huggingface/datasets/issues/5323 | 1,471,518,803 | I_kwDODunzps5XtZhT | 5,323 | Duplicated Keys in Taskmaster-2 Dataset | {
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"Thanks for reporting, @liaeh.\r\n\r\nWe are having a look at it. ",
"I have transferred the discussion to the Community tab of the dataset: https://huggingface.co/datasets/taskmaster2/discussions/1"
] | 2022-12-01T15:31:06Z | 2022-12-01T16:26:06Z | 2022-12-01T16:26:06Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Loading certain splits () of the taskmaster-2 dataset fails because of a DuplicatedKeysError. This occurs for the following domains: `'hotels', 'movies', 'music', 'sports'`. The domains `'flights', 'food-ordering', 'restaurant-search'` load fine.
Output:
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("taskmaster2", "music")
```
Output:
```
---------------------------------------------------------------------------
DuplicatedKeysError Traceback (most recent call last)
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1532, in GeneratorBasedBuilder._prepare_split_single(self, arg)
[1531](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1530) example = self.info.features.encode_example(record) if self.info.features is not None else record
-> [1532](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1531) writer.write(example, key)
[1533](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1532) num_examples_progress_update += 1
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:475, in ArrowWriter.write(self, example, key, writer_batch_size)
[474](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=473) if self._check_duplicates:
--> [475](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=474) self.check_duplicate_keys()
[476](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=475) # Re-intializing to empty list for next batch
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:492, in ArrowWriter.check_duplicate_keys(self)
[486](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=485) duplicate_key_indices = [
[487](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=486) str(self._num_examples + index)
[488](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=487) for index, (duplicate_hash, _) in enumerate(self.hkey_record)
[489](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=488) if duplicate_hash == hash
[490](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=489) ]
--> [492](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=491) raise DuplicatedKeysError(key, duplicate_key_indices)
[493](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=492) else:
DuplicatedKeysError: Found multiple examples generated with the same key
The examples at index 858, 859 have the key dlg-89174425-d57a-4db7-a92b-165c3bff6735
During handling of the above exception, another exception occurred:
DuplicatedKeysError Traceback (most recent call last)
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1541, in GeneratorBasedBuilder._prepare_split_single(self, arg)
[1540](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1539) num_shards = shard_id + 1
-> [1541](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1540) num_examples, num_bytes = writer.finalize()
[1542](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1541) writer.close()
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:563, in ArrowWriter.finalize(self, close_stream)
[562](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=561) if self._check_duplicates:
--> [563](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=562) self.check_duplicate_keys()
[564](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=563) # Re-intializing to empty list for next batch
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:492, in ArrowWriter.check_duplicate_keys(self)
[486](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=485) duplicate_key_indices = [
[487](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=486) str(self._num_examples + index)
[488](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=487) for index, (duplicate_hash, _) in enumerate(self.hkey_record)
[489](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=488) if duplicate_hash == hash
[490](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=489) ]
--> [492](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=491) raise DuplicatedKeysError(key, duplicate_key_indices)
[493](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=492) else:
DuplicatedKeysError: Found multiple examples generated with the same key
The examples at index 858, 859 have the key dlg-89174425-d57a-4db7-a92b-165c3bff6735
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[23], line 1
----> 1 dataset = load_dataset("taskmaster2", "music")
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py:1741, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
[1738](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1737) try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
[1740](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1739) # Download and prepare data
-> [1741](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1740) builder_instance.download_and_prepare(
[1742](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1741) download_config=download_config,
[1743](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1742) download_mode=download_mode,
[1744](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1743) ignore_verifications=ignore_verifications,
[1745](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1744) try_from_hf_gcs=try_from_hf_gcs,
[1746](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1745) use_auth_token=use_auth_token,
[1747](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1746) num_proc=num_proc,
[1748](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1747) )
[1750](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1749) # Build dataset for splits
[1751](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1750) keep_in_memory = (
[1752](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1751) keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
[1753](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1752) )
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:822, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
[820](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=819) if num_proc is not None:
[821](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=820) prepare_split_kwargs["num_proc"] = num_proc
--> [822](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=821) self._download_and_prepare(
[823](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=822) dl_manager=dl_manager,
[824](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=823) verify_infos=verify_infos,
[825](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=824) **prepare_split_kwargs,
[826](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=825) **download_and_prepare_kwargs,
[827](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=826) )
[828](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=827) # Sync info
[829](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=828) self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1555, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
[1554](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1553) def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
-> [1555](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1554) super()._download_and_prepare(
[1556](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1555) dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs
[1557](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1556) )
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:913, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
[909](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=908) split_dict.add(split_generator.split_info)
[911](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=910) try:
[912](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=911) # Prepare split will record examples associated to the split
--> [913](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=912) self._prepare_split(split_generator, **prepare_split_kwargs)
[914](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=913) except OSError as e:
[915](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=914) raise OSError(
[916](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=915) "Cannot find data file. "
[917](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=916) + (self.manual_download_instructions or "")
[918](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=917) + "\nOriginal error:\n"
[919](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=918) + str(e)
[920](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=919) ) from None
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1396, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
[1394](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1393) gen_kwargs = split_generator.gen_kwargs
[1395](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1394) job_id = 0
-> [1396](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1395) for job_id, done, content in self._prepare_split_single(
[1397](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1396) {"gen_kwargs": gen_kwargs, "job_id": job_id, **_prepare_split_args}
[1398](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1397) ):
[1399](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1398) if done:
[1400](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1399) result = content
File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1550, in GeneratorBasedBuilder._prepare_split_single(self, arg)
[1548](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1547) if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
[1549](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1548) e = e.__context__
-> [1550](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1549) raise DatasetGenerationError("An error occurred while generating the dataset") from e
[1552](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1551) yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
Loads the dataset
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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https://api.github.com/repos/huggingface/datasets/issues/5316 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5316/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5316/comments | https://api.github.com/repos/huggingface/datasets/issues/5316/events | https://github.com/huggingface/datasets/issues/5316 | 1,470,115,681 | I_kwDODunzps5XoC9h | 5,316 | Bug in sample_by="paragraph" | {
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"Thanks for reporting, @adampauls.\r\n\r\nWe are having a look at it. "
] | 2022-11-30T19:24:13Z | 2022-12-01T15:19:02Z | 2022-12-01T15:19:02Z | NONE | null | null | {
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} | ### Describe the bug
I think [this line](https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/text/text.py#L96) is wrong and should be `batch = f.read(self.config.chunksize)`. Otherwise it will never terminate because even when `f` is finished reading, `batch` will still be truthy from the last iteration.
### Steps to reproduce the bug
```
> cat test.txt
a b c
d e f
````
```python
>>> import datasets
>>> datasets.load_dataset("text", data_files={"train":"test.txt"}, sample_by="paragraph")
```
This will go on forever.
### Expected behavior
Terminates very quickly.
### Environment info
`version = "2.6.1"` but I think the bug is still there on main. | {
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https://api.github.com/repos/huggingface/datasets/issues/5314 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5314/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5314/comments | https://api.github.com/repos/huggingface/datasets/issues/5314/events | https://github.com/huggingface/datasets/issues/5314 | 1,469,685,118 | I_kwDODunzps5XmZ1- | 5,314 | Datasets: classification_report() got an unexpected keyword argument 'suffix' | {
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"This seems similar to https://github.com/huggingface/datasets/issues/2512 Can you try to update seqeval ? ",
"@JonathanAlis also note that the metrics are deprecated in our `datasets` library.\r\n\r\nPlease, use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate"
] | 2022-11-30T14:01:03Z | 2023-07-21T14:40:31Z | 2023-07-21T14:40:31Z | NONE | null | null | {
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} | https://github.com/huggingface/datasets/blob/main/metrics/seqeval/seqeval.py
> import datasets
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
seqeval = datasets.load_metric("seqeval")
results = seqeval.compute(predictions=predictions, references=references)
print(list(results.keys()))
print(results["overall_f1"])
print(results["PER"]["f1"])
It raises the error:
> TypeError: classification_report() got an unexpected keyword argument 'suffix'
For context, versions on my pip list -v
> datasets 1.12.1
seqeval 1.2.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5306 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5306/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5306/comments | https://api.github.com/repos/huggingface/datasets/issues/5306/events | https://github.com/huggingface/datasets/issues/5306 | 1,465,968,639 | I_kwDODunzps5XYOf_ | 5,306 | Can't use custom feature description when loading a dataset | {
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"Forgot to actually convert the feature dict to a Feature object. Closing."
] | 2022-11-28T07:55:44Z | 2022-11-28T08:11:45Z | 2022-11-28T08:11:44Z | MEMBER | null | null | {
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} | ### Describe the bug
I have created a feature dictionary to describe my datasets' column types, to use when loading the dataset, following [the doc](https://huggingface.co/docs/datasets/main/en/about_dataset_features). It crashes at dataset load.
### Steps to reproduce the bug
```python
# Creating features
task_list = [f"motif_G{i}" for i in range(19, 53)]
features = {t: Sequence(feature=Value(dtype="float64")) for t in task_list}
for col_name in ["class_label"]:
features[col_name] = Sequence(feature=Value(dtype="int64"))
for col_name in ["num_nodes"]:
features[col_name] = Value(dtype="int64")
for col_name in ["num_bridges", "num_cycles", "avg_shortest_path_len"]:
features[col_name] = Sequence(feature=Value(dtype="float64"))
for col_name in ["edge_attr", "node_feat", "edge_index"]:
features[col_name] = Sequence(feature=Sequence(feature=Value(dtype="int64")))
print(features)
dataset = load_dataset(path=f"graphs-datasets/unbalanced-motifs-500K", split="train", features=features)
```
Last line will crash and say 'TypeError: argument of type 'Sequence' is not iterable'.
Full stack:
```
Traceback (most recent call last):
File "pretrain_tokengt.py", line 131, in <module>
main(output_folder = "../workspace/pretraining",
File "pretrain_tokengt.py", line 52, in main
dataset = load_dataset(path=f"graphs-datasets/{dataset_name}", split="train", features=features)
File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1718, in load_dataset
builder_instance = load_dataset_builder(
File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1514, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "huggingface_env/lib/python3.8/site-packages/datasets/builder.py", line 321, in __init__
info.update(self._info())
File "huggingface_env/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 62, in _info
return datasets.DatasetInfo(features=self.config.features)
File "<string>", line 20, in __init__
File "huggingface_env/lib/python3.8/site-packages/datasets/info.py", line 155, in __post_init__
self.features = Features.from_dict(self.features)
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1599, in from_dict
obj = generate_from_dict(dic)
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1281, in generate_from_dict
if "_type" not in obj or isinstance(obj["_type"], dict):
TypeError: argument of type 'Sequence' is not iterable
```
### Expected behavior
For it not to crash.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | {
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"Thanks for reporting @JoelNiklaus.\r\n\r\nPlease note that since we moved all dataset loading scripts to the Hub, the issues and pull requests relative to specific datasets are directly handled on the Hub, in their Community tab. I'm transferring this issue there: https://huggingface.co/datasets/joelito/mc4_legal/... | 2022-11-28T00:16:16Z | 2022-11-28T07:22:42Z | 2022-11-28T07:22:42Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
The dataset https://huggingface.co/datasets/joelito/mc4_legal works for languages like bg with a single data file, but not for languages with multiple files like de. It shows zero rows for the de dataset.
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main) [1]> python test_mc4_legal.py (debug)
Found cached dataset mc4_legal (/Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/de/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f)
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 0
})
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main)> python test_mc4_legal.py (debug)
Downloading and preparing dataset mc4_legal/bg to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1240.55it/s]
Dataset mc4_legal downloaded and prepared to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f. Subsequent calls will reuse this data.
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 204
})
### Steps to reproduce the bug
import datasets
from datasets import load_dataset, get_dataset_config_names
language = "bg"
test = load_dataset("joelito/mc4_legal", language, split='train')
### Expected behavior
It should display the correct number of rows for the de dataset which should be a large number (thousands or more).
### Environment info
Package Version
------------------------ --------------
absl-py 1.3.0
aiohttp 3.8.1
aiosignal 1.2.0
astunparse 1.6.3
async-timeout 4.0.2
attrs 22.1.0
beautifulsoup4 4.11.1
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datasets 2.6.1
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"The [timit_asr.py](https://huggingface.co/datasets/timit_asr/blob/main/timit_asr.py) script iterates over the WAV files per split directory using this:\r\n```python\r\nwav_paths = sorted(Path(data_dir).glob(f\"**/{split}/**/*.wav\"))\r\nwav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f\"**/{spli... | 2022-11-26T10:18:22Z | 2023-02-10T16:33:21Z | 2023-02-10T16:33:21Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
When I use the function ```timit = load_dataset('timit_asr', data_dir=data_dir)```, it only loads train split, not test split.
I tried to change the directory and filename to lower case to upper case for the test split, but it does not work at all.
```python
DatasetDict({
train: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 4620
})
test: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 0
})
})
```
The directory structure of both splits are same. (DIALECT_REGION / SPEAKER_CODE / DATA_FILES)
### Steps to reproduce the bug
1. just use ```timit = load_dataset('timit_asr', data_dir=data_dir)```
### Expected behavior
```python
DatasetDict({
train: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 4620
})
test: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 1680
})
})
```
### Environment info
- ubuntu 20.04
- python 3.9.13
- datasets 2.7.1 | {
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From its implementation:
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def xopen(file: str, mode="r", *args, **kwargs):
file = _as_posix(PurePath(file))
main_hop, *rest_hops = file.split("::")
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On a Windows machine, if we pass the argument:
```python
xopen("C:\\Users\\USERNAME\\filename.txt")
```
it returns
```python
open("C:/Users/USERNAME/filename.txt")
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```python
from datasets.download.streaming_download_manager import xjoin
path = xjoin("C:\\Users\\USERNAME", "filename.txt")
```
Join path should be:
```python
"C:\\Users\\USERNAME\\filename.txt"
```
However it is:
```python
"C:/Users/USERNAME/filename.txt"
``` | {
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"Hi ! Thanks for reporting. Indeed the lock file should be placed in a directory with write permission (e.g. in the directory where the archive is extracted).",
"I opened https://github.com/huggingface/datasets/pull/5320 to fix this - it places the lock file in the cache directory instead of trying to put in next... | 2022-11-25T03:59:43Z | 2023-07-21T14:39:09Z | 2023-07-21T14:39:09Z | NONE | null | null | {
"completed": 0,
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"total": 0
} | ### Describe the bug
Hi,
`load_dataset()` does not work .zip files located on a read-only directory. Looks like it's because Dataset creates a lock file in the [same directory](https://github.com/huggingface/datasets/blob/df4bdd365f2abb695f113cbf8856a925bc70901b/src/datasets/utils/extract.py) as the .zip file.
Encountered this when attempting `load_dataset()` on a datadir with SageMaker FastFile mode.
### Steps to reproduce the bug
```python
# Showing relevant lines only.
hyperparameters = {
"dataset_name": "ydshieh/coco_dataset_script",
"dataset_config_name": 2017,
"data_dir": "/opt/ml/input/data/coco",
"cache_dir": "/tmp/huggingface-cache", # Fix dataset complains out-of-space.
...
}
estimator = PyTorch(
base_job_name="clip",
source_dir="../src/sm-entrypoint",
entry_point="run_clip.py", # Transformers/src/examples/pytorch/contrastive-image-text/run_clip.py
framework_version="1.12",
py_version="py38",
hyperparameters=hyperparameters,
instance_count=1,
instance_type="ml.p3.16xlarge",
volume_size=100,
distribution={"smdistributed": {"dataparallel": {"enabled": True}}},
)
fast_file = lambda x: TrainingInput(x, input_mode='FastFile')
estimator.fit(
{
"pre-trained": fast_file("s3://vm-sagemakerr-us-east-1/clip/pre-trained-checkpoint/"),
"coco": fast_file("s3://vm-sagemakerr-us-east-1/clip/coco-zip-files/"),
}
)
```
Error message:
```text
ErrorMessage "OSError: [Errno 30] Read-only file system: '/opt/ml/input/data/coco/image_info_test2017.zip.lock'
"""
The above exception was the direct cause of the following exception
Traceback (most recent call last)
File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/opt/conda/lib/python3.8/site-packages/mpi4py/__main__.py", line 7, in <module>
main()
File "/opt/conda/lib/python3.8/site-packages/mpi4py/run.py", line 198, in main
run_command_line(args)
File "/opt/conda/lib/python3.8/site-packages/mpi4py/run.py", line 47, in run_command_line
run_path(sys.argv[0], run_name='__main__')
File "/opt/conda/lib/python3.8/runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "/opt/conda/lib/python3.8/runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "run_clip_smddp.py", line 594, in <module>
File "run_clip_smddp.py", line 327, in main
dataset = load_dataset(
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare
super()._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 891, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/ydshieh--coco_dataset_script/e033205c0266a54c10be132f9264f2a39dcf893e798f6756d224b1ff5078998f/coco_dataset_script.py", line 123, in _split_generators
archive_path = dl_manager.download_and_extract(_DL_URLS)
File "/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py", line 447, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py", line 419, in extract
extracted_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 472, in map_nested
mapped = pool.map(_single_map_nested, split_kwds)
File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 364, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 771, in get
raise self._value
OSError: [Errno 30] Read-only file system: '/opt/ml/input/data/coco/image_info_test2017.zip.lock'"
```
### Expected behavior
`load_dataset()` to succeed, just like when .zip file is passed in SageMaker File mode.
### Environment info
* datasets-2.7.1
* transformers-4.24.0
* python-3.8
* torch-1.12
* SageMaker PyTorch DLC | {
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This feature will be useful e.g. for datasets containing text files and annotated files with the same name but different extension. | {
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"- Build docs for 2.6.2:\r\n - Commit: a6a5a1cf4cdf1e0be65168aed5a327f543001fe8\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539470622/jobs/5941404044\r\n- Build docs for 2.7.1:\r\n - Commit: 5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2\r\n - Build docs GH Action: https://github... | 2022-11-24T09:42:10Z | 2022-11-24T10:10:02Z | 2022-11-24T10:10:02Z | MEMBER | null | null | {
"completed": 0,
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} | After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered).
There was a fix by:
- #5291
However, both documentations were built from main branch, instead of their corresponding version branch.
We are rebuilding them. | {
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https://api.github.com/repos/huggingface/datasets/issues/5286 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5286/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5286/comments | https://api.github.com/repos/huggingface/datasets/issues/5286/events | https://github.com/huggingface/datasets/issues/5286 | 1,461,908,087 | I_kwDODunzps5XIvJ3 | 5,286 | FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json | {
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"I found a solution \r\n\r\nIf you specifically install datasets==1.18 and then run\r\n\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.en')\r\nthen this should work (it worked for me.)",
"I have the same problem here but installing datasets==1.18 wont work for me\r\n",
"This works wit... | 2022-11-23T14:54:15Z | 2024-11-23T01:16:41Z | 2022-11-25T11:33:14Z | NONE | null | null | {
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} | ### Describe the bug
I follow the steps provided on the website [https://huggingface.co/datasets/wikipedia](https://huggingface.co/datasets/wikipedia)
$ pip install apache_beam mwparserfromhell
>>> from datasets import load_dataset
>>> load_dataset("wikipedia", "20220301.en")
however this results in the following error:
raise MissingBeamOptions(
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
If I then prompt the system with:
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
the following error occurs:
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
Here is the exact code:
Python 3.10.6 (main, Nov 2 2022, 18:53:38) [GCC 11.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> load_dataset('wikipedia', '20220301.en')
Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████| 15.3k/15.3k [00:00<00:00, 22.2MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1879, in _download_and_prepare
raise MissingBeamOptions(
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████| 15.3k/15.3k [00:00<00:00, 18.8MB/s]
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1909, in _download_and_prepare
super()._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 891, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rorytol/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py", line 945, in _split_generators
downloaded_files = dl_manager.download_and_extract({"info": info_url})
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 447, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 311, in download
downloaded_path_or_paths = map_nested(
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 444, in map_nested
mapped = [
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 445, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 338, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 183, in cached_path
output_path = get_from_cache(
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 530, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
### Steps to reproduce the bug
$ pip install apache_beam mwparserfromhell
>>> from datasets import load_dataset
>>> load_dataset("wikipedia", "20220301.en")
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
### Expected behavior
Download the dataset
### Environment info
Running linux on a remote workstation operated through a macbook terminal
Python 3.10.6
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https://api.github.com/repos/huggingface/datasets/issues/5284 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5284/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5284/comments | https://api.github.com/repos/huggingface/datasets/issues/5284/events | https://github.com/huggingface/datasets/issues/5284 | 1,461,519,733 | I_kwDODunzps5XHQV1 | 5,284 | Features of IterableDataset set to None by remove column | {
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"Related to https://github.com/huggingface/datasets/issues/5245",
"#self-assign",
"Thanks @lhoestq and @alvarobartt!\r\n\r\nThis would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working t... | 2022-11-23T10:54:59Z | 2025-02-07T11:36:41Z | 2022-11-28T12:53:24Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# check original features
print("Original features: ", dataset.features.keys())
# define features to remove: we KEEP audio and text
COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id']
dataset = dataset.remove_columns(COLUMNS_TO_REMOVE)
# check processed features, uh-oh!
print("Processed features: ", dataset.features)
# streaming the first audio sample still works
print("First sample:", next(iter(ds)))
```
**Print Output:**
```
Original features: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])
Processed features: None
First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157, 0.0005188 , 0.00024414, ..., -0.00097656,
-0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"}
```
### Expected behavior
The features should be those **not** removed by the `remove_column` method, i.e. audio and text.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
(Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml)
cc @polinaeterna @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/5280 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5280/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5280/comments | https://api.github.com/repos/huggingface/datasets/issues/5280/events | https://github.com/huggingface/datasets/issues/5280 | 1,459,823,179 | I_kwDODunzps5XAyJL | 5,280 | Import error | {
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"Hi ! Can you \r\n```python\r\nimport platform\r\nprint(platform.python_version())\r\n```\r\nto see that it returns ?",
"Hi,\n\n3.8.13\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:37:02 PM\nTo: huggingfa... | 2022-11-22T12:56:43Z | 2022-12-15T19:57:40Z | 2022-12-15T19:57:40Z | NONE | null | null | {
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} | https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong) | {
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https://api.github.com/repos/huggingface/datasets/issues/5278 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5278/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5278/comments | https://api.github.com/repos/huggingface/datasets/issues/5278/events | https://github.com/huggingface/datasets/issues/5278 | 1,459,574,490 | I_kwDODunzps5W_1ba | 5,278 | load_dataset does not read jsonl metadata file properly | {
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"Can you try to remove \"drop_labels=false\" ? It may force the loader to infer the labels instead of reading the metadata",
"Hi, thanks for responding. I tried that, but it does not change anything.",
"Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4",
"Probably the issue, will ... | 2022-11-22T10:24:46Z | 2023-02-14T14:48:16Z | 2022-11-23T11:38:35Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features.
Below is code to reproduce my exact example/problem.
### Steps to reproduce the bug
```ruby
dataset_link="19Unu89Ih_kP6zsE7f9Mkw8dy3NwHopRF"
id = dataset_link
output = 'Godardv01.zip'
gdown.download(id=id, output=output, quiet=False)
ds = load_dataset("imagefolder", data_dir="/kaggle/working/Volumes/TOSHIBA/Godard_imgs/Volumes/TOSHIBA/Godard_imgs/Full/train", split="train", drop_labels=False)
print(ds)
```
### Expected behavior
I would expect that it returned "image" and "text" columns from the code above.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5276/comments | https://api.github.com/repos/huggingface/datasets/issues/5276/events | https://github.com/huggingface/datasets/issues/5276 | 1,459,363,442 | I_kwDODunzps5W_B5y | 5,276 | Bug in downloading common_voice data and snall chunk of it to one's own hub | {
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"Sounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ?",
"Well I just sharded the original commonVoice dataset and pushed a small chunk of it in a private rep\n\nWhat did go wrong?\n\nHolen Sie sich Outlook für iOS<https://aka.ms/o0ukef>\n________________________________... | 2022-11-22T08:17:53Z | 2023-07-21T14:33:10Z | 2023-07-21T14:33:10Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11 | {
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https://api.github.com/repos/huggingface/datasets/issues/5275 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5275/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5275/comments | https://api.github.com/repos/huggingface/datasets/issues/5275/events | https://github.com/huggingface/datasets/issues/5275 | 1,459,358,919 | I_kwDODunzps5W_AzH | 5,275 | YAML integer keys are not preserved Hub server-side | {
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"@huggingface/datasets if you agree, I can make the bulk edit on the Hub to fix integer keys into strings.",
"Ok for me, and we can merge (internal) https://github.com/huggingface/moon-landing/pull/4609",
"FYI there are still 2k+ weekly users on `datasets` 2.6.1 which doesn't support the string label format for... | 2022-11-22T08:14:47Z | 2023-01-26T10:52:35Z | 2023-01-26T10:40:21Z | MEMBER | null | null | {
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} | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets | {
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"@BradleyHsu",
"Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!",
"I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` a... | 2022-11-21T21:59:53Z | 2023-05-27T00:06:14Z | 2022-11-28T02:50:42Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing | {
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https://api.github.com/repos/huggingface/datasets/issues/5269 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5269/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5269/comments | https://api.github.com/repos/huggingface/datasets/issues/5269/events | https://github.com/huggingface/datasets/issues/5269 | 1,456,485,799 | I_kwDODunzps5W0DWn | 5,269 | Shell completions | {
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"I don't think we need completion on the datasets-cli, since we're mainly developing huggingface-cli",
"I see."
] | 2022-11-19T13:48:59Z | 2022-11-21T15:06:15Z | 2022-11-21T15:06:14Z | NONE | null | null | {
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} | ### Feature request
Like <https://github.com/huggingface/huggingface_hub/issues/1197>, datasets-cli maybe need it, too.
### Motivation
See above.
### Your contribution
Maybe. | {
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"I think `stream` could be misleading since the data is not being streamed from remote endpoints (one could think that's the case when they see `load_dataset` followed by `stream`). Hence, I prefer the second option.\r\n\r\nPS: When we resolve https://github.com/huggingface/datasets/issues/4542, we could add `as_tf... | 2022-11-18T14:54:40Z | 2023-02-01T16:36:03Z | 2023-02-01T16:36:03Z | MEMBER | null | null | {
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} | This is useful to leverage iterable datasets specific features like:
- fast approximate shuffling
- lazy map, filter etc.
Iterating over the resulting iterable dataset should be at least as fast at iterating over the map-style dataset.
Here are some ideas regarding the API:
```python
# 1.
# - consistency with load_dataset(..., streaming=True)
# - gives intuition that map/filter/etc. are done on-the-fly
ids = ds.stream()
# 2.
# - more explicit on the output type
# - but maybe sounds like a conversion tool rather than a step in a processing pipeline
ids = ds.as_iterable_dataset()
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https://api.github.com/repos/huggingface/datasets/issues/5264 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5264/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5264/comments | https://api.github.com/repos/huggingface/datasets/issues/5264/events | https://github.com/huggingface/datasets/issues/5264 | 1,455,252,906 | I_kwDODunzps5WvWWq | 5,264 | `datasets` can't read a Parquet file in Python 3.9.13 | {
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"Could you share the full stack trace please ?\r\n\r\n\r\nCan you also try running this code ? It can be useful to determine if the issue comes from `datasets` or `fsspec` (streaming) or `pyarrow` (parquet reading):\r\n```python\r\nds = load_dataset(\"parquet\", data_files=a_parquet_file_url, use_auth_token=True)\r... | 2022-11-18T14:44:01Z | 2023-05-07T09:52:59Z | 2022-11-22T11:18:08Z | NONE | null | null | {
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} | ### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
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} | This is useful to distribute the shards to training nodes.
This can be implemented in `save_to_disk` and can also leverage multiprocessing to speed up the process | {
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https://api.github.com/repos/huggingface/datasets/issues/5262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5262/comments | https://api.github.com/repos/huggingface/datasets/issues/5262/events | https://github.com/huggingface/datasets/issues/5262 | 1,455,171,100 | I_kwDODunzps5WvCYc | 5,262 | AttributeError: 'Value' object has no attribute 'names' | {
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"Hi ! It looks like your \"isDif\" column is a Sequence of Value(\"string\"), not a Sequence of ClassLabel.\r\n\r\nYou can convert your Value(\"string\") feature type to a ClassLabel feature type this way:\r\n```python\r\nfrom datasets import ClassLabel, Sequence\r\n\r\n# provide the label_names yourself\r\nlabel_n... | 2022-11-18T13:58:42Z | 2022-11-22T10:09:24Z | 2022-11-22T10:09:23Z | NONE | null | null | {
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} | Hello
I'm trying to build a model for custom token classification
I already followed the token classification course on huggingface
while adapting the code to my work, this message occures :
'Value' object has no attribute 'names'
Here's my code:
`raw_datasets`
generates
DatasetDict({
train: Dataset({
features: ['isDisf', 'pos', 'tokens', 'id'],
num_rows: 14
})
})
`raw_datasets["train"][3]["isDisf"]`
generates
['B_RM', 'I_RM', 'I_RM', 'B_RP', 'I_RP', 'O', 'O']
`dis_feature = raw_datasets["train"].features["isDisf"]
dis_feature`
generates
Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)
and
`label_names = dis_feature.feature.names
label_names`
generates
AttributeError Traceback (most recent call last)
[<ipython-input-28-972fd54a869a>](https://localhost:8080/#) in <module>
----> 1 label_names = dis_feature.feature.names
2 label_names
AttributeError: 'Value' object has
AttributeError: 'Value' object has no attribute 'names'
Thank you for your help | {
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https://api.github.com/repos/huggingface/datasets/issues/5259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5259/comments | https://api.github.com/repos/huggingface/datasets/issues/5259/events | https://github.com/huggingface/datasets/issues/5259 | 1,453,555,923 | I_kwDODunzps5Wo4DT | 5,259 | datasets 2.7 introduces sharding error | {
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"I notice a comment in the code says:\r\n`Having lists of different sizes makes sharding ambigious, raise an error in this case until we decide how to define sharding without ambiguity for users` \r\n \r\n ... which suggests this update was pushed knowing that it might break some things. But, it didn't seem to h... | 2022-11-17T15:36:52Z | 2022-12-24T01:44:02Z | 2022-11-18T12:52:05Z | NONE | null | null | {
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} | ### Describe the bug
dataset fails to load with runtime error
`RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.`
### Steps to reproduce the bug
With datasets[audio] 2.7 loaded, and logged into hugging face,
`data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)`
creates the error.
Full stack trace:
```---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-7-8cb9ca0f79f0>](https://localhost:8080/#) in <module>
----> 1 data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)
5 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1745 try_from_hf_gcs=try_from_hf_gcs,
1746 use_auth_token=use_auth_token,
-> 1747 num_proc=num_proc,
1748 )
1749
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
824 verify_infos=verify_infos,
825 **prepare_split_kwargs,
--> 826 **download_and_prepare_kwargs,
827 )
828 # Sync info
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1554 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
1555 super()._download_and_prepare(
-> 1556 dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs
1557 )
1558
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
911 try:
912 # Prepare split will record examples associated to the split
--> 913 self._prepare_split(split_generator, **prepare_split_kwargs)
914 except OSError as e:
915 raise OSError(
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1362 fpath = path_join(self._output_dir, fname)
1363
-> 1364 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1365 if num_input_shards <= 1 and num_proc is not None:
1366 logger.warning(
[/usr/local/lib/python3.7/dist-packages/datasets/utils/sharding.py](https://localhost:8080/#) in _number_of_shards_in_gen_kwargs(gen_kwargs)
16 + "\n".join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items())
17 + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
---> 18 + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
19 )
20 )
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.```
### Expected behavior
the dataset loads in datasets version 2.6.1 and should load with datasets 2.7
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | {
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"The bulk edit is running...\r\n\r\nSee for example: \r\n- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2\r\n- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1",
"TODO: Add \"dataset_info\" YAML metadata to:\r\n- [x] \"chr_en\" has no metadata JSON fil... | 2022-11-17T15:13:15Z | 2023-02-16T09:49:05Z | 2022-11-19T06:51:37Z | MEMBER | null | null | {
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- https://huggingface.co/datasets/bc2gm_corpus/commit/2384629484401ecf4bb77cd808816719c424e57c
Note that this order is the one appearing in the preview of the datasets.
I'm making a bulk edit to align the order of the splits appearing in the metadata info with the order appearing in the loading script.
Related to:
- #5202 | {
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"Also cc @mariosasko and @lhoestq ",
"Cool ! Let us know if you have questions or if we can help :)\r\n\r\nI guess we'll also have to create the NYU CS Department on the Hub ?",
"> I guess we'll also have to create the NYU CS Department on the Hub ?\r\n\r\nYes, you're right! Let me add it to my profile first, a... | 2022-11-17T03:22:22Z | 2022-12-17T12:20:38Z | 2022-12-17T12:20:37Z | MEMBER | null | null | {
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} | ### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it. | {
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https://api.github.com/repos/huggingface/datasets/issues/5251 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5251/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5251/comments | https://api.github.com/repos/huggingface/datasets/issues/5251/events | https://github.com/huggingface/datasets/issues/5251 | 1,451,761,321 | I_kwDODunzps5WiB6p | 5,251 | Docs are not generated after latest release | {
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"After a discussion with @mishig25:\r\n- He said that this action should be triggered if we call our release branch according to the regex `v*-release`, as transformers does\r\n- I said that our procedure is different: our release branch is *temporary* and it is deleted just after the release PR is merged to main\r... | 2022-11-16T14:59:31Z | 2022-11-22T16:27:50Z | 2022-11-22T16:27:50Z | MEMBER | null | null | {
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} | After the latest `datasets` release version 0.7.0, the docs were not generated.
As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:
https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8
Related to:
- #5250
CC: @mishig25 | {
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"It seems all the tasks have been addressed, meaning this issue can be closed, no?"
] | 2022-11-16T14:19:03Z | 2023-12-21T10:28:27Z | 2023-12-21T10:28:26Z | MEMBER | null | null | {
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} | We have decided to implement a protection mechanism in this repository, so that nobody (not even administrators) can inadvertently push accidentally directly to the main branch.
See context here:
- d7c942228b8dcf4de64b00a3053dce59b335f618
To do:
- [x] Protect main branch
- Settings > Branches > Branch protection rules > main > Edit
- [x] Check: Do not allow bypassing the above settings
- The above settings will apply to administrators and custom roles with the "bypass branch protections" permission.
- [x] Additionally, uncheck: Require approvals [under "Require a pull request before merging", which was already checked]
- Before, we could exceptionally merge a non-approved PR, using Administrator bypass
- Now that Administrator bypass is no longer possible, we would always need an approval to be able to merge; and pull request authors cannot approve their own pull requests. This could be an inconvenient in some exceptional circumstances when an urgent fix is needed
- Nevertheless, although it is no longer enforced, it is strongly recommended to merge PRs only if they have at least one approval
- [x] #5250
- So that direct pushes to main branch are no longer necessary | {
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https://api.github.com/repos/huggingface/datasets/issues/5245 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5245/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5245/comments | https://api.github.com/repos/huggingface/datasets/issues/5245/events | https://github.com/huggingface/datasets/issues/5245 | 1,450,376,433 | I_kwDODunzps5Wcvzx | 5,245 | Unable to rename columns in streaming dataset | {
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"Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!",
"Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.\r\n\r\nWe also trie... | 2022-11-15T21:04:41Z | 2022-11-28T12:53:24Z | 2022-11-28T12:53:24Z | NONE | null | null | {
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} | ### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1 | {
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"Thanks for reporting, @vinaykakade.\r\n\r\nWe are discussing about making a release early this week.\r\n\r\nPlease note that in the meantime, in your specific case (as we also pointed out here: https://github.com/huggingface/datasets/issues/5162#issuecomment-1291720293), you can circumvent the issue by pinning `mu... | 2022-11-12T06:46:23Z | 2022-11-14T08:24:43Z | 2022-11-14T08:07:59Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
datasets version 2.6.1 has a dependency on dill<0.3.6. This causes a conflict with dill>=0.3.6 used by multiprocess dependency in datasets 2.6.1
This issue is already fixed in https://github.com/huggingface/datasets/pull/5166/files, but not yet been released. Please release a new version of the datasets library to fix this.
### Steps to reproduce the bug
1. Create requirements.in with only dependency being datasets (or datasets[s3])
2. Run pip-compile
3. The output is as follows:
```
Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6
Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1
There are incompatible versions in the resolved dependencies:
dill<0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
dill>=0.3.6 (from multiprocess==0.70.14->datasets[s3]==2.6.1->-r requirements.in (line 1))
```
### Expected behavior
pip-compile produces requirements.txt without any conflicts
### Environment info
datasets version 2.6.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5231 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5231/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5231/comments | https://api.github.com/repos/huggingface/datasets/issues/5231/events | https://github.com/huggingface/datasets/issues/5231 | 1,445,883,267 | I_kwDODunzps5WLm2D | 5,231 | Using `set_format(type='torch', columns=columns)` makes Array2D/3D columns stop formatting correctly | {
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"In case others find this, the problem was not with set_format, but my usages of `to_pandas()` and `from_pandas()` which I was using during dataset splitting; somewhere in the chain of converting to and from pandas the `Array2D/Array3D` types get converted to series of `Sequence()` types"
] | 2022-11-11T18:54:36Z | 2022-11-11T20:42:29Z | 2022-11-11T18:59:50Z | NONE | null | null | {
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} | I have a Dataset with two Features defined as follows:
```
'image': Array3D(dtype="int64", shape=(3, 224, 224)),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
```
On said dataset, if I `dataset.set_format(type='torch')` and then use the dataset in a dataloader, these columns are correctly cast to Tensors of (batch_size, 3, 224, 244) for example.
However, if I `dataset.set_format(type='torch', columns=['image', 'bbox'])` these columns are cast to Lists of tensors and miss the batch size completely (the 3 dimension is the list length).
I'm currently digging through datasets formatting code to try and find out why, but was curious if someone knew an immediate solution for this. | {
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https://api.github.com/repos/huggingface/datasets/issues/5230 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5230/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5230/comments | https://api.github.com/repos/huggingface/datasets/issues/5230/events | https://github.com/huggingface/datasets/issues/5230 | 1,445,507,580 | I_kwDODunzps5WKLH8 | 5,230 | dataclasses error when importing the library in python 3.11 | {
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"I opened [this issue](https://github.com/python/cpython/issues/99401).\r\nPython's maintainers say that the issue is caused by [this change](https://docs.python.org/3.11/whatsnew/3.11.html#dataclasses).\r\nI believe adding a `__hash__` method to `datasets.utils.version.Version` should solve (at least partially) th... | 2022-11-11T13:53:49Z | 2023-05-25T04:37:05Z | 2022-11-14T15:27:37Z | NONE | null | null | {
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} | ### Describe the bug
When I import datasets using python 3.11 the dataclasses standard library raises the following error:
`ValueError: mutable default <class 'datasets.utils.version.Version'> for field version is not allowed: use default_factory`
When I tried to import the library using the following jupyter notebook:
```
%%bash
# create python 3.11 conda env
conda create --yes --quiet -n myenv -c conda-forge python=3.11
# activate is
source activate myenv
# install pyarrow
/opt/conda/envs/myenv/bin/python -m pip install --quiet --extra-index-url https://pypi.fury.io/arrow-nightlies/ \
--prefer-binary --pre pyarrow
# install datasets
/opt/conda/envs/myenv/bin/python -m pip install --quiet datasets
```
```
# create a python file that only imports datasets
with open("import_datasets.py", 'w') as f:
f.write("import datasets")
# run it with the env
!/opt/conda/envs/myenv/bin/python import_datasets.py
```
I get the following error:
```
Traceback (most recent call last):
File "/kaggle/working/import_datasets.py", line 1, in <module>
import datasets
File "/opt/conda/envs/myenv/lib/python3.11/site-packages/datasets/__init__.py", line 45, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/opt/conda/envs/myenv/lib/python3.11/site-packages/datasets/builder.py", line 91, in <module>
@dataclass
^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 1221, in dataclass
return wrap(cls)
^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 1211, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 959, in _process_class
cls_fields.append(_get_field(cls, name, type, kw_only))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 816, in _get_field
raise ValueError(f'mutable default {type(f.default)} for field '
ValueError: mutable default <class 'datasets.utils.version.Version'> for field version is not allowed: use default_factory
```
This is probably due to one of the following changes in the [dataclasses standard library](https://docs.python.org/3/library/dataclasses.html) in version 3.11:
1. Changed in version 3.11: Instead of looking for and disallowing objects of type list, dict, or set, unhashable objects are now not allowed as default values. Unhashability is used to approximate mutability.
2. fields may optionally specify a default value, using normal Python syntax:
```
@dataclass
class C:
a: int # 'a' has no default value
b: int = 0 # assign a default value for 'b'
In this example, both a and b will be included in the added __init__() method, which will be defined as:
def __init__(self, a: int, b: int = 0):
```
3. Changed in version 3.11: If a field name is already included in the __slots__ of a base class, it will not be included in the generated __slots__ to prevent [overriding them](https://docs.python.org/3/reference/datamodel.html#datamodel-note-slots). Therefore, do not use __slots__ to retrieve the field names of a dataclass. Use [fields()](https://docs.python.org/3/library/dataclasses.html#dataclasses.fields) instead. To be able to determine inherited slots, base class __slots__ may be any iterable, but not an iterator.
4. weakref_slot: If true (the default is False), add a slot named “__weakref__”, which is required to make an instance weakref-able. It is an error to specify weakref_slot=True without also specifying slots=True.
[TypeError](https://docs.python.org/3/library/exceptions.html#TypeError) will be raised if a field without a default value follows a field with a default value. This is true whether this occurs in a single class, or as a result of class inheritance.
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. go to [the notebook in kaggle](https://www.kaggle.com/yonikremer/repreducing-issue)
2. rub both of the cells
### Expected behavior
I'm expecting no issues.
This error should not occur.
### Environment info
kaggle kernels, with default settings:
pin to original environment, no accelerator. | {
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https://api.github.com/repos/huggingface/datasets/issues/5229 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5229/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5229/comments | https://api.github.com/repos/huggingface/datasets/issues/5229/events | https://github.com/huggingface/datasets/issues/5229 | 1,445,121,028 | I_kwDODunzps5WIswE | 5,229 | Type error when calling `map` over dataset containing 0-d tensors | {
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"Hi! \r\n\r\nWe could address this by calling `.item()` on such tensors to extract the value, but this would lose us the type, which could lead to storing the generated dataset in a suboptimal format. Considering this, I think the only proper fix would be implementing support for 0-D tensors on Apache Arrow's side ... | 2022-11-11T08:27:28Z | 2023-01-13T16:00:53Z | 2023-01-13T16:00:53Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
0-dimensional tensors in a dataset lead to `TypeError: iteration over a 0-d array` when calling `map`. It is easy to generate such tensors by using `.with_format("...")` on the whole dataset.
### Steps to reproduce the bug
```
ds = datasets.Dataset.from_list([{"a": 1}, {"a": 1}]).with_format("torch")
ds.map(None)
```
### Expected behavior
Getting back `ds` without errors.
### Environment info
Python 3.10.8
datasets 2.6.
torch 1.13.0 | {
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"Fixed. Please close.",
"how to fix?i need your help"
] | 2022-11-10T21:57:06Z | 2023-10-07T05:04:41Z | 2022-11-10T22:05:43Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
From these lines:
from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")
I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files
And yet the 'wikisql' is reported to exist via the list_datasets().
Any help appreciated.
### Steps to reproduce the bug
From these lines:
from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")
I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files
And yet the 'wikisql' is reported to exist via the list_datasets().
Any help appreciated.
### Expected behavior
Dataset should load. This same code used to work.
### Environment info
Mac OS | {
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"Hi ! Datasets are memory mapped from your disk, i.e. they're not loaded in RAM. This is possible thanks to the Arrow data format.\r\n\r\nTherefore the column you remove is not in RAM, so removing it doesn't cause the RAM to decrease.",
"Thanks for the explanation! @lhoestq \r\nI wonder since it is memory mapped,... | 2022-11-10T18:35:27Z | 2022-11-29T15:10:10Z | 2022-11-29T15:10:10Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
How do I release memory when I use methods like `.remove_columns()` or `clear()` in notebooks?
```python
from datasets import load_dataset
common_voice = load_dataset("mozilla-foundation/common_voice_11_0", "ja", use_auth_token=True)
# check memory -> RAM Used (GB): 0.704 / Total (GB) 33.670
common_voice = common_voice.remove_columns(column_names=common_voice.column_names['train'])
common_voice.clear()
# check memory -> RAM Used (GB): 0.705 / Total (GB) 33.670
```
I tried `gc.collect()` but did not help
### Steps to reproduce the bug
1. load dataset
2. remove all the columns
3. check memory is reduced or not
[link to reproduce](https://www.kaggle.com/code/bayartsogtya/huggingface-dataset-memory-issue/notebook?scriptVersionId=110630567)
### Expected behavior
Memory released when I remove the column
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
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"I just tried to do the same but changing the `.wav` files to `.mp3` files and that doesn't fix it.",
"I don't know if anyone will ever read this but I've tried to upload the same dataset with google colab and the output seems more clarifying. I didn't specify the train/test split so the dataset wasn't fully uplo... | 2022-11-10T10:29:31Z | 2023-04-25T09:54:05Z | 2022-11-22T11:24:19Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I'm following the instructions in [https://huggingface.co/docs/datasets/audio_load#audiofolder-with-metadata](url) to be able to load a dataset from a local folder.
I have everything into a folder, into a train folder and then the audios and csv. When I try to load the dataset and run from terminal, seems to work but then freezes with no apparent reason.
The metadata.csv file contains a few columns but the important ones, `file_name` with the filename and `transcription` with the transcription are okay.
The audios are `.wav` files, I don't know if that might be the problem (I will proceed to try to change them all to `.mp3` and try again).
### Steps to reproduce the bug
The code I'm using:
```python
from datasets import load_dataset
dataset = load_dataset("audiofolder", data_dir="../archive/Dataset")
dataset[0]["audio"]
```
The output I obtain:
```
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 311135.43it/s]
Using custom data configuration default-38d4546ffd010f3e
Downloading and preparing dataset audiofolder/default to /Users/mine/.cache/huggingface/datasets/audiofolder/default-38d4546ffd010f3e/0.0.0/6cbdd16f8688354c63b4e2a36e1585d05de285023ee6443ffd71c4182055c0fc...
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 166467.72it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 187772.74it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 59623.71it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 138090.55it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 106065.64it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 56036.38it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 74004.24it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 162343.45it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 101881.23it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 60145.67it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 80890.02it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 54036.67it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 95851.09it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 155897.00it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 137656.96it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 131230.81it/s]
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
```
And then here it just freezes and nothing more happens.
### Expected behavior
Load the dataset.
### Environment info
Datasets version:
datasets 2.6.1 pypi_0 pypi
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"cc @McPatate maybe you know what's happening ?",
"Yes I think I know what is happening. We check in zips for pickles, and the UI must display the pickle jar when a scan has an associated list of imports, even when empty.\r\n~I'll fix ASAP !~",
"> I'll fix ASAP !\r\n\r\nActually I'd rather leave it like that f... | 2022-11-09T16:41:16Z | 2022-11-09T18:10:46Z | 2022-11-09T18:06:57Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
HuggingFace is incorrectly reporting that my datasets are pickled. They are not picked, they are simple ZIP files containing PNG images.
Hopefully this is the right location to report this bug.
### Steps to reproduce the bug
Inspect my dataset respository here: https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images
### Expected behavior
They should not be reported as being pickled.
### Environment info
N/A | {
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"Did you run `huggingface-cli lfs-enable-largefiles` before committing or before adding ? Maybe you can try before adding\r\n\r\nAnyway I'd encourage you to split your data into several TAR archives if possible, this way the dataset can loaded faster using multiprocessing (by giving each process a subset of shards ... | 2022-11-09T15:32:05Z | 2022-11-10T18:11:21Z | 2022-11-10T18:11:11Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I am facing the issue when I try to push the tar.gz file around 11G to HUB.
```
(venv) ╭─laptop@laptop ~/PersonalProjects/data/ulaanbal_v0 ‹main●›
╰─$ du -sh *
4.0K README.md
13G data
516K test.jsonl
18M train.jsonl
4.0K ulaanbal_v0.py
11G ulaanbal_v0.tar.gz
452K validation.jsonl
(venv) ╭─laptop@laptop~/PersonalProjects/data/ulaanbal_v0 ‹main●›
╰─$ git add ulaanbal_v0.tar.gz && git commit -m 'large version'
(venv) ╭─laptop@laptop ~/PersonalProjects/data/ulaanbal_v0 ‹main●›
╰─$ git push
EOFoading LFS objects: 0% (0/1), 0 B | 0 B/s
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
error: failed to push some refs to 'https://huggingface.co/datasets/bayartsogt/ulaanbal_v0'
```
I have already tried pushing a small version of this and it was working fine. So my guess it is probably because of the big file.
Following I run before the commit:
```
╰─$ git lfs install
╰─$ huggingface-cli lfs-enable-largefiles .
```
### Steps to reproduce the bug
Create a private dataset on huggingface and push 12G tar.gz file
### Expected behavior
To be pushed with no issue
### Environment info
- `datasets` version: 2.6.1
- Platform: Darwin-21.6.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 10.0.0
- Pandas version: 1.3.5
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https://api.github.com/repos/huggingface/datasets/issues/5220 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5220/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5220/comments | https://api.github.com/repos/huggingface/datasets/issues/5220/events | https://github.com/huggingface/datasets/issues/5220 | 1,441,664,377 | I_kwDODunzps5V7g15 | 5,220 | Implicit type conversion of lists in to_pandas | {
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"I think this behavior comes from PyArrow:\r\n```python\r\nimport pyarrow as pa\r\nt = pa.table({\"a\": [[0]]})\r\nt.to_pandas().a.values[0]\r\n# array([0])\r\n```\r\n\r\nI believe this has to do with zero-copy: you can get a pandas DataFrame without copying the buffers from arrow, and therefore end up with numpy a... | 2022-11-09T08:40:18Z | 2022-11-10T16:12:26Z | 2022-11-10T16:12:26Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ### Describe the bug
```
ds = Dataset.from_list([{'a':[1,2,3]}])
ds.to_pandas().a.values[0]
```
Results in `array([1, 2, 3])` -- a rather unexpected conversion of types which made downstream tools expecting lists not happy.
### Steps to reproduce the bug
See snippet
### Expected behavior
Keep the original type
### Environment info
datasets 2.6.1
python 3.8.10 | {
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https://api.github.com/repos/huggingface/datasets/issues/5218 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5218/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5218/comments | https://api.github.com/repos/huggingface/datasets/issues/5218/events | https://github.com/huggingface/datasets/issues/5218 | 1,441,254,194 | I_kwDODunzps5V58sy | 5,218 | Delta Tables usage using Datasets Library | {
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"completed": 0,
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} | ### Feature request
Adding compatibility of Datasets library with Delta Format. Elevating the utilities of Datasets library from Machine Learning Scope to Data Engineering Scope as well.
### Motivation
We know datasets library can absorb csv, json, parquet, etc. file formats but it would be great if Datasets library could work with Delta Tables (with delta format) as it has different features such as time travelling, layout optimization, query performance, aids in Data Engineering.
This will help and enhance Datasets library from Machine Learning utility to Data Engineering utilities and expand horizons thereafter. I am totally using Datasets library in all my usecases and as my role expands so does the work, compatibility with Datasets library is something I don't want to lose.
### Your contribution
Would love to work on this feature, even if this has to picked up from scratch, including design paradigms and patterns.
I have basic idea about Delta Live Tables, would brush it easily for this feature. | {
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https://api.github.com/repos/huggingface/datasets/issues/5209 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5209/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5209/comments | https://api.github.com/repos/huggingface/datasets/issues/5209/events | https://github.com/huggingface/datasets/issues/5209 | 1,438,367,678 | I_kwDODunzps5Vu7-- | 5,209 | Implement ability to define splits in metadata section of dataset card | {
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"@merveenoyan Do you want different files to be splits or configurations?\r\n\r\nFrom [what you specified in `Readme.md`](https://huggingface.co/datasets/inria-soda/tabular-benchmark/commit/fb4575853772c62a20203bdd6cc0202f5db4ce4e) I hypothesize that you want to have 4 **configs** corresponding to directories: `\"c... | 2022-11-07T13:27:16Z | 2023-07-21T14:36:02Z | 2023-07-21T14:36:01Z | CONTRIBUTOR | null | null | {
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} | ### Feature request
If you go here: https://huggingface.co/datasets/inria-soda/tabular-benchmark/tree/main you will see bunch of folders that has various CSV files. I’d like dataset viewer to show these files instead of only one dataset like it currently does. (and also people to be able to load them as splits instead of loading through `data_files`)
e.g GLUE has various splits on viewer but it’s too overkill to ask people to implement loading script, so it would be better to let them define these in the README file instead.
Also pinging @polinaeterna @lhoestq @adrinjalali
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"Actually upon closer inspection, it is documented in the code that this behavior is intentional, so I'll close this."
] | 2022-11-05T23:48:02Z | 2022-11-06T00:06:00Z | 2022-11-06T00:05:59Z | NONE | null | null | {
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} | ### Describe the bug
Some logs ([here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L778), [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L786), and [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L830)) generated by the `DatasetBuilder` are printed to the console instead of passed to `datasets` logger.
### Steps to reproduce the bug
```python
>> import datasets
>> datasets.load_dataset("some-dataset")
Downloading and preparing dataset csv/data to <path>...
Downloading data files: 100%|██████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 7729.06it/s]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 527.23it/s]
Dataset csv downloaded and prepared to <path>. Subsequent calls will reuse this data.
```
### Expected behavior
The logs should not be printed to the console directly but passed to the logger so that the user can redirect them wherever he wants.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-13.0-x86_64-i386-64bit
- Python version: 3.9.15
- PyArrow version: 10.0.0
- Pandas version: 1.5.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5204 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5204/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5204/comments | https://api.github.com/repos/huggingface/datasets/issues/5204/events | https://github.com/huggingface/datasets/issues/5204 | 1,437,221,259 | I_kwDODunzps5VqkGL | 5,204 | `push_to_hub` not propagating `token` through `DownloadConfig` | {
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"#self-assign",
"@lhoestq can you close this issue as part of the recent #5205 merge? Thanks 🤗 ",
"Thank you :)"
] | 2022-11-05T23:32:20Z | 2022-11-08T10:12:09Z | 2022-11-08T10:12:08Z | MEMBER | null | null | {
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} | ### Describe the bug
When trying to upload a new 🤗 Dataset to the Hub via Python, and providing the `token` as a parameter to the `Dataset.push_to_hub` function, it just works for the first time, assuming that the dataset didn't exist before.
But when trying to run `Dataset.push_to_hub` again over the same dataset, instead of updating it, it throws a `ConnectionError` when trying to retrieve the `README.md` that may contain some metadata about the dataset, so as to also update it, but since the `token` is not propagated, the `DownloadConfig` provided to the `datasets.utils.file_utils.get_from_cache` function doesn't contain the `use_auth_token` value set to `token`, it's just using the default one which is None/False.
So on, when uploading a dataset via Python with `push_to_hub` with the `token` as a parameter with the HuggingFace API Token as value, it can just be uploaded when the dataset is new, otherwise it fails with to `ConnectionError` due to the `token` not being propagated as `use_auth_token`.
### Steps to reproduce the bug
Let's create a new dataset in our HF account via Python as:
```python
from datasets import Dataset
data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
```
When we create the `Dataset` for the first time it works and there are no issues, but when trying to actually upload a new version of the same dataset (same name under the same username), we encounter the following issue:
```python
from datasets import Dataset
data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
>>> ConnectionError: Couldn't reach https://huggingface.co/datasets/alvarobartt/demo/resolve/main/README.md (ConnectionError('Unauthorized for URL https://huggingface.co/datasets/<HF_USERNAME>/<HF_DATASET>/resolve/main/README.md. Please use the parameter `use_auth_token=True` after logging in with `huggingface-cli login`'))
```
### Expected behavior
Ideally, the `token` parameter provided to `push_to_hub` should be propagated and used to download the `README.md` when trying to update a `Dataset`, instead of throwing that exception, so that the authentication can be done directly through code without running `huggingface-cli login`as mentioned at https://huggingface.co/docs/datasets/upload_dataset#upload-with-python.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-13.0-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.0
- Pandas version: 1.5.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5202 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5202/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5202/comments | https://api.github.com/repos/huggingface/datasets/issues/5202/events | https://github.com/huggingface/datasets/issues/5202 | 1,435,886,090 | I_kwDODunzps5VleIK | 5,202 | CI fails after bulk edit of canonical datasets | {
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"Fixed by: https://huggingface.co/datasets/paws/discussions/1"
] | 2022-11-04T10:51:20Z | 2023-02-16T09:11:10Z | 2023-02-16T09:11:10Z | MEMBER | null | null | {
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} | ```
______ test_get_dataset_config_info[paws-labeled_final-expected_splits2] _______
[gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
path = 'paws', config_name = 'labeled_final'
expected_splits = ['train', 'test', 'validation']
@pytest.mark.parametrize(
"path, config_name, expected_splits",
[
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
],
)
def test_get_dataset_config_info(path, config_name, expected_splits):
info = get_dataset_config_info(path, config_name=config_name)
assert info.config_name == config_name
> assert list(info.splits.keys()) == expected_splits
E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation']
E At index 0 diff: 'test' != 'train'
E Full diff:
E - ['train', 'test', 'validation']
E + ['test', 'train', 'validation']
tests/test_inspect.py:45: AssertionError
_ test_get_dataset_info[paws-expected_configs2-expected_splits_in_first_config2] _
[gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
path = 'paws'
expected_configs = ['labeled_final', 'labeled_swap', 'unlabeled_final']
expected_splits_in_first_config = ['train', 'test', 'validation']
@pytest.mark.parametrize(
"path, expected_configs, expected_splits_in_first_config",
[
("squad", ["plain_text"], ["train", "validation"]),
("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
],
)
def test_get_dataset_info(path, expected_configs, expected_splits_in_first_config):
infos = get_dataset_infos(path)
assert list(infos.keys()) == expected_configs
expected_config = expected_configs[0]
assert expected_config in infos
info = infos[expected_config]
assert info.config_name == expected_config
> assert list(info.splits.keys()) == expected_splits_in_first_config
E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation']
E At index 0 diff: 'test' != 'train'
E Full diff:
E - ['train', 'test', 'validation']
E + ['test', 'train', 'validation']
tests/test_inspect.py:90: AssertionError
______ test_get_dataset_split_names[paws-labeled_final-expected_splits2] _______
[gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
path = 'paws', expected_config = 'labeled_final'
expected_splits = ['train', 'test', 'validation']
@pytest.mark.parametrize(
"path, expected_config, expected_splits",
[
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
],
)
def test_get_dataset_split_names(path, expected_config, expected_splits):
infos = get_dataset_infos(path)
assert expected_config in infos
info = infos[expected_config]
assert info.config_name == expected_config
> assert list(info.splits.keys()) == expected_splits
E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation']
E At index 0 diff: 'test' != 'train'
E Full diff:
E - ['train', 'test', 'validation']
E + ['test', 'train', 'validation']
``` | {
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"Thanks for catching this, I can go through the docs and replace the links to their corresponding datasets on the Hub!"
] | 2022-11-04T10:06:21Z | 2022-11-07T18:40:20Z | 2022-11-07T18:40:20Z | CONTRIBUTOR | null | null | {
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} | As we don't have canonical datasets in the github repo anymore, some old links to them doesn't work. I don't know how many of them are there, I found link to SuperGlue here: https://huggingface.co/docs/datasets/dataset_script#multiple-configurations, probably there are more of them. These links should be replaced by links to the corresponding datasets on the Hub. | {
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https://api.github.com/repos/huggingface/datasets/issues/5193 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5193/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5193/comments | https://api.github.com/repos/huggingface/datasets/issues/5193/events | https://github.com/huggingface/datasets/issues/5193 | 1,433,883,780 | I_kwDODunzps5Vd1SE | 5,193 | "One or several metadata. were found, but not in the same directory or in a parent directory" | {
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"Also unrelated but still: https://huggingface.co/docs/datasets/image_dataset#generate-the-dataset\r\n```If your loading script passed the test, you should now have a dataset_infos.json file in your dataset folder.```\r\nIt's not the case anymore as it's now in the readme.md, it was confusing to me",
"And here is... | 2022-11-02T22:46:25Z | 2022-11-03T13:39:16Z | 2022-11-03T13:35:44Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
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} | ### Describe the bug
When loading my own dataset, on loading it I get an error.
Here is my dataset link: https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data
And the error after loading with:
```python
from datasets import load_dataset
load_dataset("corentinm7/MyoQuant-SDH-Data")
```
```python
Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.34k/3.34k [00:00<00:00, 4.45MB/s]
Using custom data configuration SDH_16k-53e7301a92ab0025
Downloading and preparing dataset None/SDH_16k to /home/corentin/.cache/huggingface/datasets/corentinm7___imagefolder/SDH_16k-53e7301a92ab0025/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f...
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.28M/3.28M [00:00<00:00, 4.31MB/s]
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.75s/it]
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.13G/1.13G [00:15<00:00, 74.3MB/s]
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:16<00:00, 16.09s/it]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:13<00:00, 13.16s/it]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/load.py", line 1742, in load_dataset
builder_instance.download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 814, in download_and_prepare
self._download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1423, in _download_and_prepare
super()._download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 905, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1374, in _prepare_split
for key, record in logging.tqdm(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 394, in _generate_examples
raise ValueError(
ValueError: One or several metadata. were found, but not in the same directory or in a parent directory of /home/corentin/.cache/huggingface/datasets/downloads/extracted/60c4aa8d4da3065bb3d310de4373dffd73bd4dc331aedcb4ee867febe4fdb7cd/validation/sick/2_CG_SDH_TAM_Bin1cKO_ko_pla_4_1640.tif.
```
However the test command is working fine. ```datasets-cli test hugging_face_play/ds_test/SDH_16k.py --save_info --all_configs --force_redownload```
```
Using custom data configuration SDH_16k
Testing builder 'SDH_16k' (1/1)
Downloading and preparing dataset sdh_16k/SDH_16k to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d...
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.13G/1.13G [00:14<00:00, 76.5MB/s]
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:15<00:00, 15.66s/it]
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.28M/3.28M [00:02<00:00, 1.44MB/s]
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00, 3.21s/it]
Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 11586.48it/s]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:13<00:00, 13.42s/it]
Dataset sdh_16k downloaded and prepared to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d. Subsequent calls will reuse this data.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 605.27it/s]
Dataset card saved at hugging_face_play/ds_test/README.md
Test successful.
```
### Steps to reproduce the bug
Simply run on python
```python
from datasets import load_dataset
load_dataset("corentinm7/MyoQuant-SDH-Data")
```
### Expected behavior
As the test command worked, this error should not appear
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.6
- PyArrow version: 10.0.0
- Pandas version: 1.5.1
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https://api.github.com/repos/huggingface/datasets/issues/5190 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5190/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5190/comments | https://api.github.com/repos/huggingface/datasets/issues/5190/events | https://github.com/huggingface/datasets/issues/5190 | 1,433,014,626 | I_kwDODunzps5VahFi | 5,190 | `path` is `None` when downloading a custom audio dataset from the Hub | {
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"Hi! Yes, this is expected behavior - we do this as a security measure to not leak local paths (this info would be useless on other users' machines anyways) and only push audio bytes. \r\n"
] | 2022-11-02T11:51:25Z | 2022-11-02T12:55:02Z | 2022-11-02T12:55:02Z | MEMBER | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I've created an [audio dataset](https://huggingface.co/datasets/lewtun/audio-test-push) using the `audiofolder` feature desribed in the [docs](https://huggingface.co/docs/datasets/audio_dataset#audiofolder) and then pushed it to the Hub.
Locally, I can see the `audio.path` feature is of the expected form `path/to/data_dir`, but when I download the dataset from the Hub, I see `audio.path` is `None`
Here's an example:
```python
from datasets import load_dataset
ds = load_dataset("lewtun/audio-test-push")
ds["train"][0]
# {
# "audio": {
# "path": None, <-- Is this expected?
# "array": array(
# [
# 3.97140226e-07,
# 7.30310290e-07,
# 7.56406735e-07,
# ...,
# -1.19636677e-01,
# -1.16811886e-01,
# -1.12441722e-01,
# ]
# ),
# "sampling_rate": 44100,
# },
# "song_id": 0,
# "genre_id": 0,
# "genre": "Electronic",
# }
```
Is this expected behaviour? If yes, feel free to close this issue as it's not a true bug then :)
### Steps to reproduce the bug
1. Create an audio dataset with the `audiofolder` feature
2. Push the dataset to the Hub with `push_to_hub()`
3. Download the Hub dataset and inspect the `audio.path` feature
### Expected behavior
`audio.path` points to the file associated with the audio data
### Environment info
- `datasets` version: 2.6.2.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5186 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5186/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5186/comments | https://api.github.com/repos/huggingface/datasets/issues/5186/events | https://github.com/huggingface/datasets/issues/5186 | 1,432,045,011 | I_kwDODunzps5VW0XT | 5,186 | Incorrect error message when Dataset.from_sql fails and sqlalchemy not installed | {
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"Hi! The first `Dataset.from_sql` call also outputs the \"ImportError: Using URI string without sqlalchemy installed.\" message, but you also get \"During handling of the above exception another exception occurred: ...\" after which the ValueError is printed. I agree that this behavior makes it easy to miss the ori... | 2022-11-01T20:25:51Z | 2022-11-15T18:24:39Z | 2022-11-15T18:24:39Z | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
When calling `Dataset.from_sql` (in my case, with sqlite3), it fails with a message ```ValueError: Please pass `features` or at least one example when writing data``` when I don't have `sqlalchemy` installed.
### Steps to reproduce the bug
Make a new sqlite db with `sqlite3` and `pandas` from a remote [URL](https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv).
```python
import sqlite3
import pandas as pd
from datasets import Dataset
conn = sqlite3.connect('us_covid_data.db')
df = pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv')
df.to_sql('states', conn, if_exists='replace')
```
Then if you try to query this DB like this:
```python
ds = Dataset.from_sql('''SELECT * from states WHERE state=="New York";''', "sqlite:///us_covid_data.db")
```
You run into the error I described above:
```ValueError: Please pass `features` or at least one example when writing data```
However, if you try to pass features, as the error suggests, then you get an error that tells you the underlying problem...
```python
from datasets import Dataset, Features, Value
features = Features({
'date': Value('date32'),
'label': Value('string'),
'fips': Value('int32'),
'cases': Value('int32'),
'deaths': Value('int32')
})
ds = Dataset.from_sql(
'''SELECT * from states WHERE state=="New York";''',
"sqlite:///us_covid_data.db",
features=features
)
```
Which results in the actual underlying error: `ImportError: Using URI string without sqlalchemy installed.`
### Expected behavior
Instead of `ValueError` about needing to pass features, we should provide the actual underlying error about not having SQLAlchemy installed when it isn't found in the environment.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.10
- PyArrow version: 10.0.0
- Pandas version: 1.2.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5183 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5183/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5183/comments | https://api.github.com/repos/huggingface/datasets/issues/5183/events | https://github.com/huggingface/datasets/issues/5183 | 1,431,418,066 | I_kwDODunzps5VUbTS | 5,183 | Loading an external dataset in a format similar to conll2003 | {
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} | I'm trying to load a custom dataset in a Dataset object, it's similar to conll2003 but with 2 columns only (word entity), I used the following script:
features = datasets.Features(
{"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=["B-PER", .... etc.]))}
)
from datasets import Dataset
INPUT_COLUMNS = "tokens ner_tags".split(" ")
def read_conll(file):
#all_labels = []
example = {col: [] for col in INPUT_COLUMNS}
idx = 0
with open(file) as f:
for line in f:
if line:
if line.startswith("-DOCSTART-") and example["tokens"] != []:
print(idx, example)
yield idx, example
idx += 1
example = {col: [] for col in INPUT_COLUMNS}
elif line == "\n" or (line.startswith("-DOCSTART-") and example["tokens"] == []):
continue
else:
row_cols = line.split(" ")
for i, col in enumerate(example):
example[col] = row_cols[i].rstrip()
dset = Dataset.from_generator(read_conll, gen_kwargs={"file": "/content/new_train.txt"}, features = features)
The following error happened:
[/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <genexpr>(.0)
285 for key in unique_values(itertools.chain(*dicts)): # set merge all keys
286 # Will raise KeyError if the dict don't have the same keys
--> 287 yield key, tuple(d[key] for d in dicts)
288
TypeError: tuple indices must be integers or slices, not str
What does this mean and what should I modify? | {
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https://api.github.com/repos/huggingface/datasets/issues/5182 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5182/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5182/comments | https://api.github.com/repos/huggingface/datasets/issues/5182/events | https://github.com/huggingface/datasets/issues/5182 | 1,431,029,547 | I_kwDODunzps5VS8cr | 5,182 | Add notebook / other resource links to the task-specific data loading guides | {
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... | null | [
"Yea this would be great! We would need an object detection tutorial notebook too if it doesn't already exist there. ",
"There is one: https://huggingface.co/docs/datasets/object_detection.\r\n\r\nI will start the work. "
] | 2022-11-01T07:57:26Z | 2022-11-03T01:49:57Z | 2022-11-03T01:49:57Z | MEMBER | null | null | {
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} | Does it make sense to include links to notebooks / scripts that show how to use a dataset for training / fine-tuning a model?
For example, here in [https://huggingface.co/docs/datasets/image_classification] we could include a mention of https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb.
Applies to https://huggingface.co/docs/datasets/object_detection as well.
Cc: @osanseviero @nateraw | {
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https://api.github.com/repos/huggingface/datasets/issues/5181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5181/comments | https://api.github.com/repos/huggingface/datasets/issues/5181/events | https://github.com/huggingface/datasets/issues/5181 | 1,431,027,102 | I_kwDODunzps5VS72e | 5,181 | Add a guide for semantic segmentation | {
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... | null | [
"Sure this sounds great! Would this be pure torchvision, albumentations, or something else?",
"I am considering `torchvision` and `albumentations`. Also [works with TensorFlow](https://github.com/deep-diver/segformer-tf-transformers/blob/main/notebooks/TFSegFormer_Finetune.ipynb). \r\n\r\nI am assigning the issue... | 2022-11-01T07:54:50Z | 2022-11-04T18:23:36Z | 2022-11-04T18:23:36Z | MEMBER | null | null | {
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} | Currently, we have these guides for object detection and image classification:
* https://huggingface.co/docs/datasets/object_detection
* https://huggingface.co/docs/datasets/image_classification
I am proposing adding a similar guide for semantic segmentation.
I am happy to contribute a PR for it.
Cc: @osanseviero @nateraw | {
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https://api.github.com/repos/huggingface/datasets/issues/5179 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5179/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5179/comments | https://api.github.com/repos/huggingface/datasets/issues/5179/events | https://github.com/huggingface/datasets/issues/5179 | 1,430,826,100 | I_kwDODunzps5VSKx0 | 5,179 | `map()` fails midway due to format incompatibility | {
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"Cc: @lhoestq ",
"You can end up with a list instead of a tensor if all the tensors inside the list can't be stacked together - can you make sure all your inputs are tensors with the same shape ?",
"Is there an easy way to ensure it?",
"You can make sure your `tokenize` function always return tensors of the s... | 2022-11-01T03:57:59Z | 2022-11-08T11:35:26Z | 2022-11-08T11:35:26Z | MEMBER | null | null | {
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} | ### Describe the bug
I am using the `emotion` dataset from Hub for sequence classification. After training the model, I am using it to generate predictions for all the entries present in the `validation` split of the dataset.
```py
def get_test_accuracy(model):
def fn(batch):
inputs = {k:v.to(device) for k,v in batch.items()
if k in tokenizer.model_input_names}
with torch.no_grad():
output = model(**inputs)
pred_label = torch.argmax(output.logits, axis=-1)
return {"predicted_label": pred_label.cpu().numpy()}
return fn
```
This is how the `get_test_accuracy()` is being used:
```py
emotions = load_dataset("emotion")
def tokenize(batch):
return tokenizer(batch["text"], padding=True, truncation=True)
emotions_encoded = emotions.map(tokenize, batched=True)
emotions_encoded.set_format("torch",
columns=["input_ids", "attention_mask", "label"])
new_dataset = emotions_encoded["validation"].map(
accuracy_fn, batched=True, batch_size=128
)
```
Complete code is available in the Colab Notebook provided below.
The `map()` process fails midway giving:
```shell
AttributeError Traceback (most recent call last)
<ipython-input-8-ad24ac288eb4> in <module>
2
3 new_dataset = emotions_encoded["validation"].map(
----> 4 accuracy_fn, batched=True, batch_size=128
5 )
7 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, 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)
2588 new_fingerprint=new_fingerprint,
2589 disable_tqdm=disable_tqdm,
-> 2590 desc=desc,
2591 )
2592 else:
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
582 self: "Dataset" = kwargs.pop("self")
583 # apply actual function
--> 584 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
585 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
586 for dataset in datasets:
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
478 # Call actual function
479
--> 480 out = func(self, *args, **kwargs)
481
482 # Update fingerprint of in-place transforms + update in-place history of transforms
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, 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, disable_tqdm, desc, cache_only)
2970 indices,
2971 check_same_num_examples=len(input_dataset.list_indexes()) > 0,
-> 2972 offset=offset,
2973 )
2974 except NumExamplesMismatchError:
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)
2850 if with_rank:
2851 additional_args += (rank,)
-> 2852 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
2853 if update_data is None:
2854 # Check if the function returns updated examples
<ipython-input-6-4e0d280426f6> in fn(batch)
1 def get_test_accuracy(model):
2 def fn(batch):
----> 3 inputs = {k:v.to(device) for k,v in batch.items()
4 if k in tokenizer.model_input_names}
5 with torch.no_grad():
<ipython-input-6-4e0d280426f6> in <dictcomp>(.0)
2 def fn(batch):
3 inputs = {k:v.to(device) for k,v in batch.items()
----> 4 if k in tokenizer.model_input_names}
5 with torch.no_grad():
6 output = model(**inputs)
AttributeError: 'list' object has no attribute 'to'
```
As you'd notice in the notebook, the process fails _midway_ and not at the beginning.
Is this expected?
### Steps to reproduce the bug
Colab Notebook:
https://colab.research.google.com/gist/sayakpaul/d1570d537faf39040d02d77b1ed7de07/scratchpad.ipynb
### Expected behavior
The mapping process should complete as is. If you switch the `split` to `test` it works as expected.
### Environment info
Colab | {
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https://api.github.com/repos/huggingface/datasets/issues/5178 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5178/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5178/comments | https://api.github.com/repos/huggingface/datasets/issues/5178/events | https://github.com/huggingface/datasets/issues/5178 | 1,430,800,810 | I_kwDODunzps5VSEmq | 5,178 | Unable to download the Chinese `wikipedia`, the dumpstatus.json not found! | {
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} | [] | closed | false | {
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"In the dumps page of the wiki (https://dumps.wikimedia.org/zhwiki/), I found the following dumps:\r\n```\r\nIndex of /zhwiki/\r\n[../](https://dumps.wikimedia.org/)\r\n[20220701/](https://dumps.wikimedia.org/zhwiki/20220701/) 21-Aug-2022 01:48 -\r\n[202207... | 2022-11-01T03:17:55Z | 2022-11-02T08:27:15Z | 2022-11-02T08:24:29Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
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} | ### Describe the bug
I tried:
`data = load_dataset('wikipedia', '20220301.zh', beam_runner='DirectRunner')`
and
`data = load_dataset("wikipedia", language="zh", date="20220301", beam_runner='DirectRunner')`
but both got:
`FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/zhwiki/20220301/dumpstatus.json`
the full report is:
```
FileNotFoundError Traceback (most recent call last)
<ipython-input-13-d07c5021090c> in <module>
1 from datasets import load_dataset
2
----> 3 data = load_dataset("wikipedia", language="zh", date="20220301", beam_runner='DirectRunner')<?, ?it/s]
/opt/conda/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1740
1741 # Download and prepare data
-> 1742 builder_instance.download_and_prepare(
1743 download_config=download_config,
1744 download_mode=download_mode,
/opt/conda/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, storage_options, **download_and_prepare_kwargs)
812 **download_and_prepare_kwargs,
813 }
--> 814 self._download_and_prepare(
815 dl_manager=dl_manager,
816 verify_infos=verify_infos,
/opt/conda/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1645 options=beam_options,
1646 )
-> 1647 super()._download_and_prepare(
1648 dl_manager, verify_infos=False, pipeline=pipeline, **prepare_splits_kwargs
1649 ) # TODO handle verify_infos in beam datasets
/opt/conda/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
881 split_dict = SplitDict(dataset_name=self.name)
882 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 883 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
884
885 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py in _split_generators(self, dl_manager, pipeline)
943 info_url = _base_url(lang) + _INFO_FILE
944 # Use dictionary since testing mock always returns the same result.
--> 945 downloaded_files = dl_manager.download_and_extract({"info": info_url})
946
947 xml_urls = []
/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls)
431 extracted_path(s): `str`, extracted paths of given URL(s).
432 """
--> 433 return self.extract(self.download(url_or_urls))
434
435 def get_recorded_sizes_checksums(self):
/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py in download(self, url_or_urls)
308
309 start_time = datetime.now()
--> 310 downloaded_path_or_paths = map_nested(
311 download_func,
312 url_or_urls,
/opt/conda/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, parallel_min_length, types, disable_tqdm, desc)
427 num_proc = 1
428 if num_proc <= 1 or len(iterable) < parallel_min_length:
--> 429 mapped = [
430 _single_map_nested((function, obj, types, None, True, None))
431 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)
428 if num_proc <= 1 or len(iterable) < parallel_min_length:
429 mapped = [
--> 430 _single_map_nested((function, obj, types, None, True, None))
431 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
432 ]
/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)
329 # Singleton first to spare some computation
330 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 331 return function(data_struct)
332
333 # Reduce logging to keep things readable in multiprocessing with tqdm
/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config)
335 # append the relative path to the base_path
336 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 337 return cached_path(url_or_filename, download_config=download_config)
338
339 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]):
/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
186 if is_remote_url(url_or_filename):
187 # URL, so get it from the cache (downloading if necessary)
--> 188 output_path = get_from_cache(
189 url_or_filename,
190 cache_dir=cache_dir,
/opt/conda/lib/python3.8/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, ignore_url_params, download_desc)
533 )
534 elif response is not None and response.status_code == 404:
--> 535 raise FileNotFoundError(f"Couldn't find file at {url}")
536 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
537 if head_error is not None:
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/zhwiki/20220301/dumpstatus.json
```
### Steps to reproduce the bug
`data = load_dataset('wikipedia', '20220301.zh', beam_runner='DirectRunner')`
### Expected behavior
download the data
### Environment info
python3.6
latest datasets/transformers version | {
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https://api.github.com/repos/huggingface/datasets/issues/5176 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5176/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5176/comments | https://api.github.com/repos/huggingface/datasets/issues/5176/events | https://github.com/huggingface/datasets/issues/5176 | 1,430,214,539 | I_kwDODunzps5VP1eL | 5,176 | prepare dataset for cloud storage doesn't work | {
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"It looks like an issue with `gcsfs`, are you able to instantiate a `GCSFileSystem` manually ?",
"closing since it was probably due to gcsfs"
] | 2022-10-31T17:28:57Z | 2023-03-28T09:11:46Z | 2023-03-28T09:11:45Z | NONE | null | null | {
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} | ### Describe the bug
Following the [documentation](https://huggingface.co/docs/datasets/filesystems#load-and-save-your-datasets-using-your-cloud-storage-filesystem) and [this PR](https://github.com/huggingface/datasets/pull/4724), I was downloading and storing huggingface dataset to cloud storage.
```
from datasets import load_dataset, load_dataset_builder
dataset = load_dataset_builder("wikipedia", "20220301.en", cache_dir='LOCAL_PATH')
dataset.download_and_prepare("gs://Bucket_NAME", file_format="parquet")
```
The above code successfully downloaded dataset, however, it returns error from `download_and_prepare`.
> Traceback (most recent call last):
> File "/shared/zhuiai/research/wiki/wiki/gcsfs.py", line 12, in <module>
> dataset.download_and_prepare("gs://upgen/dataset/wiki", file_format="parquet")
> File "/shared/zhuiai/.conda/envs/wiki/lib/python3.9/site-packages/datasets/builder.py", line 671, in download_and_prepare
> fs_token_paths = fsspec.get_fs_token_paths(output_dir, storage_options=storage_options)
> File "/shared/zhuiai/.conda/envs/wiki/lib/python3.9/site-packages/fsspec/core.py", line 635, in get_fs_token_paths
> cls = get_filesystem_class(protocol)
> File "/shared/zhuiai/.conda/envs/wiki/lib/python3.9/site-packages/fsspec/registry.py", line 234, in get_filesystem_class
> register_implementation(protocol, _import_class(bit["class"]))
> File "/shared/zhuiai/.conda/envs/wiki/lib/python3.9/site-packages/fsspec/registry.py", line 257, in _import_class
> mod = importlib.import_module(mod)
> File "/shared/zhuiai/.conda/envs/wiki/lib/python3.9/importlib/__init__.py", line 127, in import_module
> return _bootstrap._gcd_import(name[level:], package, level)
> File "<frozen importlib._bootstrap>", line 1030, in _gcd_import
> File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
> File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked
> File "<frozen importlib._bootstrap>", line 680, in _load_unlocked
> File "<frozen importlib._bootstrap_external>", line 850, in exec_module
> File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
> File "/shared/zhuiai/research/wiki/wiki/gcsfs.py", line 12, in <module>
> dataset.download_and_prepare("gs://upgen/dataset/wiki", file_format="parquet")
> File "/shared/zhuiai/.conda/envs/wiki/lib/python3.9/site-packages/datasets/builder.py", line 671, in download_and_prepare
> fs_token_paths = fsspec.get_fs_token_paths(output_dir, storage_options=storage_options)
> File "/shared/zhuiai/.conda/envs/wiki/lib/python3.9/site-packages/fsspec/core.py", line 635, in get_fs_token_paths
> cls = get_filesystem_class(protocol)
> File "/shared/zhuiai/.conda/envs/wiki/lib/python3.9/site-packages/fsspec/registry.py", line 234, in get_filesystem_class
> register_implementation(protocol, _import_class(bit["class"]))
> File "/shared/zhuiai/.conda/envs/wiki/lib/python3.9/site-packages/fsspec/registry.py", line 258, in _import_class
> return getattr(mod, name)
> AttributeError: partially initialized module 'gcsfs' has no attribute 'GCSFileSystem' (most likely due to a circular import)
### Steps to reproduce the bug
1. pip install datasets==2.6.1 gcsfs==2022.8.2
2. Run the following code will reproduce the issue (change `LOCAL_PATH` and `Bucket_NAME` accordingly)
```
from datasets import load_dataset, load_dataset_builder
dataset = load_dataset_builder("wikipedia", "20220301.en", cache_dir='LOCAL_PATH')
dataset.download_and_prepare("gs://Bucket_NAME", file_format="parquet")
```
### Expected behavior
Expecting successful downloading dataset and uploading it to cloud storage.
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-25-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.5.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5175 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5175/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5175/comments | https://api.github.com/repos/huggingface/datasets/issues/5175/events | https://github.com/huggingface/datasets/issues/5175 | 1,428,696,231 | I_kwDODunzps5VKCyn | 5,175 | Loading an external NER dataset | {
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} | I need to use huggingface datasets to load a custom dataset similar to conll2003 but with more entities and each the files contain only two columns: word and ner tag.
I tried this code snnipet that I found here as an answer to a similar issue:
from datasets import Dataset
INPUT_COLUMNS = "ID Text NER".split()
def read_conll(file):
example = {col: [] for col in INPUT_COLUMNS}
idx = 0
with open(file) as f:
for line in f:
if line.startswith("-DOCSTART-") or line == "\n" or not line:
if example[next(iter(example))]:
yield idx, example
idx += 1
example = {col: [] for col in INPUT_COLUMNS}
else:
row_cols = line.split()
for i, col in enumerate(example):
example[col] = row_cols[i].rstrip()
train = Dataset.from_generator(read_conll, gen_kwargs={"file": "some_path"})
But the following error happened:
ValueError: Please pass `features` or at least one example when writing data | {
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} | Currently this fails
```python
import torch
from datasets.fingerprint import Hasher
t = torch.tensor([1.])
def func(x):
return t + x
hash1 = Hasher.hash(func)
t = torch.tensor([1.])
hash2 = Hasher.hash(func)
assert hash1 == hash2
```
Also as noticed in https://discuss.huggingface.co/t/dataset-cant-cache-models-outputs/24945, using a model in a `map` function doesn't work well with caching. Indeed the `bert-base-uncased` model has a different hash every time you reload it. Supporting torch tensors may also help in this case.
This can be fixed by registering a custom pickling functions for torch tensors - as we did for other objects such as CodeType, FunctionType and Regex in `py_utils.py` | {
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"Thanks for reporting, @Rijgersberg.\r\n\r\nWe were waiting for the release of `dill` 0.3.6, that happened 2 days ago (24 Oct 2022): https://github.com/uqfoundation/dill/releases/tag/dill-0.3.6\r\n- See comment: https://github.com/huggingface/datasets/pull/4397#discussion_r880629543\r\n\r\nAlso `multiprocess` 0.70.... | 2022-10-25T13:23:50Z | 2022-11-14T08:25:37Z | 2022-10-28T05:38:15Z | NONE | null | null | {
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} | ### Describe the bug
When using `pip-compile` (part of `pip-tools`) to generate a pinned requirements file that includes `datasets`, a version conflict of `dill` appears.
It is caused by a transitive dependency conflict between `datasets` and `multiprocess`.
### Steps to reproduce the bug
```bash
$ echo "datasets" > requirements.in
$ pip install pip-tools
$ pip-compile requirements.in
Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets==2.6.1->-r requirements.in (line 1))
Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6
Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1
There are incompatible versions in the resolved dependencies:
dill<0.3.6 (from datasets==2.6.1->-r requirements.in (line 1))
dill>=0.3.6 (from multiprocess==0.70.14->datasets==2.6.1->-r requirements.in (line 1))
```
### Expected behavior
A correctly generated file `requirements.txt` with pinned dependencies
### Environment info
Tested with versions `2.6.1, 2.6.0, 2.5.2` on Python 3.8 and 3.10 on Ubuntu 20.04LTS and Python 3.10 on MacOS 12.6 (M1). | {
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"Addressed in https://github.com/huggingface/datasets/pull/5191 (torch.Tensor objects now produce deterministic hashes)"
] | 2022-10-25T12:19:00Z | 2022-11-03T16:12:52Z | 2022-11-03T16:12:51Z | NONE | null | null | {
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} | ### Describe the bug
Hi,
I try to cache some outputs of teacher model( Knowledge Distillation ) by using map function of Dataset library, while every time I run my code, I still recompute all the sequences. I tested Bert Model like this, I got different hash every single run, so any idea to deal with this?
### Steps to reproduce the bug
1. run below code
2. get different hash
```
from transformers import BertModel
from transformers import AutoTokenizer
import torch
token = ['hello']
model = BertModel.from_pretrained("bert-base-uncased").eval()
tok = AutoTokenizer.from_pretrained("bert-base-uncased")
def abcd():
with torch.no_grad():
out = model(**tok(token,return_tensors='pt'))[0]
# out = tok(token)
return out
from datasets.fingerprint import Hasher
my_func = abcd
print(Hasher.hash(my_func))
print(abcd())
```
### Expected behavior
I wanna cache all the model output
### Environment info
datasets:2.5.0 | {
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"There are currently 402 datasets with deprecated \"languages\" or \"licenses\".",
"hey @albertvillanova ,i would love to work on this issue if you like.",
"Hi @ayushthe1, thanks for your offer.\r\n\r\nBut as you can see, I self-assigned this issue.\r\n\r\nI have already fixed 200 out of the 402 datasets. My sc... | 2022-10-25T08:19:29Z | 2022-10-25T11:27:26Z | 2022-10-25T10:42:19Z | MEMBER | null | null | {
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} | While working on this:
- #5137
we realized there are still many datasets with deprecated "languages" and "licenses" tag names (instead of "language" and "license").
This is a blocking issue: no subsequent PR can be opened to modify their metadata: a ValueError will be thrown.
We should fix the "language" and "license" tag names in all Hub datasets.
TODO:
- [x] Fix language and license tag names in 402 Hub datasets
CC: @julien-c | {
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https://api.github.com/repos/huggingface/datasets/issues/5157 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5157/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5157/comments | https://api.github.com/repos/huggingface/datasets/issues/5157/events | https://github.com/huggingface/datasets/issues/5157 | 1,421,703,577 | I_kwDODunzps5UvXmZ | 5,157 | Consistent caching between python and jupyter | {
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"Hi ! Maybe it's possible to have a consistent hash for a function defined in `__main__` and a function define in a notebook.\r\n\r\nHowever for functions imported from another location, pickle uses the location to identify the code, so in that case we can't do much I believe.\r\n\r\nWould it be ok for you if we on... | 2022-10-25T01:34:33Z | 2022-11-02T15:43:22Z | 2022-11-02T15:43:22Z | CONTRIBUTOR | null | null | {
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} | ### Feature request
I hope this is not my mistake, currently if I use `load_dataset` from a python session on a custom dataset to do the preprocessing, it will be saved in the cache and in other python sessions it will be loaded from the cache, however calling the same from a jupyter notebook does not work, meaning the preprocessing starts from scratch.
If adjusting the hashes is impossible, is there a way to manually set dataset fingerprint to "force" this behaviour?
### Motivation
If this is not already the case and I am doing something wrong, it would be useful to have the two fingerprints consistent so one can create the dataset once and then try small things on jupyter without preprocessing everything again.
### Your contribution
I am happy to try a PR if you give me some pointers where the changes should happen | {
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"Hi ! From the `adlfs` docs, there are two filesystems you can use:\r\n> To use the Gen1 filesystem:\r\n> - known_implementations[‘adl’] = {‘class’: ‘adlfs.AzureDatalakeFileSystem’}\r\n> \r\n> To use the Gen2 filesystem:\r\n> - known_implementations[‘abfs’] = {‘class’: ‘adlfs.AzureBlobFileSystem’}\r\n\r\nIf I'm no... | 2022-10-25T00:43:18Z | 2024-02-15T09:48:36Z | 2022-11-17T23:37:08Z | NONE | null | null | {
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} | ### Describe the bug
When using the DatasetBuilder method with the credentials for the cloud storage Azure Data Lake (adl) Gen2, the following error is showed:
```
Traceback (most recent call last):
File "download_hf_dataset.py", line 143, in <module>
main()
File "download_hf_dataset.py", line 102, in main
builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet")
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/datasets/builder.py", line 671, in download_and_prepare
fs_token_paths = fsspec.get_fs_token_paths(output_dir, storage_options=storage_options)
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/core.py", line 639, in get_fs_token_paths
fs = cls(**options)
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/spec.py", line 76, in __call__
obj = super().__call__(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'account_name'
```
If I don't pass the storage_options argument (leave it as None), it requires the credentials used in ADL Gen 1:
`TypeError: __init__() missing 3 required positional arguments: 'tenant_id', 'client_id', and 'client_secret'`
Thus, it is not possible to download a dataset from the cloud using Azure Data Lake (adl) Gen2.
### Steps to reproduce the bug
Assuming that you have an account on Azure and at Storage Account that can be used for reproduce:
1. Create a dict with the format to connect to Azure Data Lake Gen 2
```
storage_options = {"account_name": ACCOUNT_NAME, "account_key": ACCOUNT_KEY) # gen 2 filesystem
```
2. Create a dataset builder for any HF hosted dataset
```
builder = load_dataset_builder(dataset_name)
```
3. Try to download the dataset passing the storage_options as an argument
```
save_dir = 'adl://my_save_dir'
builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet")
```
### Expected behavior
Not seeing the error mentioned above and being able to download the dataset to the provided path on ADL
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5153 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5153/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5153/comments | https://api.github.com/repos/huggingface/datasets/issues/5153/events | https://github.com/huggingface/datasets/issues/5153 | 1,420,833,457 | I_kwDODunzps5UsDKx | 5,153 | default Image/AudioFolder infers labels when there is no metadata files even if there is only one dir | {
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"Makes sense! For the last structure, we could count the path segments (delimited by \"/\" for URLs and `os.sep` for local paths) to ensure all inferred labels are on the same level. Otherwise, I think it's safe to assume they are meaningless and ignore them.\r\n"
] | 2022-10-24T13:28:18Z | 2022-11-15T16:31:10Z | 2022-11-15T16:31:09Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
By default FolderBasedBuilder infers labels if there is not metadata files, even if it's meaningless (for example, they are in a single directory or in the root folder, see this repo as an example: https://huggingface.co/datasets/patrickvonplaten/audios
As this is a corner case for quick exploration of images or audios on the Hub.
### Steps to reproduce the bug
If you have directory like this:
```
repo
image1.jpg
image2.jpg
image3.jpg
```
or
```
repo
data
image1.jpg
image2.jpg
image3.jpg
```
doing `ds = load_dataset(repo)` would create `label` feature:
```python
print(ds["train"][0])
>> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FB5326468E0>, 'label': 0}
```
Also, if you have the following structure:
```
repo
data
image1.jpg
image2.jpg
image3.jpg
image4.jpg
image5.jpg
image6.jpg
```
it will infer two labels:
```python
print(ds["train"][0])
print(ds["train"][-1])
>> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FB5326468E0>, 'label': 1}
>> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x415 at 0x7FB5326555B0>, 'label': 0}
```
### Expected behavior
We should have only one base feature (Image/Audio) in such cases.
### Environment info
all versions of `datasets` | {
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"Hi, @santule.\r\n\r\nWe have transferred all dataset scripts from GitHub to the Hugging Face Hub: https://huggingface.co/datasets\r\n- Concretely, you have \"rvl_cdip\" here: https://huggingface.co/datasets/rvl_cdip\r\n\r\nTo be able to load them, you should update your `datasets` library:\r\n```\r\npip install -U... | 2022-10-24T04:57:42Z | 2022-10-24T12:23:47Z | 2022-10-24T06:25:28Z | NONE | null | null | {
"completed": 0,
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} | Hi,
I am trying to use load_dataset to load the official "rvl_cdip" dataset but getting an error.
dataset = load_dataset("rvl_cdip")
Couldn't find 'rvl_cdip' on the Hugging Face Hub either: FileNotFoundError: Couldn't find the file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/rvl_cdip/rvl_cdip.py
Regards,
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https://api.github.com/repos/huggingface/datasets/issues/5145 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5145/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5145/comments | https://api.github.com/repos/huggingface/datasets/issues/5145/events | https://github.com/huggingface/datasets/issues/5145 | 1,418,005,452 | I_kwDODunzps5UhQvM | 5,145 | Dataset order is not deterministic with ZIP archives and `iter_files` | {
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"Thanks for reporting ! The issue doesn't come from shuffling, but from `beans` row order not being deterministic:\r\n\r\nhttps://huggingface.co/datasets/beans/blob/main/beans.py uses `dl_manager.iter_files` on ZIP archives and the file order doesn't seen to be deterministic and changes across machines",
"Thank y... | 2022-10-21T09:00:03Z | 2022-10-27T09:51:49Z | 2022-10-27T09:51:10Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ### Describe the bug
For the `beans` dataset (did not try on other), the order of samples is not the same on different machines. Tested on my local laptop, github actions machine, and ec2 instance. The three yield a different order.
### Steps to reproduce the bug
In a clean docker container or conda environment with datasets==2.6.1, run
```python
from datasets import load_dataset
from pprint import pprint
data = load_dataset("beans", split="validation")
pprint(data["image_file_path"])
```
### Expected behavior
The order of the images is the same on all machines.
### Environment info
On the EC2 instance:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.14.291-218.527.amzn2.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.7.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
- Numpy version: not checked
```
On my local laptop:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-50-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- Numpy version: 1.23.1
```
On github actions:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-1022-azure-x86_64-with-glibc2.2.5
- Python version: 3.8.14
- PyArrow version: 9.0.0
- Pandas version: 1.5.1
- Numpy version: 1.23.4
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"Thanks for reporting, @zhaowei-wang-nlp.\r\n\r\nYou are right, the documentation is confusing on the behavior of `remove_columns`. We should better explain it. ",
"This is a duplicate of https://github.com/huggingface/datasets/issues/2343.",
"I'm closing this issue because as @mariosasko pointed out, it is a d... | 2022-10-21T08:37:53Z | 2022-11-15T14:15:10Z | 2022-11-15T14:15:10Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
The page [process](https://huggingface.co/docs/datasets/process) says this about the parameter `remove_columns` of the function `map`:
When you remove a column, it is only removed after the example has been provided to the mapped function.
So it seems that the `remove_columns` parameter removes after the mapped functions.
However, another page, [the documentation of the function map](https://huggingface.co/docs/datasets/v2.6.1/en/package_reference/main_classes#datasets.Dataset.map.remove_columns) says:
Columns will be removed before updating the examples with the output of `function`, i.e. if `function` is adding columns with names in remove_columns, these columns will be kept.
So one page says "after the mapped function" and another says "before the mapped function."
Is there something wrong?
### Steps to reproduce the bug
Not about code.
### Expected behavior
consistent about the descriptions of the behavior of the parameter `remove_columns` in the function `map`.
### Environment info
datasets V2.6.0 | {
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"Hey ! Actually it works, just pass the right URL ;)\r\nThe URL must be the one with “/resolve/”\r\n\r\ne.g. https://huggingface.co/datasets/imagenet-1k/resolve/main/data/test_images.tar.gz\r\n\r\nYou can even pass a relative path to the dl_manager instead, like `dl_manager.download(\"data/test_images.tar.gz\")`",
... | 2022-10-20T15:29:29Z | 2022-10-20T17:17:10Z | 2022-10-20T17:17:10Z | CONTRIBUTOR | null | null | {
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} | ### Feature request
Maybe I'm mistaken but the `DownloadManager` does not support extracting git lfs files out of the box right?
Using `dl_manager.download()` or `dl_manager.download_and_extract()` still returns lfs files afaict.
Is there a good way to write a dataset loading script for a repo with lfs files?
### Motivation
/
### Your contribution
/ | {
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"I removed all the invalid task_ids in datasts without namespace, based on the <s>(internal)</s> types.ts",
"(Types.ts is not internal it's public)",
"I have opened PRs to fix the task_ids in all datasets within a namespace as well.\r\n\r\nWorking on task_categories...",
"For future reference: this fix had so... | 2022-10-19T09:41:42Z | 2022-11-10T05:25:58Z | 2022-10-25T06:17:00Z | MEMBER | null | null | {
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} | ## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets
- [x] task_categories: 451 datasets
- [x] task_ids: 556 datasets
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} | ## Describe the bug
As discussed in:
- https://github.com/huggingface/hub-docs/pull/423#pullrequestreview-1146083701
we should update our docs once dataset scripts have been transferred to the Hub (and removed from GitHub):
- #4974
Concretely:
- [x] Datasets on GitHub (legacy): https://huggingface.co/docs/datasets/main/en/share#datasets-on-github-legacy
- [x] ADD_NEW_DATASET: https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md
- ...
This PR complements the work of:
- #5067
This PR is a follow-up of PRs:
- #3777
CC: @julien-c | {
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"hey ,i would like to work on this issue . Please assign it to me.",
"hey @mariosasko , i made a pr for this issue. Could you please review it.\r\nAlso i found multiple `OSError` in `extract.py` file which i thought could be replaced too but wasn't sure about them.\r\nPlease do tell if that also needs to be done.... | 2022-10-18T17:53:46Z | 2022-10-25T15:56:59Z | 2022-10-25T15:56:59Z | COLLABORATOR | null | null | {
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> This exception is raised when a system function returns a system-related error, including I/O failures such as “file not found” or “disk full” (not for illegal argument types or other incidental errors).
Hence, it makes more sense to raise `ImportError` instead of `OSError` when the required extraction/decompression library is not installed. | {
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"Hi! The Torch ops in your snippet are not equivalent to the NumPy ones, hence the difference. You can get the same behavior by replacing the line `feature = torch.mean(feature, dim=1)` with `feature = feature.squeeze().mean(1)` .",
"> Hi! The Torch ops in your snippet are not equivalent to the NumPy ones, hence ... | 2022-10-18T17:53:35Z | 2022-10-19T04:15:45Z | 2022-10-19T04:15:44Z | NONE | null | null | {
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} | ## Describe the bug
I'm doing a torch.mean() operation in data preprocessing, and it's not working.
## Steps to reproduce the bug
```
from transformers import pipeline
import torch
import numpy as np
from datasets import load_dataset
device = 'cuda:0'
raw_dataset = load_dataset("glue", "sst2")
feature_extraction = pipeline('feature-extraction', 'bert-base-uncased', device=device)
def extracted_data(examples):
# feature = torch.tensor(feature_extraction(examples['sentence'], batch_size=16), device=device)
# feature = torch.mean(feature, dim=1)
feature = np.asarray(feature_extraction(examples['sentence'], batch_size=16)).squeeze().mean(1)
print(feature.shape)
return {'feature': feature}
extracted_dataset = raw_dataset.map(extracted_data, batched=True, batch_size=16)
```
## Results
When running with torch.mean(), the shape printed out is [16, seq_len, 768], which is exactly the same before the operation. While numpy works just fine, which gives [16, 768].
## Environment info
- `datasets` version: 2.6.1
- Platform: Linux-4.4.0-142-generic-x86_64-with-glibc2.31
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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"I can take this! #self-assign",
"#self-assign",
"@lazarust i'm already working on this issue :smile: ",
"#self-assign",
"hey @mariosasko , i made a pr for this issue. Could you please review it."
] | 2022-10-18T17:41:05Z | 2022-10-25T15:56:46Z | 2022-10-25T15:56:46Z | COLLABORATOR | null | null | {
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} | The `num_proc` parameter is only present in `DownloadManager.extract` but not in `StreamingDownloadManager.extract`, making it impossible to support streaming in the dataset scripts that use it (`openwebtext` and `the_pile_stack_exchange`). We can avoid this situation by deprecating this parameter and passing `DownloadConfig`'s `num_proc` to `map_nested` instead, as it's done in `DownloadManager.download`. | {
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https://api.github.com/repos/huggingface/datasets/issues/5131 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5131/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5131/comments | https://api.github.com/repos/huggingface/datasets/issues/5131/events | https://github.com/huggingface/datasets/issues/5131 | 1,413,534,863 | I_kwDODunzps5UQNSP | 5,131 | WikiText 103 tokenizer hangs | {
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"any updates on this? It happens to me on [OpenWikiText-20%](https://huggingface.co/datasets/Bingsu/openwebtext_20p) dataset, but not on [OpenWebText-10k](https://huggingface.co/datasets/stas/openwebtext-10k). This is really strange because I don't change anything else in my running script.\r\n\r\ntransformers vers... | 2022-10-18T16:44:00Z | 2023-08-08T08:42:40Z | 2023-07-21T14:41:51Z | NONE | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5129/comments | https://api.github.com/repos/huggingface/datasets/issues/5129/events | https://github.com/huggingface/datasets/issues/5129 | 1,413,031,664 | I_kwDODunzps5UOSbw | 5,129 | unexpected `cast` or `class_encode_column` result after `rename_column` | {
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"Hi! Unfortunately, I can't reproduce this issue locally (in Python 3.7/3.10) or in Colab. I would assume this is due to a bug we fixed in the latest release, but your version is up-to-date, so I'm not sure if there is something we can do to help...",
"Hi, 方子东. I tried running the code with exact the same configu... | 2022-10-18T11:15:24Z | 2022-10-19T03:02:26Z | 2022-10-19T03:02:26Z | NONE | null | null | {
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} | ## Describe the bug
When invoke `cast` or `class_encode_column` to a colunm renamed by `rename_column` , it will convert all the variables in this column into one variable. I also run this script in version 2.5.2, this bug does not appear. So I switched to the older version.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("amazon_reviews_multi", "en")
data = dataset['train']
data = data.remove_columns(
[
"review_id",
"product_id",
"reviewer_id",
"review_title",
"language",
"product_category",
]
)
data = data.rename_column("review_body", "text")
data1 = data.class_encode_column("stars")
print(set(data1.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
data = data.rename_column("stars", "label")
print(set(data.data.columns[0]))
# output: {<pyarrow.Int32Scalar: 5>, <pyarrow.Int32Scalar: 4>, <pyarrow.Int32Scalar: 1>, <pyarrow.Int32Scalar: 3>, <pyarrow.Int32Scalar: 2>}
data2 = data.class_encode_column("label")
print(set(data2.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 0>}
```
## Expected results
the last print should be:
{<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
## Actual results
but it output:
{<pyarrow.Int64Scalar: 0>}
## Environment info
- `datasets` version: 2.6.1
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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https://api.github.com/repos/huggingface/datasets/issues/5118 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5118/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5118/comments | https://api.github.com/repos/huggingface/datasets/issues/5118/events | https://github.com/huggingface/datasets/issues/5118 | 1,410,547,373 | I_kwDODunzps5UEz6t | 5,118 | Installing `datasets` on M1 computers | {
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"Thanks for reporting, @david1542."
] | 2022-10-16T16:50:08Z | 2022-10-19T09:10:08Z | 2022-10-19T09:10:08Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ## Describe the bug
I wanted to install `datasets` dependencies on my M1 (in order to start contributing to the project). However, I got an error regarding `tensorflow`.
On M1, `tensorflow-macos` needs to be installed instead. Can we add a conditional requirement, so that `tensorflow-macos` would be installed on M1?
## Steps to reproduce the bug
Fresh clone this project (on m1), create a virtualenv and run this:
```python
pip install -e ".[dev]"
```
## Expected results
Installation should be smooth, and all the dependencies should be installed on M1.
## Actual results
You should receive an error, saying pip couldn't find a version that matches this pattern:
```
tensorflow>=2.3,!=2.6.0,!=2.6.1
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.6.2.dev0
- Platform: macOS-12.6-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.0
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"Hi @echatzikyriakidis, thanks for submitting the issue.\r\nWhich shell are you using exactly? I tried to run the command you sent, but I don't see colors at all 🧐\r\n\r\nI tried from bash and zsh as well.",
"Hi @david1542 ,\r\n\r\nI use Google Colab.\r\n",
"Got it. I [created a PR](https://github.com/huggingf... | 2022-10-14T16:12:30Z | 2024-06-19T19:03:42Z | 2022-10-23T12:58:41Z | NONE | null | null | {
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} | ## Describe the bug
Progress bars after transformative operations turn in red and never be completed to 100%
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('rotten_tomatoes', split='test').filter(lambda o: True)
```
## Expected results
Progress bar should be 100% and green
## Actual results
Progress bar turn in red and never completed to 100%
## Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.14
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5112 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5112/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5112/comments | https://api.github.com/repos/huggingface/datasets/issues/5112/events | https://github.com/huggingface/datasets/issues/5112 | 1,409,143,409 | I_kwDODunzps5T_dJx | 5,112 | Bug with filtered indices | {
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"The issue is here:\r\nhttps://github.com/huggingface/datasets/blob/3ad9644b9a2e4558dd1d0f1e43c67658674e6228/src/datasets/arrow_dataset.py#L2964",
"@PartiallyTyped, @Muennighoff: the issue is fixed.\r\n\r\nWe are planning to make a patch release today.",
"Thanks a lot for the swift response! For a brief moment ... | 2022-10-14T10:35:47Z | 2022-10-14T13:55:03Z | 2022-10-14T12:11:45Z | MEMBER | null | null | {
"completed": 0,
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} | ## Describe the bug
As reported by @PartiallyTyped (and by @Muennighoff):
- https://github.com/huggingface/datasets/issues/5111#issuecomment-1278652524
There is an issue with the indices of a filtered dataset.
## Steps to reproduce the bug
```python
ds = Dataset.from_dict({"num": [0, 1, 2, 3]})
ds = ds.filter(lambda num: num % 2 == 0, input_columns="num", batch_size=2)
assert all(item["num"] % 2 == 0 for item in ds)
```
## Expected results
The indices of the filtered dataset should correspond to the examples with "language" equals to "english".
## Actual results
Indices to items with other languages are included in the filtered dataset indices
## Preliminar investigation
It seems a bug introduced by:
- #5030
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"Same bug exists with `num_proc=1` on colab. `3.7.14 (default, Sep 8 2022, 00:06:44) [GCC 7.5.0]` ",
"Thanks for reporting, @loubnabnl and for the additional information, @PartiallyTyped.\r\n\r\nHowever, I'm not able to reproduce this issue, neither locally nor on Colab:\r\n```\r\nDataset({\r\n features: ['re... | 2022-10-13T17:00:55Z | 2022-10-17T08:26:59Z | 2022-10-14T14:59:59Z | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
When mapping is used on a dataset with more than one process, there is a weird behavior when trying to use `filter` , it's like only the samples from one worker are retrieved, one needs to specify the same `num_proc` in filter for it to work properly. This doesn't happen with `datasets` version 2.5.2
In the code below the data is filtered differently when we increase `num_proc` used in `map` although the datsets before and after mapping have identical elements.
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
def preprocess(example):
return example
ds = load_dataset("codeparrot/codeparrot-clean-valid", split="train").select([i for i in range(10)])
ds1 = ds.map(preprocess, num_proc=2)
ds2 = ds.map(preprocess)
# the datasets elements are the same
for i in range(len(ds1)):
assert ds1[i]==ds2[i]
print(f'Target column before filtering {ds1["autogenerated"]}')
print(f'Target column before filtering {ds2["autogenerated"]}')
print(f"datasets version {datasets.__version__}")
ds_filtered_1 = ds1.filter(lambda x: not x["autogenerated"])
ds_filtered_2 = ds2.filter(lambda x: not x["autogenerated"])
# all elements in Target column are false so they should all be kept, but for ds2 only the first 5=num_samples/num_proc are kept
print(ds_filtered_1)
print(ds_filtered_2)
```
```
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 5
})
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 10
})
```
## Expected results
Increasing `num_proc` in mapping shouldn't alter filtering. With the previous version 2.5.2 this doesn't happen
## Actual results
Filtering doesn't work properly when we increase `num_proc` in mapping but not when calling `filter`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.6.0
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | {
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"The hash used for caching is computed by pickling recursively the function passed to `map`. Maybe some objects don't have the same hash across sessions. In particular you can check the hash of your model using\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\nobj = AutoModel.from_config(config=config, ad... | 2022-10-13T09:12:58Z | 2022-10-17T10:38:45Z | 2022-10-17T10:38:45Z | CONTRIBUTOR | null | null | {
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} | ## Describe the bug
The cache loading is not working as expected for some class methods with a model stored in an attribute.
The new fingerprint for `_map_single` is not the same at each run. The hasher generate a different hash for the class method.
This comes from `dumps` function in `datasets.utils.py_utils` which generates a different dump at each run.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import AutoConfig, AutoModel, AutoTokenizer
dataset = load_dataset("ethos", "binary")
BASE_MODELNAME = "sentence-transformers/all-MiniLM-L6-v2"
class Object:
def __init__(self):
config = AutoConfig.from_pretrained(BASE_MODELNAME)
self.bert = AutoModel.from_config(config=config, add_pooling_layer=False)
self.tok = AutoTokenizer.from_pretrained(BASE_MODELNAME)
def tokenize(self, examples):
tokenized_texts = self.tok(
examples["text"],
padding="max_length",
truncation=True,
max_length=256,
)
return tokenized_texts
instance = Object()
result = dict()
for phase in ["train"]:
result[phase] = dataset[phase].map(instance.tokenize, batched=True, load_from_cache_file=True, num_proc=2)
```
## Expected results
Load cache instead of recompute result.
## Actual results
Result recomputed from scratch at each run.
The cache works fine when deleting `bert` attribute.
## Environment info
- `datasets` version: 2.5.3.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.13
- PyArrow version: 7.0.0
- Pandas version: 1.5.0
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"Hi, thanks for reporting! The last line should be `dataset = Dataset.from_generator(my_gen)`.",
"Can I work on this one?"
] | 2022-10-11T14:28:58Z | 2022-10-12T11:31:56Z | 2022-10-12T11:31:56Z | NONE | null | null | {
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} | ## Describe the bug
In HOW-TO-GUIDES > Load > [Python generator](https://huggingface.co/docs/datasets/v2.5.2/en/loading#python-generator), the code example defines the `my_gen` function, but when creating the dataset, an undefined `my_dict` is passed in.
```Python
>>> from datasets import Dataset
>>> def my_gen():
... for i in range(1, 4):
... yield {"a": i}
>>> dataset = Dataset.from_generator(my_dict)
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https://api.github.com/repos/huggingface/datasets/issues/5099 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5099/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5099/comments | https://api.github.com/repos/huggingface/datasets/issues/5099/events | https://github.com/huggingface/datasets/issues/5099 | 1,404,370,191 | I_kwDODunzps5TtP0P | 5,099 | datasets doesn't support # in data paths | {
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"`datasets` doesn't seem to urlencode the directory names here\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/utils/file_utils.py#L109-L111\r\n\r\nfor example we should have\r\n```python\r\nfrom datasets.utils.file_utils import hf_hub_url\r\n\r\nurl = hf_hu... | 2022-10-11T10:05:32Z | 2022-10-13T13:14:20Z | 2022-10-13T13:14:20Z | NONE | null | null | {
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} | ## Describe the bug
dataset files with `#` symbol their paths aren't read correctly.
## Steps to reproduce the bug
The data in folder `c#`of this [dataset](https://huggingface.co/datasets/loubnabnl/bigcode_csharp) can't be loaded. While the folder `c_sharp` with the same data is loaded properly
```python
ds = load_dataset('loubnabnl/bigcode_csharp', split="train", data_files=["data/c#/*"])
```
```
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/27a3166cff4bb18e11919cafa6f169c0f57483de/data/c#/data_0003.jsonl
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-12.2.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
cc @lhoestq | {
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