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1,375,895,035
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https://api.github.com/repos/huggingface/datasets/issues/4986/labels{/name}
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Hello!
### Pull request overview
* Fix broken snippet in https://huggingface.co/docs/datasets/main/en/process that has too many quotes
### Details
The snippet in question can be found here: https://huggingface.co/docs/datasets/main/en/process#map
This screenshot shows the issue, there is a quote too many, causing the snippet to be colored incorrectly:
The change speaks for itself.
Thank you for the detailed documentation, by the way.
- Tom Aarsen
|
2022-09-16T22:12:21Z
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2022-09-16T12:41:07Z
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[doc] Fix broken snippet that had too many quotes
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2022-09-16T17:32:14Z
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PR_kwDODunzps4_GNSd
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Spent the day familiarising myself with the huggingface line of products, and happened to run into some small issues here and there. Magically, I've found exactly one small issue in `transformers`, one in `accelerate` and now one in `datasets`, hah!\r\n\r\nAs for this PR, the issue seems solved according to the [new PR documentation](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4986/en/process#map):\r\n\r\n"
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related to https://github.com/huggingface/datasets/issues/4895
|
2022-11-02T11:54:28Z
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2022-09-16T11:20:40Z
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Prefer split patterns from directories over split patterns from filenames
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2022-09-29T08:07:49Z
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PR_kwDODunzps4_F6kU
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Can we merge this one since the issue this PR fixes was reported for the second time? I also think we don't need a test for this simple change.",
"@mariosasko sure! could you please approve it? ",
"Hi there @polinaeterna @mariosasko! I have installed 5.2.3.dev0, which should have this fix. Unfortunately, I am still getting the error:\r\n`ValueError: Unknown split \"validation\". Should be one of ['train'].` When I call `load_dataset(\"csv\", data_files=files, split=split)`\r\n\r\nAny help would be greatly appreciated!"
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I added some links to the Datasets API in the docs. See https://github.com/huggingface/datasets-server/pull/566 for a companion PR in the datasets-server. The idea is to improve the discovery of the API through the docs.
I'm a bit shy about pasting a lot of links to the API in the docs, so it's minimal for now. I'm interested in ideas to integrate the API better in these docs without being too much. cc @lhoestq @julien-c @albertvillanova @stevhliu.
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2022-09-16T13:10:14Z
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docs: ✏️ add links to the Datasets API
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2022-09-16T13:07:33Z
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PR_kwDODunzps4_FhTm
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"_The documentation is not available anymore as the PR was closed or merged._",
"OK, thanks @lhoestq. I'll close this PR, and come back to it with @stevhliu once we work on https://github.com/huggingface/datasets-server/issues/568"
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I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below:
```python
from datasets import Dataset
data = [[1, 2],[3, 4]]
ds = Dataset.from_dict({"data": data})
ds = ds.with_format("torch")
ds[0]
ds[:2]
```
So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert?
Thanks.
|
2023-12-14T20:54:15Z
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How to convert torch.utils.data.Dataset to huggingface dataset?
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2022-09-20T11:23:43Z
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[
"Hi! I think you can use the newly-added `from_generator` method for that:\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndef gen():\r\n for idx in len(torch_dataset):\r\n yield torch_dataset[idx] # this has to be a dictionary\r\n ## or if it's an IterableDataset\r\n # for ex in torch_dataset:\r\n # yield ex\r\n\r\ndset = Dataset.from_generator(gen)\r\n```",
"Maybe `Dataset.from_list` can work as well no ?\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndset = Dataset.from_list(torch_dataset)\r\n```",
"> ```python\r\n> from datasets import Dataset\r\n> \r\n> def gen():\r\n> for idx in len(torch_dataset):\r\n> yield torch_dataset[idx] # this has to be a dictionary\r\n> ## or if it's an IterableDataset\r\n> # for ex in torch_dataset:\r\n> # yield ex\r\n> \r\n> dset = Dataset.from_generator(gen)\r\n> ```\r\n\r\nI try to use `Dataset.from_generator()` method, and it returns an error:\r\n```bash\r\nAttributeError: type object 'Dataset' has no attribute 'from_generator'\r\n```\r\nAnd I think it maybe the version of my datasets package is out-of-date, so I update it\r\n```bash\r\npip install --upgrade datasets\r\n```\r\nBut after that, the code still return the above Error. ",
"> ```python\r\n> dset = Dataset.from_list(torch_dataset)\r\n> ```\r\n\r\nIt seems that Dataset also has no `from_list` method 😂\r\n```bash\r\nAttributeError: type object 'Dataset' has no attribute 'from_list'\r\n```",
"> I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below:\r\n> \r\n> ```python\r\n> from datasets import Dataset\r\n> data = [[1, 2],[3, 4]]\r\n> ds = Dataset.from_dict({\"data\": data})\r\n> ds = ds.with_format(\"torch\")\r\n> ds[0]\r\n> ds[:2]\r\n> ```\r\n> \r\n> So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert? Thanks.\r\n\r\nMy dummy code is like:\r\n```python\r\nimport os\r\nimport json\r\nfrom torch.utils import data\r\nimport datasets\r\n\r\ndef gen(torch_dataset):\r\n for idx in len(torch_dataset):\r\n yield torch_dataset[idx] # this has to be a dictionary\r\n\r\nclass MyDataset(data.Dataset):\r\n def __init__(self, path):\r\n self.dict = []\r\n for line in open(path, 'r', encoding='utf-8'):\r\n j_dict = json.loads(line)\r\n self.dict.append(j_dict['context'])\r\n \r\n def __getitem__(self, idx):\r\n return self.dict[idx]\r\n\r\n def __len__(self):\r\n return len(self.dict)\r\n\r\nroot_path = os.path.dirname(os.path.abspath(__file__))\r\npath = os.path.join(root_path, 'dataset', 'train.json')\r\ntorch_dataset = MyDataset(path)\r\n\r\ndit = []\r\nfor line in open(path, 'r', encoding='utf-8'):\r\n j_dict = json.loads(line)\r\n dit.append(j_dict['context'])\r\ndset1 = datasets.Dataset.from_list(dit)\r\nprint(dset1)\r\ndset2 = datasets.Dataset.from_generator(gen)\r\nprint(dset2)\r\n```",
"We're releasing `from_generator` and `from_list` today :)\r\nIn the meantime you can play with them by installing `datasets` from source",
"> We're releasing `from_generator` and `from_list` today :) In the meantime you can play with them by installing `datasets` from source\r\n\r\nThanks a lot for your work!",
"> > I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below:\r\n> > ```python\r\n> > from datasets import Dataset\r\n> > data = [[1, 2],[3, 4]]\r\n> > ds = Dataset.from_dict({\"data\": data})\r\n> > ds = ds.with_format(\"torch\")\r\n> > ds[0]\r\n> > ds[:2]\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert? Thanks.\r\n> \r\n> My dummy code is like:\r\n> \r\n> ```python\r\n> import os\r\n> import json\r\n> from torch.utils import data\r\n> import datasets\r\n> \r\n> def gen(torch_dataset):\r\n> for idx in len(torch_dataset):\r\n> yield torch_dataset[idx] # this has to be a dictionary\r\n> \r\n> class MyDataset(data.Dataset):\r\n> def __init__(self, path):\r\n> self.dict = []\r\n> for line in open(path, 'r', encoding='utf-8'):\r\n> j_dict = json.loads(line)\r\n> self.dict.append(j_dict['context'])\r\n> \r\n> def __getitem__(self, idx):\r\n> return self.dict[idx]\r\n> \r\n> def __len__(self):\r\n> return len(self.dict)\r\n> \r\n> root_path = os.path.dirname(os.path.abspath(__file__))\r\n> path = os.path.join(root_path, 'dataset', 'train.json')\r\n> torch_dataset = MyDataset(path)\r\n> \r\n> dit = []\r\n> for line in open(path, 'r', encoding='utf-8'):\r\n> j_dict = json.loads(line)\r\n> dit.append(j_dict['context'])\r\n> dset1 = datasets.Dataset.from_list(dit)\r\n> print(dset1)\r\n> dset2 = datasets.Dataset.from_generator(gen)\r\n> print(dset2)\r\n> ```\r\nHi, when I am using this code to build my own dataset, ` datasets.Dataset.from_generator(gen)` report `TypeError: cannot pickle generator object` whre MyDataset returns a dict like {'image': bytes, 'text': string}. How can I resolve this? Thanks a lot!",
"Hi ! Right now generator functions are expected to be picklable, so that `datasets` can hash it and use the hash to cache the resulting Dataset on disk. Maybe this can be improved.\r\n\r\nIn the meantime, can you check that you're not using unpickable objects. In your case it looks like you're using a generator object that is unpickable. It might come from an opened file, e.g. this doesn't work:\r\n```python\r\nwith open(...) as f:\r\n\r\n def gen():\r\n for x in f:\r\n yield json.loads(x)\r\n\r\n ds = Dataset.from_generator(gen)\r\n```\r\nbut this does work:\r\n```python\r\ndef gen():\r\n with open(...) as f:\r\n for x in f:\r\n yield json.loads(x)\r\n\r\nds = Dataset.from_generator(gen)\r\n```",
"> Hi ! Right now generator functions are expected to be picklable, so that `datasets` can hash it and use the hash to cache the resulting Dataset on disk. Maybe this can be improved.\r\n> \r\n> In the meantime, can you check that you're not using unpickable objects. In your case it looks like you're using a generator object that is unpickable. It might come from an opened file, e.g. this doesn't work:\r\n> \r\n> ```python\r\n> with open(...) as f:\r\n> \r\n> def gen():\r\n> for x in f:\r\n> yield json.loads(x)\r\n> \r\n> ds = Dataset.from_generator(gen)\r\n> ```\r\n> \r\n> but this does work:\r\n> \r\n> ```python\r\n> def gen():\r\n> with open(...) as f:\r\n> for x in f:\r\n> yield json.loads(x)\r\n> \r\n> ds = Dataset.from_generator(gen)\r\n> ```\r\n\r\nThanks a lot! That's the reason why I have encountered this issue. Sorry for bothering you again with another problem, since my dataset is large and I use IterableDataset.from_generator which has no attribute with_transform, how can I equip it with some customed preprocessings like Dataset.from_generator? Should I move the preprocessing to the my torch Dataset?",
"Iterable datasets are lazy: exactly like `with_transform` they apply processing on the fly when accessing the examples.\r\n\r\nTherefore you can use `my_iterable_dataset.map()` instead :)",
"@lhoestq thanks a lot and I have successfully made it work~",
"@lhoestq I am having a similar issue. Can you help me understand which kinds of generators are picklable? I previously thought that no generators are picklable so I'm intrigued to hear this.",
"Generator functions are generally picklable. E.g.\r\n```python\r\nimport dill as pickle\r\n\r\ndef generator_fn():\r\n for i in range(10):\r\n yield i\r\n\r\npickle.dumps(generator_fn)\r\n```\r\n\r\nhowever generators are not picklable\r\n```python\r\ngenerator = generator_fn()\r\npickle.dumps(generator)\r\n# TypeError: cannot pickle 'generator' object\r\n```\r\n\r\nThough it can happen that some generator functions are not recursively picklable if they use global objects that are not picklable:\r\n```python\r\ndef generator_fn_not_picklable():\r\n for i in generator:\r\n yield i\r\n\r\npickle.dumps(generator_fn_not_picklable, recurse=True)\r\n# TypeError: cannot pickle 'generator' object\r\n````",
"I'm trying to create an IterableDataset from a generator but I get this error:\r\n`PicklingError: Can't pickle <built-in function input>: it's not the same object as builtins.input`\r\n\r\nWhat can I do?"
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The problem is described in that [issue](https://github.com/huggingface/datasets/issues/4895#issuecomment-1247975569).
> When I try to create data_infos.json using datasets-cli test Peter.py --save_infos --all_configs I get an error:
> ValueError: Unknown split "test". Should be one of ['train'].
>
> The data_infos.json is created perfectly fine when I use only one split - datasets.Split.TRAIN
>
> You can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch)
I tried to clear the cache folder, than I got an another error. I run:
```
git clone https://huggingface.co/datasets/sberbank-ai/Peter
cd Peter
git checkout add_splits # switch to a add_splits branch
rm dataset_infos.json # remove local dataset_infos.json
rm -r ~/.cache/huggingface # remove cached dataset_infos.json
datasets-cli test Peter.py --save_infos --all_configs # trying to create new dataset_infos.json
```
The error message:
```
Using custom data configuration default
Testing builder 'default' (1/1)
Downloading and preparing dataset peter/default to /Users/kalinin/.cache/huggingface/datasets/peter/default/0.0.0/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d...
Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 5160.63it/s]
Extracting data files: 0%| | 0/4 [00:00<?, ?it/s]Traceback (most recent call last):
File "/usr/local/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/usr/local/lib/python3.9/site-packages/datasets/commands/test.py", line 137, in run
builder.download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 771, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/kalinin/.cache/huggingface/modules/datasets_modules/datasets/Peter/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d/Peter.py", line 23, in _split_generators
data_files = dl_manager.download_and_extract(_URLS)
File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 431, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 403, in extract
extracted_paths = map_nested(
File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 393, in map_nested
mapped = [
File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 213, in cached_path
output_path = ExtractManager(cache_dir=download_config.cache_dir).extract(
File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 46, in extract
self.extractor.extract(input_path, output_path, extractor_format)
File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 263, in extract
with FileLock(lock_path):
File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 399, in __init__
max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax
FileNotFoundError: [Errno 2] No such file or directory: ''
Exception ignored in: <function BaseFileLock.__del__ at 0x11caeec10>
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 328, in __del__
self.release(force=True)
File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 303, in release
with self._thread_lock:
AttributeError: 'UnixFileLock' object has no attribute '_thread_lock'
Extracting data files: 0%| | 0/4 [00:00<?, ?it/s]
```
Can you help me please?
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 9.0.0
- Pandas version: 1.2.4
|
2022-09-28T07:59:39Z
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|
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Create dataset_infos.json with VALIDATION and TEST splits
|
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[
"@mariosasko could you help me with this issue? we've started the discussion from [here](https://github.com/huggingface/datasets/issues/4895#issuecomment-1248227130)",
"Hi again! Can you please pass the directory name containing the dataset script instead of the script name to `datasets-cli test`?",
"Yes, it worked! thanks a lot"
] |
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## Describe the bug
I can't create a dataset with `float16` features.
I understand from the traceback that this is a `pyarrow` error, but I don't see anywhere in the `datasets` documentation about how to successfully do this. Is it actually supported? I've tried older versions of `pyarrow` as well with the same exact error.
The bug seems to arise from `datasets` casting the values to `double` and then `pyarrow` doesn't know how to convert those back to `float16`... does that sound right? Is there a way to bypass this since it's not necessary in the `numpy` and `torch` cases?
Thanks!
## Steps to reproduce the bug
All of the following raise the following error with the same exact (as far as I can tell) traceback:
```python
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
```python
from datasets import Dataset, Features, Value
Dataset.from_dict({"x": [0.0, 1.0, 2.0]}, features=Features(x=Value("float16")))
import numpy as np
Dataset.from_dict({"x": np.arange(3, dtype=np.float16)}, features=Features(x=Value("float16")))
import torch
Dataset.from_dict({"x": torch.arange(3).to(torch.float16)}, features=Features(x=Value("float16")))
```
## Expected results
A dataset with `float16` features is successfully created.
## Actual results
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
Cell In [14], line 1
----> 1 Dataset.from_dict({"x": [1.0, 2.0, 3.0]}, features=Features(x=Value("float16")))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py:870, in Dataset.from_dict(cls, mapping, features, info, split)
865 mapping = features.encode_batch(mapping)
866 mapping = {
867 col: OptimizedTypedSequence(data, type=features[col] if features is not None else None, col=col)
868 for col, data in mapping.items()
869 }
--> 870 pa_table = InMemoryTable.from_pydict(mapping=mapping)
871 if info.features is None:
872 info.features = Features({col: ts.get_inferred_type() for col, ts in mapping.items()})
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:750, in InMemoryTable.from_pydict(cls, *args, **kwargs)
734 @classmethod
735 def from_pydict(cls, *args, **kwargs):
736 """
737 Construct a Table from Arrow arrays or columns
738
(...)
748 :class:`datasets.table.Table`:
749 """
--> 750 return cls(pa.Table.from_pydict(*args, **kwargs))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:3648, in pyarrow.lib.Table.from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:5174, in pyarrow.lib._from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:343, in pyarrow.lib.asarray()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:231, in pyarrow.lib.array()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py:197, in TypedSequence.__arrow_array__(self, type)
192 # otherwise we can finally use the user's type
193 elif type is not None:
194 # We use cast_array_to_feature to support casting to custom types like Audio and Image
195 # Also, when trying type "string", we don't want to convert integers or floats to "string".
196 # We only do it if trying_type is False - since this is what the user asks for.
--> 197 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
198 return out
199 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1853, in cast_array_to_feature(array, feature, allow_number_to_str)
1851 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
1852 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1853 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
1854 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1762, in array_cast(array, pa_type, allow_number_to_str)
1760 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):
1761 raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
-> 1762 return array.cast(pa_type)
1763 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:919, in pyarrow.lib.Array.cast()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/compute.py:389, in cast(arr, target_type, safe, options)
387 else:
388 options = CastOptions.safe(target_type)
--> 389 return call_function("cast", [arr], options)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:560, in pyarrow._compute.call_function()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:355, in pyarrow._compute.Function.call()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
|
2023-03-22T21:40:09Z
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Can't create a dataset with `float16` features
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CONTRIBUTOR
| null | null |
I_kwDODunzps5R9ii1
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[
"Hi @dconathan, thanks for reporting.\r\n\r\nWe rely on Arrow as a backend, and as far as I know currently support for `float16` in Arrow is not fully implemented in Python (C++), hence the `ArrowNotImplementedError` you get.\r\n\r\nSee, e.g.: https://arrow.apache.org/docs/status.html?highlight=float16#data-types",
"Thanks for the link…. didn’t realize arrow didn’t support it yet. Should it be removed from https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Value until Arrow supports it?",
"Yes, you are right: maybe we should either remove it from our docs or add a comment explaining the issue.\r\n\r\nThe thing is that in Arrow it is partially supported: you can create `float16` values, but you can't cast them from/to other types. And current implementation of `Value` always tries to perform a cast from `float64` to `float16`.",
"Maybe we can just add a note in the `Value` documentation ?",
"Would you accept a PR to fix this? @lhoestq Do you have an idea of how hard it would be to fix?",
"I think the issue comes mostly from pyarrow not supporting `float16` completely.\r\n\r\nFor example you stil can't cast from/to `float16`\r\n```python\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\npa.array(range(5)).cast(pa.float16())\r\n# ArrowNotImplementedError: Unsupported cast from int64 to halffloat using function cast_half_float\r\npa.array(range(5), pa.float32()).cast(pa.float16())\r\n# ArrowNotImplementedError: Unsupported cast from float to halffloat using function cast_half_float\r\npa.array(range(5), pa.float16())\r\n# ArrowTypeError: Expected np.float16 instance\r\npa.array(np.arange(5, dtype=np.float16())).cast(pa.float32())\r\n# ArrowNotImplementedError: Unsupported cast from halffloat to float using function cast_float\r\n```",
"Hmm it seems like we can either:\r\n1. try to fix pyarrow upstream\r\n2. half-support float16 with some workaround to make sure we don't ever do casting internally\r\n"
] |
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|
**Is your feature request related to a problem? Please describe.**
Is `pyarrow` really needed for every dataset?
**Describe the solution you'd like**
It is made optional.
**Describe alternatives you've considered**
Likely, no.
|
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Make `pyarrow` optional
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"The whole datasets library is pretty much a wrapper to pyarrow (just take a look at some of the source for a Dataset) https://github.com/huggingface/datasets/blob/51aef08ad7053c0bfe8f9a961207b26df15850d3/src/datasets/arrow_dataset.py#L639 \r\n\r\nI think removing the pyarrow dependency would involve a complete rewrite / a different library with minimal functionality (datasets-lite ?)",
"Thanks for the proposal, @KOLANICH. And also thanks for your answer, @dconathan.\r\n\r\nIndeed, we are using `pyarrow` as the backend for our datasets, in order to cache them and also allow memory-mapping (using datasets larger than your RAM memory).\r\n\r\nOne way to avoid using `pyarrow` could be loading the datasets in streaming mode, by passing `streaming=True` to `load_dataset`. This way you basically get a generator for the dataset; nothing is downloaded, nor cached. ",
"Thanks for the info. Could `datasets` then be made optional for `transformers` instead? I used `transformers` only to deal with pretrained models to deploy them (convert to ONNX, and then I use TVM), so I don't really need `pyarrow` and `datasets` by now.\r\n"
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Fix missing tags in dataset cards:
- amazon_us_reviews
- art
- discofuse
- indic_glue
- ubuntu_dialogs_corpus
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
- #4908
- #4921
- #4931
|
2022-09-22T12:37:55Z
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Update IndicGLUE download links
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**Is your feature request related to a problem? Please describe.**
Especially for big datasets like [LAION](https://huggingface.co/datasets/laion/laion2B-en/), it's hard to know exactly the downloaded size (because there are many files and you don't have their exact size when downloaded).
**Describe the solution you'd like**
Auto-populating the downloaded dataset size on the dataset page would be really useful, including that of each split (when there are some).
**Describe alternatives you've considered**
People should be adding this to dataset cards, but I don't think that is systematically the case :slightly_smiling_face:
**Additional context**
Mentioned to @lhoestq
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Providing dataset size
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"Hi @sashavor, thanks for your suggestion.\r\n\r\nUntil now we have the CLI command \r\n```\r\ndatasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs\r\n```\r\nthat generates the `dataset_infos.json` with the size of the downloaded dataset, among other information.\r\n\r\nWe are currently in the middle of removing those JSON files and putting their information directly in the header of the `README.md` (as YAML tags). Normally, the CLI command should continue working but saving its output to the dataset card instead. See:\r\n- #4926",
"Additionally, the download size can be inferred by doing HEAD requests to the files to be downloaded. And for files hosted on the hub you can even get the file sizes using the Hub API",
"Amazing @albertvillanova ! I think just having that information visible in the dataset info (without having to do any requests/additional coding) would be really useful :hugs: "
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**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context about the feature request here.
|
2022-09-26T16:32:35Z
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Hope to adapt Python3.9 as soon as possible
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"Hi! `datasets` should work in Python 3.9. What kind of issue have you encountered?",
"There is this related issue already: https://github.com/huggingface/datasets/issues/4113\r\nAnd I guess we need a CI job for 3.9 ^^",
"Perhaps we should report this issue in the `filelock` repo?"
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Add the `fn_kwargs` parameter to `IterableDataset.map`.
("Resolves" https://discuss.huggingface.co/t/how-to-use-large-image-text-datasets-in-hugging-face-hub-without-downloading-for-free/22780/3)
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2022-09-13T16:45:34Z
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thank you for adding this fix! \r\n\r\nWould it be possible to get `fn_kwargs` added to `IterableDatasetDict.map` as well? It looks like a very similar problem, and hopefully shouldn't be a huge change. \r\n",
"Hi @brianhill11! https://github.com/huggingface/datasets/pull/5810 adds this (opened a couple of days ago). It should be merged soon.",
"That's fantastic news, thanks @mariosasko ! I'll give it a shot once the changes are merged in. "
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Now that all the datasets live on the Hub we can remove the /datasets directory that contains all the dataset scripts of this repository
- [x] Needs https://github.com/huggingface/datasets/pull/4973 to be merged first
- [x] and PR to be enabled on the Hub for non-namespaced datasets
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[GH->HF] Part 2: Remove all dataset scripts from github
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"_The documentation is not available anymore as the PR was closed or merged._",
"So this means metrics will be deleted from this repo in favor of the \"evaluate\" library? Maybe you guys could just redirect metrics to that library.",
"We are deprecating the metrics in `datasets` indeed and suggest users to switch to `evaluate` (via a warning message)\r\n\r\nWe'll keep the current metrics as they are for now, but they'll be completely removed at one point",
"I guess this is ready to merge ?\r\n\r\nIt should break nothing except one rare case:\r\n\r\nIf someone is using an old version of `datasets` to try to load a recent dataset. Indeed in that case it fetches the `main` branch on github to see if it exists. But since we're removing all the datasets, forward fetching won't work anymore.\r\n\r\ne.g. if someone uses \"imagenet-1k\" with a version of `datasets` that didn't have it at that time. I checked on kibana and one single user would be affected with 4k downloads/months. It should still work for them though thanks to the `datasets` cache\r\n\r\nBut if they delete their cache, the workaround is... 🥁 update `datasets` 😅",
"Let's merge this on monday if we can, to make sure contributors who wanted to merge their dataset PRs here could do it",
"Alright, merging !"
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Currently datasets with no namespace (e.g. squad, glue) are loaded from github.
In this PR I changed this logic to use the Hugging Face Hub instead.
This is the first step in removing all the dataset scripts in this repository
related to discussions in https://github.com/huggingface/datasets/pull/4059 (I should have continued from this PR actually)
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[GH->HF] Load datasets from the Hub
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Reported in https://discuss.huggingface.co/t/typeerror-when-applying-map-after-set-format-type-torch/23067/2
Currently it fails if one uses batched `map` and the map function returns a torch tensor.
I fixed it for torch, tf, jax and pandas series.
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Fix map batched with torch output
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Currently, if the `input_columns` list in `Dataset.map` is specified, the columns not in that list are dropped after the `map` transform.
This makes the behavior inconsistent with `IterableDataset.map`.
(It seems this issue was introduced by mistake in https://github.com/huggingface/datasets/pull/2246)
Fix https://github.com/huggingface/datasets/issues/4858
|
2022-09-13T13:51:08Z
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Preserve non-`input_colums` in `Dataset.map` if `input_columns` are specified
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Support streaming nli_tr dataset.
This PR removes legacy `codecs.open` and replaces it with `open` that supports passing encoding.
Fix #3186.
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After contacting the authors of the VIVOS dataset to report that their data server is down, we have received a reply from Hieu-Thi Luong that their data is now hosted on Zenodo: https://doi.org/10.5281/zenodo.7068130
This PR updates their data URL and some metadata (homepage, citation and license).
Fix #4936.
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Fix data URL and metadata of vivos dataset
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Support streaming `compguesswhat` dataset.
Fix #3191.
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Strip "/" in local dataset path to avoid empty dataset name error
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## Describe the bug
I'm trying to run `cast_column("audio", Audio())` on Apple M1 Pro, but it seems that it doesn't work.
## Steps to reproduce the bug
```python
import datasets
dataset = load_dataset("csv", data_files="./train.csv")["train"]
dataset = dataset.map(lambda x: {"audio": str(DATA_DIR / "audio" / x["audio"])})
dataset = dataset.cast_column("audio", Audio())
dataset[0]
```
## Expected results
```
{'audio': {'bytes': None,
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav'},
'english_transcription': 'I would like to set up a joint account with my partner',
'intent_class': 11,
'lang_id': 4,
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'transcription': 'I would like to set up a joint account with my partner'}
```
## Actual results
````---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
Input In [6], in <cell line: 1>()
----> 1 dataset[0]
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/arrow_dataset.py:2165, in Dataset.__getitem__(self, key)
2163 def __getitem__(self, key): # noqa: F811
2164 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2165 return self._getitem(
2166 key,
2167 )
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/arrow_dataset.py:2150, in Dataset._getitem(self, key, decoded, **kwargs)
2148 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
2149 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2150 formatted_output = format_table(
2151 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2152 )
2153 return formatted_output
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table)
310 row = self.python_arrow_extractor().extract_row(pa_table)
311 if self.decoded:
--> 312 row = self.python_features_decoder.decode_row(row)
313 return row
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row)
220 def decode_row(self, row: dict) -> dict:
--> 221 return self.features.decode_example(row) if self.features else row
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1647, in Features.decode_example(self, example, token_per_repo_id)
1634 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1635 """Decode example with custom feature decoding.
1636
1637 Args:
(...)
1644 :obj:`dict[str, Any]`
1645 """
-> 1647 return {
1648 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1649 if self._column_requires_decoding[column_name]
1650 else value
1651 for column_name, (feature, value) in zip_dict(
1652 {key: value for key, value in self.items() if key in example}, example
1653 )
1654 }
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1648, in <dictcomp>(.0)
1634 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1635 """Decode example with custom feature decoding.
1636
1637 Args:
(...)
1644 :obj:`dict[str, Any]`
1645 """
1647 return {
-> 1648 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1649 if self._column_requires_decoding[column_name]
1650 else value
1651 for column_name, (feature, value) in zip_dict(
1652 {key: value for key, value in self.items() if key in example}, example
1653 )
1654 }
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1260, in decode_nested_example(schema, obj, token_per_repo_id)
1257 # Object with special decoding:
1258 elif isinstance(schema, (Audio, Image)):
1259 # we pass the token to read and decode files from private repositories in streaming mode
-> 1260 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
1261 return obj
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/audio.py:156, in Audio.decode_example(self, value, token_per_repo_id)
154 array, sampling_rate = self._decode_non_mp3_file_like(file)
155 else:
--> 156 array, sampling_rate = self._decode_non_mp3_path_like(path, token_per_repo_id=token_per_repo_id)
157 return {"path": path, "array": array, "sampling_rate": sampling_rate}
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/audio.py:257, in Audio._decode_non_mp3_path_like(self, path, format, token_per_repo_id)
254 use_auth_token = None
256 with xopen(path, "rb", use_auth_token=use_auth_token) as f:
--> 257 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)
258 return array, sampling_rate
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/util/decorators.py:88, in deprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f(*args, **kwargs)
86 extra_args = len(args) - len(all_args)
87 if extra_args <= 0:
---> 88 return f(*args, **kwargs)
90 # extra_args > 0
91 args_msg = [
92 "{}={}".format(name, arg)
93 for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])
94 ]
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/core/audio.py:164, in load(path, sr, mono, offset, duration, dtype, res_type)
161 else:
162 # Otherwise try soundfile first, and then fall back if necessary
163 try:
--> 164 y, sr_native = __soundfile_load(path, offset, duration, dtype)
166 except RuntimeError as exc:
167 # If soundfile failed, try audioread instead
168 if isinstance(path, (str, pathlib.PurePath)):
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/core/audio.py:195, in __soundfile_load(path, offset, duration, dtype)
192 context = path
193 else:
194 # Otherwise, create the soundfile object
--> 195 context = sf.SoundFile(path)
197 with context as sf_desc:
198 sr_native = sf_desc.samplerate
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:629, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
626 self._mode = mode
627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
--> 629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
632 self.seek(0)
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:1179, in SoundFile._open(self, file, mode_int, closefd)
1177 file_ptr = _snd.sf_open_fd(file, mode_int, self._info, closefd)
1178 elif _has_virtual_io_attrs(file, mode_int):
-> 1179 file_ptr = _snd.sf_open_virtual(self._init_virtual_io(file),
1180 mode_int, self._info, _ffi.NULL)
1181 else:
1182 raise TypeError("Invalid file: {0!r}".format(self.name))
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:1197, in SoundFile._init_virtual_io(self, file)
1194 def _init_virtual_io(self, file):
1195 """Initialize callback functions for sf_open_virtual()."""
1196 @_ffi.callback("sf_vio_get_filelen")
-> 1197 def vio_get_filelen(user_data):
1198 curr = file.tell()
1199 file.seek(0, SEEK_END)
MemoryError: Cannot allocate write+execute memory for ffi.callback(). You might be running on a system that prevents this. For more information, see https://cffi.readthedocs.io/en/latest/using.html#callbacks
```
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
|
2024-03-21T17:25:53Z
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[Apple M1] MemoryError: Cannot allocate write+execute memory for ffi.callback()
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2023-07-21T14:45:50Z
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[
"Hi! This seems like a bug in `soundfile`. Could you please open an issue in their repo? `soundfile` works without any issues on my M1, so I'm not sure we can help.",
"Hi @mariosasko, can you share how you installed `soundfile` on your mac M1?",
"Hi @hoangtnm - I upgraded to python 3.10 and it fixed the problem for me. I was also running 3.8 on an M1 mac.",
"Same here, upgrade python didn't work for me \r\n\r\nMemoryError: Cannot allocate write+execute memory for ffi.callback()\r\n\r\nany idea?",
"This is a `soundfile` issue, so there isn't much we can do about it. Hopefully, it gets fixed soon.",
"> Hi @hoangtnm - I upgraded to python 3.10 and it fixed the problem for me. I was also running 3.8 on an M1 mac.\r\n\r\nit work for me too \r\n"
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## Describe the bug
When trying to store `Array2D, Array3D, etc` as column values in a dataset, accessing that column (or creating depending on how you create it, see code below) will cause more than 10 fold of memory usage.
## Steps to reproduce the bug
```python
from datasets import Dataset, Features, Array2D, Array3D
import numpy as np
column_name = "a"
array_shape = (64, 64, 3)
data = np.random.random((10000,) + array_shape)
dataset = Dataset.from_dict({column_name: data}, features=Features({column_name: Array3D(shape=array_shape, dtype="float64")}))
```
the code above will use about 10Gb of RAM while constructing the `dataset` object.
The code below will use roughly the same amount of memory (and time) when trying to actually access the data itself of that column.
```python
from datasets import Dataset
import numpy as np
column_name = "a"
array_shape = (64, 64, 3)
data = np.random.random((10000,) + array_shape)
dataset = Dataset.from_dict({column_name: data})
dataset[column_name]
```
## Expected results
Some memory overhead, but not like as it is now and certainly not an overhead of such runtime that is currently happening.
## Actual results
Enormous memory- and runtime overhead.
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
|
2022-09-22T18:29:22Z
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2022-09-10T13:07:22Z
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Column of arrays (2D+) are using unreasonably high memory
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CONTRIBUTOR
| null | null |
I_kwDODunzps5Rk3Fq
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[
"note i have tried the same code with `datasets` version 2.4.0, the outcome is the very same as described above.",
"Seems related to issues #4623 and #4802 so it would appear this issue has been around for a few months.",
"Hi ! `Dataset.from_dict` keeps the data in memory. You can write on disk and reload them with\r\n```python\r\ndataset.save_to_disk(\"path/to/local\")\r\ndataset = load_from_disk(\"path/to/local\")\r\n```\r\nthis way you'll end up with a dataset loaded from your disk using memory mapping, and it won't fill up your RAM :)\r\n\r\nrelated to https://github.com/huggingface/datasets/issues/4861",
"@lhoestq thnx for getting back to me! i've tested the suggested method, but unfortunately the memory consumption is the very same:\r\n\r\n```\r\nfrom datasets import Dataset, Features, Array2D, Array3D, load_from_disk\r\nimport numpy as np\r\n\r\ncolumn_name = \"a\"\r\narray_shape = (64, 64, 3)\r\n\r\ndata = np.random.random((10000,) + array_shape)\r\ndataset = Dataset.from_dict({column_name: data}, features=Features({column_name: Array3D(shape=array_shape, dtype=\"float64\")}))\r\ndataset.save_to_disk(\"foo\")\r\n\r\nfoo_db = load_from_disk(\"foo\")\r\ncolum_value = foo_db[column_name]\r\n```\r\n\r\nthe very same happens when you create the dataset, but dont specify the feature type.\r\n\r\ni've tried running this on different envs (macOS, linux) and it's behaving the very same way.",
"When you call `colum_value = foo_db[column_name]`, you load the full column in memory.\r\n\r\nIf you want to avoid filling up your memory, you can access chunks of data instead\r\n```python\r\nembeddings = dataset[i:i + chunk_size][\"embeddings\"]\r\n```",
"@lhoestq yeah that's intentional, i.e. i really want to load the whole column into the memory. but as said above there's an unreasonable amount of overhead for the memory. the np array itself is using about 1G of memory:\r\n```\r\n>>> getsizeof(data)/1024/1024\r\n937.5001525878906\r\n```\r\nthat accessing of column above is using 10x memory compared to the original numpy array.",
"The dataset must be twice as big because we use regular arrow ListArray under the hood and not FixedSizeListArray. Basically we store unnecessary offsets.\r\n\r\nAnd this should affect performance as well. When we developed this, FixedSizeListArray still had some issues but they should be resolved on the PyArrow side now",
"A doubling would be fine. My very basic understanding of PyArrow is that using ListArray is probably related to the issue though. Using a multi-dimensional array in datasets is storing everything as strange nested 1d object arrays, which I imagine is creating the massive overhead.\r\n\r\nI think it should be a PyArrow Tensor, no?",
"PyArrow tensors are not part of the Arrow format AFAIK:\r\n\r\n> There is no direct support in the arrow columnar format to store Tensors as column values.\r\n\r\nsource: https://github.com/apache/arrow/issues/4802#issuecomment-508494694",
"That's... unfortunate. I didn't realize that."
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### Link
https://huggingface.co/datasets/julien-c/label-studio-my-dogs
### Description
<img width="1115" alt="image" src="https://user-images.githubusercontent.com/326577/189422048-7e9c390f-bea7-4521-a232-43f049ccbd1f.png">
### Owner
Yes
|
2022-09-20T15:40:07Z
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2022-09-09T18:45:33Z
|
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Dataset without script does not support regular JSON data file
|
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2022-09-20T15:40:07Z
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I_kwDODunzps5RjRfk
|
[
"Hi @julien-c,\r\n\r\nOut of the box, we only support JSON lines (NDJSON) data files, but your data file is a regular JSON file. The reason is we use `pyarrow.json.read_json` and this only supports line-delimited JSON. "
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exclude broken version of fsspec. See the [related issue](https://github.com/huggingface/datasets/issues/4961)
|
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Update setup.py
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[
"Before addressing this PR, we should be sure about the issue. See my comment in:\r\n- https://github.com/huggingface/datasets/issues/4961#issuecomment-1243376247",
"Once we know 2022.8.2 works, I'm closing this PR, as the corresponding issue."
] |
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## Describe the bug
When fsspec 2022.8.2 is installed in your environment, xopen will prematurely close files, making streaming mode inoperable.
## Steps to reproduce the bug
```python
import datasets
data = datasets.load_dataset('MLCommons/ml_spoken_words', 'id_wav', split='train', streaming=True)
```
## Expected results
Dataset should load as iterator.
## Actual results
```
[/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, **config_kwargs)
1737 # Return iterable dataset in case of streaming
1738 if streaming:
-> 1739 return builder_instance.as_streaming_dataset(split=split)
1740
1741 # Some datasets are already processed on the HF google storage
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1023 )
1024 self._check_manual_download(dl_manager)
-> 1025 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
1026 # By default, return all splits
1027 if split is None:
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _split_generators(self, dl_manager)
182 name=datasets.Split.TRAIN,
183 gen_kwargs={
--> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages],
185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in
186 self.config.languages] if not dl_manager.is_streaming else None,
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in <listcomp>(.0)
182 name=datasets.Split.TRAIN,
183 gen_kwargs={
--> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages],
185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in
186 self.config.languages] if not dl_manager.is_streaming else None,
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives(dl_manager, lang, format, split)
267 # for streaming case
268 def _download_audio_archives(dl_manager, lang, format, split):
--> 269 archives_paths = _download_audio_archives_paths(dl_manager, lang, format, split)
270 return [dl_manager.iter_archive(archive_path) for archive_path in archives_paths]
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives_paths(dl_manager, lang, format, split)
251 n_files_path = dl_manager.download(n_files_url)
252
--> 253 with open(n_files_path, "r", encoding="utf-8") as file:
254 n_files = int(file.read().strip()) # the file contains a number of archives
255
ValueError: I/O operation on closed file.
```
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
|
2022-09-12T17:45:50Z
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fsspec 2022.8.2 breaks xopen in streaming mode
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2022-09-12T14:32:05Z
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I_kwDODunzps5Ri-qB
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[
"loading `fsspec==2022.7.1` fixes this issue, setup.py would need to be changed to prevent users from using the latest version of fsspec.",
"Opened [PR](https://github.com/huggingface/datasets/pull/4962) to address this.",
"Hi @DCNemesis, thanks for reporting.\r\n\r\nThat was a temporary issue in `fsspec` releases 2022.8.0 and 2022.8.1. But they fixed it in their patch release 2022.8.2 (and yanked both previous versions). See:\r\n- https://github.com/huggingface/transformers/pull/18846\r\n\r\nAre you sure you have version 2022.8.2 installed?\r\n```shell\r\npip install -U fsspec\r\n```\r\n",
"@albertvillanova I was using a temporary Google Colab instance, but checking it again today it seems it was loading 2022.8.1 rather than 2022.8.2. It's surprising that colab is using the version that was replaced the same day it was released. Testing with 2022.8.2 did work. It appears Colab [will be fixing it](https://github.com/googlecolab/colabtools/issues/3055) on their end too. ",
"Thanks for the additional information.\r\n\r\nOnce we know 2022.8.2 works, I'm closing this issue. Feel free to reopen it if necessary.",
"Colab just upgraded their default `fsspec` version to 2022.8.2:\r\n- https://github.com/googlecolab/colabtools/issues/3055#issuecomment-1244019010"
] |
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## Describe the bug
I am trying to load a dataset from drive and running into an error.
## Steps to reproduce the bug
```python
data_dir = "/Users/dlituiev/repos/datasets/bioasq/BioASQ-training9b"
bioasq_task_b = load_dataset("aps/bioasq_task_b", data_dir=data_dir)
```
## Actual results
`AttributeError: 'BuilderConfig' object has no attribute 'schema'`
<details>
```
Using custom data configuration default-a1ca3e05be5abf2f
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [8], in <cell line: 2>()
1 data_dir = "/Users/dlituiev/repos/datasets/bioasq/BioASQ-training9b"
----> 2 bioasq_task_b = load_dataset("aps/bioasq_task_b", data_dir=data_dir)
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/load.py:1723, 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)
1720 ignore_verifications = ignore_verifications or save_infos
1722 # Create a dataset builder
-> 1723 builder_instance = load_dataset_builder(
1724 path=path,
1725 name=name,
1726 data_dir=data_dir,
1727 data_files=data_files,
1728 cache_dir=cache_dir,
1729 features=features,
1730 download_config=download_config,
1731 download_mode=download_mode,
1732 revision=revision,
1733 use_auth_token=use_auth_token,
1734 **config_kwargs,
1735 )
1737 # Return iterable dataset in case of streaming
1738 if streaming:
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/load.py:1526, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1523 raise ValueError(error_msg)
1525 # Instantiate the dataset builder
-> 1526 builder_instance: DatasetBuilder = builder_cls(
1527 cache_dir=cache_dir,
1528 config_name=config_name,
1529 data_dir=data_dir,
1530 data_files=data_files,
1531 hash=hash,
1532 features=features,
1533 use_auth_token=use_auth_token,
1534 **builder_kwargs,
1535 **config_kwargs,
1536 )
1538 return builder_instance
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/builder.py:1154, in GeneratorBasedBuilder.__init__(self, writer_batch_size, *args, **kwargs)
1153 def __init__(self, *args, writer_batch_size=None, **kwargs):
-> 1154 super().__init__(*args, **kwargs)
1155 # Batch size used by the ArrowWriter
1156 # It defines the number of samples that are kept in memory before writing them
1157 # and also the length of the arrow chunks
1158 # None means that the ArrowWriter will use its default value
1159 self._writer_batch_size = writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/builder.py:307, in DatasetBuilder.__init__(self, cache_dir, config_name, hash, base_path, info, features, use_auth_token, repo_id, data_files, data_dir, name, **config_kwargs)
305 if info is None:
306 info = self.get_exported_dataset_info()
--> 307 info.update(self._info())
308 info.builder_name = self.name
309 info.config_name = self.config.name
File ~/.cache/huggingface/modules/datasets_modules/datasets/aps--bioasq_task_b/3d54b1213f7e8001eef755af92877f9efa44161ee83c2a70d5d649defa95759e/bioasq_task_b.py:477, in BioasqTaskBDataset._info(self)
474 def _info(self):
475
476 # BioASQ Task B source schema
--> 477 if self.config.schema == "source":
478 features = datasets.Features(
479 {
480 "id": datasets.Value("string"),
(...)
504 }
505 )
506 # simplified schema for QA tasks
AttributeError: 'BuilderConfig' object has no attribute 'schema'
```
</details>
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.10.4
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
|
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BioASQ AttributeError: 'BuilderConfig' object has no attribute 'schema'
|
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[
"Following worked:\r\n\r\n```\r\ndata_dir = \"/Users/dlituiev/repos/datasets/bioasq/\"\r\nbioasq_task_b = load_dataset(\"aps/bioasq_task_b\", data_dir=data_dir, name=\"bioasq_9b_source\")\r\n```\r\n\r\nWould maintainers be open to one of the following:\r\n- automating this with a latest default config (e.g. `bioasq_9b_source`); how can this be generalized to other datasets?\r\n- providing an actionable error message that lists available `name` values? I only got available `name` values once I've provided something there (`name=\"aps/bioasq_task_b\"`), before it would not even mention that it requires `name` argument",
"Hi ! In general the list of available configurations is prompted. I think this is an issue with this specific dataset.\r\n\r\nFeel free to open a new discussions at https://huggingface.co/datasets/aps/bioasq_task_b/discussions\r\n\r\ncc @apsdehal\r\n\r\nIn particular it sounds like the `BUILDER_CONFIG_CLASS= BigBioConfig ` class attribute is missing and the _info should account for schema being None and raise an error"
] |
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After we informed the `compguesswhat` dataset authors about an error with their data URLs, they have updated them:
- https://github.com/CompGuessWhat/compguesswhat.github.io/issues/1
This PR updates their data URLs in our loading script.
Related to:
- #3191
|
2022-09-09T16:01:34Z
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Fix data URLs of compguesswhat dataset
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
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Hi,
When I use load_dataset from local jsonl files, below error happens, and I type the link into the browser prompting me `404: Not Found`. I download the other `.py` files using the same method and it works. It seems that the server is missing the appropriate file, or it is a problem with the code version.
```
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x2b08342004c0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))")))
```
|
2022-09-09T11:38:44Z
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|
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ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.4.0/datasets/jsonl/jsonl.py
|
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2022-09-09T11:38:44Z
| null |
I_kwDODunzps5RhWAQ
|
[
"I have solved this problem... The extension of the file should be `.json` not `.jsonl`"
] |
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Add `Dataset.from_generator` to the API to allow creating datasets from data larger than RAM. The implementation relies on a packaged module not exposed in `load_dataset` to tie this method with `datasets`' caching mechanism.
Closes https://github.com/huggingface/datasets/issues/4417
|
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Add `Dataset.from_generator`
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2022-09-16T14:44:18Z
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Fixes #4953
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2022-09-08T15:16:51Z
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Fix TF tests for 2.10
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See for example:
- https://github.com/huggingface/datasets/issues/3191
- https://github.com/huggingface/datasets/issues/3186
It would help provide clearer information on the Hub and help the dataset maintainer solve the issue by themselves quicker. Currently:
- https://huggingface.co/datasets/compguesswhat
<img width="1029" alt="Capture d’écran 2022-09-08 à 15 51 37" src="https://user-images.githubusercontent.com/1676121/189139946-6deffb91-f21b-4281-8825-a98026c69740.png">
- https://huggingface.co/datasets/nli_tr
<img width="1032" alt="Capture d’écran 2022-09-08 à 15 51 44" src="https://user-images.githubusercontent.com/1676121/189139963-d26490ed-ad23-48ea-9cfc-1ab9c4d08d0c.png">
cc @albertvillanova
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2022-09-08T13:53:36Z
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Raise a more precise error when the URL is unreachable in streaming mode
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Temporarily fix TensorFlow until a permanent solution is found.
Related to:
- #4953
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2022-09-08T14:12:33Z
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Pin TensorFlow temporarily
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## Describe the bug
The following CI test fails: https://github.com/huggingface/datasets/runs/8246722693?check_suite_focus=true
```
FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - AssertionError:
```
Details:
```
_________________________ TempSeedTest.test_tensorflow _________________________
[gw0] linux -- Python 3.7.13 /opt/hostedtoolcache/Python/3.7.13/x64/bin/python
self = <tests.test_py_utils.TempSeedTest testMethod=test_tensorflow>
@require_tf
def test_tensorflow(self):
import tensorflow as tf
from tensorflow.keras import layers
def gen_random_output():
model = layers.Dense(2)
x = tf.random.uniform((1, 3))
return model(x).numpy()
with temp_seed(42, set_tensorflow=True):
out1 = gen_random_output()
with temp_seed(42, set_tensorflow=True):
out2 = gen_random_output()
out3 = gen_random_output()
> np.testing.assert_equal(out1, out2)
E AssertionError:
E Arrays are not equal
E
E Mismatched elements: 2 / 2 (100%)
E Max absolute difference: 0.84619296
E Max relative difference: 16.083529
E x: array([[-0.793581, 0.333286]], dtype=float32)
E y: array([[0.052612, 0.539708]], dtype=float32)
tests/test_py_utils.py:149: AssertionError
```
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CI test of TensorFlow is failing
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To avoid having too many conflicts in the datasets and metrics dependencies I split the CI into test and test-catalog
test does the test of the core of the `datasets` lib, while test-catalog tests the datasets scripts and metrics scripts
This also makes `pip install -e .[dev]` much smaller for developers
WDYT @albertvillanova ?
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2023-09-24T10:05:57Z
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Add test-datasets CI job
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This PR adds the license information to `qasc` dataset, once reported via GitHub by Tushar Khot, the dataset is licensed under CC BY 4.0:
- https://github.com/allenai/qasc/issues/5
|
2022-09-08T14:54:47Z
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Fix license information in qasc dataset card
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The current enwik8 dataset link give a 502 bad gateway error which can be view on https://huggingface.co/datasets/enwik8 (click the dropdown to see the dataset preview, it will show the error). This corrects the links, and json metadata as well as adds a little bit more information about enwik8.
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The current enwik8 dataset link give a 502 bad gateway error which can be view on https://huggingface.co/datasets/enwik8 (click the dropdown to see the dataset preview, it will show the error). This corrects the links, and json metadata as well as adds a little bit more information about enwik8.
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Update enwik8 fixing the broken link
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Fix minor typo in error message for missing imports
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Try to fix the Windows CI after TF update 2.10
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Closes https://github.com/huggingface/datasets/issues/4945 by adding a regex check when pushing to hub.
Let me know if this is helpful and if it's the fix you would have in mind for the issue and I'm happy to contribute tests.
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2022-09-13T10:19:01Z
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Introduce regex check when pushing as well
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## Describe the bug
The `push_to_hub` method can push splits that do not respect the regex check that is used for downloads. Therefore, splits may be pushed but never re-used, which can be painful if the split was done after runtime preprocessing.
## Steps to reproduce the bug
```python
>>> from datasets import Dataset, DatasetDict, load_dataset
>>> d = Dataset.from_dict({'x': [1,2,3], 'y': [1,2,3]})
>>> di = DatasetDict()
>>> di['identifier-with-column'] = d
>>> di.push_to_hub('open-source-metrics/test')
Pushing split identifier-with-column to the Hub.
Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:04<00:00, 4.40s/it]
```
Loading it afterwards:
```python
>>> load_dataset('open-source-metrics/test')
Downloading: 100%|██████████| 610/610 [00:00<00:00, 432kB/s]
Using custom data configuration open-source-metrics--test-28b63ec7cde80488
Downloading and preparing dataset None/None (download: 950 bytes, generated: 48 bytes, post-processed: Unknown size, total: 998 bytes) to /home/lysandre/.cache/huggingface/datasets/open-source-metrics___parquet/open-source-metrics--test-28b63ec7cde80488/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]
Downloading data: 100%|██████████| 950/950 [00:00<00:00, 1.01MB/s]
Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.48s/it]
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 2291.97it/s]
Traceback (most recent call last):
File "/home/lysandre/.pyenv/versions/3.10.6/lib/python3.10/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/load.py", line 1746, in load_dataset
builder_instance.download_and_prepare(
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 771, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 48, in _split_generators
splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files}))
File "<string>", line 5, in __init__
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 599, in __post_init__
NamedSplit(self.name) # check that it's a valid split name
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 346, in __init__
raise ValueError(f"Split name should match '{_split_re}' but got '{split_name}'.")
ValueError: Split name should match '^\w+(\.\w+)*$' but got 'identifier-with-column'.
```
## Expected results
I would expect `push_to_hub` to stop me in my tracks if trying to upload a split that will not be working afterwards.
## Actual results
See above
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.15.64-1-lts-x86_64-with-glibc2.36
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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Push to hub can push splits that do not respect the regex
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from datasets import set_caching_enabled
set_caching_enabled(False)
for ds_name in ["squad","newsqa","nqopen","narrativeqa"]:
train_ds = load_from_disk("../../../dall/downstream/processedproqa/{}-train.hf".format(ds_name))
break
train_ds = concatenate_datasets([train_ds,train_ds,train_ds,train_ds]) #operation 1
trainer = QuestionAnsweringTrainer( #huggingface trainer
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset= None,
eval_examples=None,
answer_column_name=answer_column,
dataset_name="squad",
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics if training_args.predict_with_generate else None,
)
with operation 1, the GPU memory increases from 16G to 23G
|
2022-09-07T12:34:58Z
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2022-09-07T08:46:30Z
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larger dataset, larger GPU memory in the training phase? Is that correct?
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2022-09-07T12:34:58Z
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"does the trainer save it in GPU? sooo curious... how to fix it",
"It's my bad. didn't limit the input length"
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This PR addresses https://github.com/huggingface/datasets/issues/4795
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Add splits to MBPP dataset
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[
"```\r\n(env) cwarny@Cedrics-Air datasets % RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_mbpp\r\n================================================================================================ test session starts =================================================================================================\r\nplatform darwin -- Python 3.8.13, pytest-7.1.3, pluggy-1.0.0\r\nrootdir: /Users/cwarny/datasets, configfile: setup.cfg\r\ncollected 1 item \r\n\r\ntests/test_dataset_common.py . [100%]\r\n\r\n================================================================================================= 1 passed in 1.12s ==================================================================================================\r\n(env) cwarny@Cedrics-Air datasets % RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_mbpp \r\n================================================================================================ test session starts =================================================================================================\r\nplatform darwin -- Python 3.8.13, pytest-7.1.3, pluggy-1.0.0\r\nrootdir: /Users/cwarny/datasets, configfile: setup.cfg\r\ncollected 1 item \r\n\r\ntests/test_dataset_common.py . [100%]\r\n\r\n================================================================================================= 1 passed in 0.35s ==================================================================================================\r\n\r\n```",
"_The documentation is not available anymore as the PR was closed or merged._",
"Hi @cwarny ! Thanks for adding the correct splits :)\r\n\r\nYou can fix the CI error by running `make style` - this should reformat the dataset script",
"done"
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## Describe the bug
Both coarse and fine labels seem to be out of line.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = "trec"
raw_datasets = load_dataset(dataset)
df = pd.DataFrame(raw_datasets["test"])
df.head()
```
## Expected results
text (string) | coarse_label (class label) | fine_label (class label)
-- | -- | --
How far is it from Denver to Aspen ? | 5 (NUM) | 40 (NUM:dist)
What county is Modesto , California in ? | 4 (LOC) | 32 (LOC:city)
Who was Galileo ? | 3 (HUM) | 31 (HUM:desc)
What is an atom ? | 2 (DESC) | 24 (DESC:def)
When did Hawaii become a state ? | 5 (NUM) | 39 (NUM:date)
## Actual results
index | label-coarse |label-fine | text
-- |-- | -- | --
0 | 4 | 40 | How far is it from Denver to Aspen ?
1 | 5 | 21 | What county is Modesto , California in ?
2 | 3 | 12 | Who was Galileo ?
3 | 0 | 7 | What is an atom ?
4 | 4 | 8 | When did Hawaii become a state ?
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-1086-azure-x86_64-with-glibc2.27
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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Trec Dataset has incorrect labels
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2022-09-08T11:12:03Z
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[
"Thanks for reporting, @wmpauli. \r\n\r\nIndeed we recently fixed this issue:\r\n- #4801 \r\n\r\nThe fix will be accessible after our next library release. In the meantime, you can have it by passing `revision=\"main\"` to `load_dataset`."
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This PR fixes issue reported on the Hub:
- Label as multilingual: https://huggingface.co/datasets/xquad_r/discussions/1
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Fix multilinguality tag and missing sections in xquad_r dataset card
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Fix issue reported on the Hub: https://huggingface.co/datasets/adv_glue/discussions/1
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Fix NonMatchingChecksumError in adv_glue dataset
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We added a notice in README.md to show that we renamed the master branch to main, but we can remove it now (it's been 2 months)
I also unpinned the github issue about the branch renaming
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Remove main branch rename notice
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`huggingface-hub` says that the `identical_ok` argument of `HfApi.upload_file` is now deprecated, and will be removed soon. It even has no effect at the moment when it's passed:
```python
Args:
...
identical_ok (`bool`, *optional*, defaults to `True`):
Deprecated: will be removed in 0.11.0.
Changing this value has no effect.
...
```
There was only one occurence of `identical_ok=False` but it's maybe not worth adding a check ti verify if the files were the same.
cc @mariosasko
|
2022-09-06T22:24:09Z
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Remove deprecated identical_ok
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## Describe the bug
VIVOS data is not accessible anymore, neither of these links work (at least from France):
* https://ailab.hcmus.edu.vn/assets/vivos.tar.gz (data)
* https://ailab.hcmus.edu.vn/vivos (dataset page)
Therefore `load_dataset` doesn't work.
## Steps to reproduce the bug
```python
ds = load_dataset("vivos")
```
## Expected results
dataset loaded
## Actual results
```
ConnectionError: Couldn't reach https://ailab.hcmus.edu.vn/assets/vivos.tar.gz (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='ailab.hcmus.edu.vn', port=443): Max retries exceeded with url: /assets/vivos.tar.gz (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f9d8a27d190>: Failed to establish a new connection: [Errno -5] No address associated with hostname'))")))
```
Will try to contact the authors, as we wanted to use Vivos as an example in documentation on how to create scripts for audio datasets (https://github.com/huggingface/datasets/pull/4872), because it's small and straightforward and uses tar archives.
|
2022-09-21T06:06:02Z
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vivos (Vietnamese speech corpus) dataset not accessible
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completed
|
CONTRIBUTOR
|
2022-09-12T07:14:20Z
| null |
I_kwDODunzps5RQeyb
|
[
"If you need an example of a small audio datasets, I just created few hours ago a speech dataset with only 300MB of compressed audio files https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia. It works also with streaming (@albertvillanova helped me adding this functionality) :-)",
"@cahya-wirawan omg this is awesome!! thank you! ",
"We have contacted the authors to ask them."
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### Link
_No response_
### Description
_No response_
### Owner
_No response_
|
2022-09-06T12:51:25Z
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Dataset Viewer issue for ubuntu_dialogs_corpus
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completed
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NONE
|
2022-09-06T12:51:25Z
| null |
I_kwDODunzps5RQTBw
|
[
"The dataset maintainers (https://huggingface.co/datasets/ubuntu_dialogs_corpus) decided to forbid the dataset from being downloaded automatically (https://huggingface.co/docs/datasets/v2.4.0/en/loading#manual-download), and the dataset viewer respects this.\r\nWe will try to improve the error display though. Thanks for reporting."
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### Link
https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia
### Description
I created a new speech dataset https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia, but the dataset preview doesn't work with following error message:
```
Server error
Status code: 400
Exception: TypeError
Message: unsupported operand type(s) for +: 'NoneType' and 'str'
```
Please help, I am not sure what the problem here is. Thanks a lot.
### Owner
Yes
|
2022-09-06T12:46:40Z
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completed
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CONTRIBUTOR
|
2022-09-06T12:46:40Z
| null |
I_kwDODunzps5RPkCN
|
[
"The error is not related to the dataset viewer. I'm having a look...",
"Thanks @albertvillanova for checking the issue. Actually, I can use the dataset like following:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> ds=load_dataset(\"indonesian-nlp/librivox-indonesia\")\r\nNo config specified, defaulting to: librivox-indonesia/all\r\nReusing dataset librivox-indonesia (/root/.cache/huggingface/datasets/indonesian-nlp___librivox-indonesia/all/1.0.0/9a934a42bfb53dc103003d191618443b8a786bea2bd7bb0bc2d9454b8494521e)\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 500.87it/s]\r\n>>> ds\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['path', 'language', 'reader', 'sentence', 'audio'],\r\n num_rows: 7815\r\n })\r\n})\r\n>>> ds[\"train\"][0]\r\n{'path': '/root/.cache/huggingface/datasets/downloads/extracted/c8ead52370fa28feb64643ea9d05cd7d820192dc8a1700d665ec45ec7624f5a3/librivox-indonesia/sundanese/universal-declaration-of-human-rights/human_rights_un_sun_brc_0000.mp3', 'language': 'sun', 'reader': '3174', 'sentence': 'pernyataan umum ngeunaan hak hak asasi manusa sakabeh manusa', 'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/c8ead52370fa28feb64643ea9d05cd7d820192dc8a1700d665ec45ec7624f5a3/librivox-indonesia/sundanese/universal-declaration-of-human-rights/human_rights_un_sun_brc_0000.mp3', 'array': array([ 0. , 0. , 0. , ..., -0.02419001,\r\n -0.01957154, -0.01502833], dtype=float32), 'sampling_rate': 44100}}\r\n\r\n```\r\nIt would be just nice if I also can see it using dataset viewer.",
"Yes, the issue arises when streaming (that is used by the viewer): your script does not support streaming and to support it in this case there are some subtleties that we are explaining better in our docs in a work-in progress pull request:\r\n- #4872\r\n\r\nJust note that when streaming, `local_extracted_archive` is None, and this code line generates the error:\r\n```python\r\nfilepath = local_extracted_archive + \"/librivox-indonesia/audio_transcription.csv\"\r\n```\r\n\r\nFor a proper implementation, you could have a look at: https://huggingface.co/datasets/common_voice/blob/main/common_voice.py\r\n\r\nYou can test your script locally by passing `streaming=True` to `load_dataset`:\r\n```python\r\nds = load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\", streaming=True); item = next(iter(ds)); item\r\n```",
"Great, I will have a look and update the script. Thanks.",
"Hi @albertvillanova , I just add the streaming functionality and it works in the first try :-) Thanks a lot!",
"Awesome!!! :hugs: "
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## Describe the bug
`Dataset/DatasetDict.filter()` cannot have `batched=True` due to `mask` (numpy array?) being non-iterable.
## Steps to reproduce the bug
(In a python 3.7.12 env, I've tried 2.4.0 and 2.3.2 with both `pyarraw==9.0.0` and `pyarrow==8.0.0`.)
```python
from datasets import load_dataset
ds_mc4_ja = load_dataset("mc4", "ja") # This will take 6+ hours... perhaps test it with a toy dataset instead?
ds_mc4_ja_2020 = ds_mc4_ja.filter(
lambda example: example["timestamp"][:4] == "2020",
batched=True,
)
```
## Expected results
No error
## Actual results
```python
---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 524, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py", line 480, in wrapper
out = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2779, in _map_single
offset=offset,
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2655, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2347, in decorated
result = f(decorated_item, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 4946, in get_indices_from_mask_function
indices_array = [i for i, to_keep in zip(indices, mask) if to_keep]
TypeError: zip argument #2 must support iteration
"""
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
/tmp/ipykernel_51348/2345782281.py in <module>
7 batched=True,
8 # batch_size=10_000,
----> 9 num_proc=111,
10 )
11 # ds_mc4_ja_clean_2020 = ds_mc4_ja.filter(
/opt/conda/lib/python3.7/site-packages/datasets/dataset_dict.py in filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, fn_kwargs, num_proc, desc)
878 desc=desc,
879 )
--> 880 for k, dataset in self.items()
881 }
882 )
/opt/conda/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
878 desc=desc,
879 )
--> 880 for k, dataset in self.items()
881 }
882 )
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
522 }
523 # apply actual function
--> 524 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
525 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
526 # re-apply format to the output
/opt/conda/lib/python3.7/site-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
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
2920 new_fingerprint=new_fingerprint,
2921 input_columns=input_columns,
-> 2922 desc=desc,
2923 )
2924 new_dataset = copy.deepcopy(self)
/opt/conda/lib/python3.7/site-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)
2498
2499 for index, async_result in results.items():
-> 2500 transformed_shards[index] = async_result.get()
2501
2502 assert (
/opt/conda/lib/python3.7/site-packages/multiprocess/pool.py in get(self, timeout)
655 return self._value
656 else:
--> 657 raise self._value
658
659 def _set(self, i, obj):
TypeError: zip argument #2 must support iteration
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
(I've tried 2.4.0 and 2.3.2 with both `pyarraw==9.0.0` and `pyarrow==8.0.0`.)
|
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Dataset/DatasetDict.filter() cannot have `batched=True` due to `mask` (numpy array?) being non-iterable.
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[
"Hi ! When `batched=True`, you filter function must take a batch as input, and return a list of booleans.\r\n\r\nIn your case, something like\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n\r\nds_mc4_ja = load_dataset(\"mc4\", \"ja\") # This will take 6+ hours... perhaps test it with a toy dataset instead?\r\nds_mc4_ja_2020 = ds_mc4_ja.filter(\r\n lambda batch: [timestamp[:4] == \"2020\" for timestamp in batch[\"timestamp\"]],\r\n batched=True,\r\n)\r\n```\r\n\r\nLet me know if it helps !",
"> Hi ! When `batched=True`, you filter function must take a batch as input, and return a list of booleans.\r\n> [...]\r\n> Let me know if it helps !\r\n\r\nHi @lhoestq,\r\n\r\nAh, my bad, I totally forgot that part...\r\nSorry for the trouble and thank you for the kind help!"
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### Link
https://huggingface.co/datasets/bigscience-biomedical/biosses
### Description
I've just been working on adding the dataset loader script to this dataset and working with the relative imports. I'm not sure how to interpret the error below (show where the dataset preview used to be) .
```
Status code: 400
Exception: ModuleNotFoundError
Message: No module named 'datasets_modules.datasets.bigscience-biomedical--biosses.ddbd5893bf6c2f4db06f407665eaeac619520ba41f69d94ead28f7cc5b674056.bigbiohub'
```
### Owner
Yes
|
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Dataset Viewer issue for bigscience-biomedical/biosses
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[
"Possibly not related to the dataset viewer in itself. cc @huggingface/datasets.\r\n\r\nIn particular, I think that the import of bigbiohub is not working here: https://huggingface.co/datasets/bigscience-biomedical/biosses/blob/main/biosses.py#L29 (requires a relative path?)\r\n\r\n```python\r\n>>> from datasets import get_dataset_config_names\r\n>>> get_dataset_config_names('bigscience-biomedical/biosses')\r\nDownloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8.00k/8.00k [00:00<00:00, 7.47MB/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 289, in get_dataset_config_names\r\n dataset_module = dataset_module_factory(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1247, in dataset_module_factory\r\n raise e1 from None\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1220, in dataset_module_factory\r\n return HubDatasetModuleFactoryWithScript(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 931, in get_module\r\n local_imports = _download_additional_modules(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 215, in _download_additional_modules\r\n raise ImportError(\r\nImportError: To be able to use bigscience-biomedical/biosses, you need to install the following dependency: bigbiohub.\r\nPlease install it using 'pip install bigbiohub' for instance'\r\n```",
"Opened a PR here to (hopefully) fix the dataset script: https://huggingface.co/datasets/bigscience-biomedical/biosses/discussions/1/files",
"thanks for taking a look @severo . agree this isn't related to dataset viewer (sorry just clicked on the auto issue creator). also thanks @lhoestq , I see the format to use for relative imports. was a bit confused b/c it seems to be working here \r\n\r\nhttps://huggingface.co/datasets/bigscience-biomedical/scitail/blob/main/scitail.py#L31\r\n\r\nI'll try this PR a see what happens. ",
"closing as I think the issue is relative imports and attempting to read json files directly in the repo (thanks again @lhoestq ) "
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Fix missing tags in dataset cards:
- coqa
- hyperpartisan_news_detection
- opinosis
- scientific_papers
- scifact
- search_qa
- wiki_qa
- wiki_split
- wikisql
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
- #4908
- #4921
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Fix missing tags in dataset cards
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This PR adds the `cc-by-nc-2.0` to the list of licenses because it is required by `scifact` dataset: https://github.com/allenai/scifact/blob/master/LICENSE.md
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"_The documentation is not available anymore as the PR was closed or merged._",
"this list needs to be kept in sync with the ones in moon-landing and hub-docs :)",
"@julien-c don't you think it might be better to a have a single file (source of truth) in one of the repos and then use it in every other repo, instead of having 3 copies of the same file that must be kept in sync?\r\n\r\nAlso note that the licenses we are adding were all already present in our previous `licenses.json` file: are we regenerating it, step by step? Why don't we use a file with ALL the licenses we previously had in the list?\r\n\r\nLicenses added:\r\n- #4887\r\n- #4930 \r\n\r\nPrevious `licenses.json` file:\r\n- https://github.com/huggingface/datasets/blob/b7612754928e0fd43b9e3c3becb906ec280ff5d4/src/datasets/utils/resources/licenses.json\r\n- removed in this commit: https://github.com/huggingface/datasets/pull/4613/commits/9f7725412dac1089b3e057f9e3fcf39cc222bc26\r\n\r\nLet me know what you think and I can take care of this.",
"> Let me know what you think and I can take care of this.\r\n\r\nWhat I think is that we shouldn't add licenses that are just used in a couple of datasets, and just use `license_details` for this.\r\n\r\n> don't you think it might be better to a have a single file (source of truth) in one of the repos and then use it in every other repo, instead of having 3 copies of the same file that must be kept in sync?\r\n\r\nYes, in my opinion we can just delete this file from `datasets`, the validation is happening hub-side anyways now? \r\n",
"Feel free to delete the license list in `datasets` @albertvillanova ;)\r\n\r\nAlso FYI in #4926 I also removed all the validation steps anyway (language, license, types etc.)"
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As show in the [documentation page](https://huggingface.co/docs/datasets/loading) here the `"tr"in` should be `"train`.
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Fixes a typo in loading documentation
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Fixes #3094
Add ability to read/write to SQLite files and also read from any SQL database supported by SQLAlchemy.
I didn't add SQLAlchemy as a dependence as it is fairly big and it remains optional.
I also recorded a Loom to showcase the feature.
https://www.loom.com/share/f0e602c2de8a46f58bca4b43333d541f
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"_The documentation is not available anymore as the PR was closed or merged._",
"Ah CI runs with `pandas=1.3.5` which doesn't return the number of row inserted.",
"wow this is super cool!",
"@lhoestq I'm getting error in integration tests, not sure if it's related to my PR. Any help would be appreciated :) \r\n\r\n```\r\nif not self._is_valid_token(token):\r\n> raise ValueError(\"Invalid token passed!\")\r\nE ValueError: Invalid token passed!\r\n```",
"I just relaunched the tests, it should be fixed now",
"Thanks a lot for working on this!\r\n\r\nI have some concerns with the current design:\r\n* Besides SQLite, the loader should also work with the other engines supported by SQLAlchemy. (A better name for it in the current state would be `sqlite` :))\r\n* It should support arbitrary queries/table names - only the latter currently works.\r\n* Exposing this loader as a packaged builder (`load_dataset(\"sql\", ...)`) is not a good idea for the following reasons:\r\n * Considering the scenario where a table with the same name is present in multiple files is very unlikely, the data files resolution is not needed here. And if we remove that, what the name of the default split should be? \"train\"?\r\n * `load_dataset(\"sql\", ...)` also implies that streaming should work, but that's not the case. And I don't think we can change that, considering how hard it is to make SQLite files streamable.\r\n\r\nAll this makes me think we shouldn't expose this builder as a packaged module and, instead, limit the API to `Dataset.from_sql`/`Dataset.to_sql` (with the signatures matching the ones in pandas as much as possible; regarding this, note that SQLAlchemy connections are not hashable/picklable, which is required for caching, but I think it's OK only to allow URI strings as connections to bypass that (Dask has the same limitation).\r\n\r\nWDYT?",
"Hi @mariosasko thank you for your review.\r\n\r\nI agree that `load_dataset('sql',...)` is a bit weird and I would be happy to remove it. To be honest, I only added it when I saw that it was the preferred way in `loading.mdx`. \r\n\r\nI agree that the `SELECT` should be a parameters as well. I'll add it.\r\n\r\nSo far, only `Dataset.to_sql` explicitly supports any SQLAlchemy Connexion, I'm pretty sure that `Dataset.from_sql` would work with a Connexion as well, but it would break the typing from the parent class which is `path_or_paths: NestedDataStructureLike[PathLike]`. I would prefer not to break this API Contract.\r\n\r\n\r\nI will have time to work on this over the weekend. Please let me know what you think if I do the following:\r\n* Remove `load_dataset('sql', ...)` and edit the documentation to use `to_sql, from_sql`.\r\n* Tentatively make `Dataset.from_sql` typing work with SQLAlchemy Connexion.\r\n* Add support for custom queries (Default would be `SELECT * FROM {table_name}`).\r\n\r\nCheers!",
"Perhaps after we merge https://github.com/huggingface/datasets/pull/4957 (**Done!**), you can subclass `AbstractDatasetInputStream` instead of `AbstractDatasetReader` to not break the contract with the connection object. Also, let's avoid having the default value for the query/table (you can set it to `None` in the builder and raise an error in the builder config's `__post_init__` if it's not provided). Other than that, sounds good!",
"@Dref360 I've made final changes/refinements to align the SQL API with Pandas/Dask. Let me know what you think.\r\n",
"Thank you so much! I was missing a lot of things sorry about that.\r\nLGTM",
"I think we can merge if the tests pass. \r\n\r\nOne last thing I would like to get your opinion on - currently, if SQLAlchemy is not installed, the missing dependency error will be thrown inside `pandas.read_sql`. Do you think we should be the ones throwing this error, e.g. after the imports in `packaged_modules/sql/sql.py` if `SQLALCHEMY_AVAILABLE` is `False` (note that this would mean making `sqlalchemy` a required dependency for the docs to be able to add `SqlConfig` to the package reference)?",
"> One last thing I would like to get your opinion on - currently, if SQLAlchemy is not installed, the missing dependency error will be thrown inside pandas.read_sql\r\n\r\nIs sqlalchemy always required for pd.read_sql ? If so, I think we can raise the error on our side.\r\nBut sqlalchemy should still be an optional dependency for `datasets` IMO",
"@lhoestq \r\n> Is sqlalchemy always required for pd.read_sql ? If so, I think we can raise the error on our side.\r\n\r\nIn our case, it's always required as we only support database URIs.\r\n\r\n> But sqlalchemy should still be an optional dependency for datasets IMO\r\n\r\nYes, it will remain optional for datasets but will be required for building the docs (as is`s3fs`, for instance). ",
"Ok I see ! Sounds good :)"
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I've fixed some typos in BLEU metric card.
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To simplify the addition of new datasets, we'd like to have the dataset infos in the YAML and deprecate the dataset_infos.json file. YAML is readable and easy to edit, and the YAML metadata of the readme already contain dataset metadata so we would have everything in one place.
To be more specific, I moved these fields from DatasetInfo to the YAML:
- config_name (if there are several configs)
- download_size
- dataset_size
- features
- splits
Here is what I ended up with for `squad`:
```yaml
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 79346360
num_examples: 87599
- name: validation
num_bytes: 10473040
num_examples: 10570
config_name: plain_text
download_size: 35142551
dataset_size: 89819400
```
and it can be a list if there are several configs
I already did the change for `conll2000` and `crime_and_punish` as an example.
## Implementation details
### Load/Read
This is done via `DatasetInfosDict.write_to_directory/from_directory`
I had to implement a custom the YAML export logic for `SplitDict`, `Version` and `Features`.
The first two are trivial, but the logic for `Features` is more complicated, because I added a simplification step (or the YAML would be too long and less readable): it's just a formatting step to remove unnecessary nesting of YAML data.
### Other changes
I had to update the DatasetModule factories to also download the README.md alongside the dataset scripts/data files, and not just the dataset_infos.json
## YAML validation
I removed the old validation code that was in metadata.py, now we can just use the Hub YAML validation
## Datasets-cli
The `datasets-cli test --save_infos` command now creates a README.md file with the dataset_infos in it, instead of a datasets_infos.json file
## Backward compatibility
`dataset_infos.json` files are still supported and loaded if they exist to have full backward compatibility.
Though I removed the unnecessary keys when the value is the default (like all the `id: null` from the Value feature types) to make them easier to read.
## TODO
- [x] add comments
- [x] tests
- [x] document the new YAML fields
- [x] try to reload the new dataset_infos.json file content with an old version of `datasets`
## EDITS
- removed "config_name" when there's only one config
- removed "version" for now (?), because it's not useful in general
- renamed the yaml field "dataset_info" instead of "dataset_infos", since it only has one by default (and because "infos" is not english)
Fix https://github.com/huggingface/datasets/issues/4876
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Dataset infos in yaml
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"_The documentation is not available anymore as the PR was closed or merged._",
"Alright this is ready for review :)\r\nI mostly would like your opinion on the YAML structure and what we can do in the docs (IMO we can add the docs about those fields in the Hub docs). Other than that let me know if the changes in info.py and features.py look good to you",
"LGTM and looking forward to having this merged!! ❤️ ",
"We plan to do a release today, we'll merge this after the release :)\r\n\r\nEDIT: actually tomorrow",
"Created https://github.com/huggingface/datasets/pull/5018 where I added the YAML `dataset_info` of every single dataset in this repo\r\n\r\nsee other dataset cards: [imagenet-1k](https://github.com/huggingface/datasets/blob/040102f100964a33fd334e2695f1c493fa6b92db/datasets/imagenet-1k/README.md), [glue](https://github.com/huggingface/datasets/blob/040102f100964a33fd334e2695f1c493fa6b92db/datasets/glue/README.md), [flores](https://github.com/huggingface/datasets/blob/040102f100964a33fd334e2695f1c493fa6b92db/datasets/flores/README.md), [gem](https://github.com/huggingface/datasets/blob/040102f100964a33fd334e2695f1c493fa6b92db/datasets/gem/README.md)",
"Took your comments into account and updated `push_to_hub` to push the dataset_info to the README.md instead of json :) Let me know if it sounds good to you now !"
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This PR adds a small note about how to load image / audio datasets that have multiple splits in their dataset structure.
Related forum thread: https://discuss.huggingface.co/t/loading-train-and-test-splits-with-audiofolder/22447
cc @NielsRogge
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4925). All of your documentation changes will be reflected on that endpoint.",
"Thanks for the feedback @polinaeterna ! I've reworded the docs a bit to integrate your comments and this should be ready for another review :)",
"> I've just realized that there is another PR about audio documentation open: #4872\r\n> and there the more detailed description on how to use `audiofolder` is moved to another section (\"Create an audio dataset\")\r\n\r\nAh yes, let's add a comment to #4872 - that will be simpler than the alternatives :)",
"@polinaeterna @lhoestq What do you think about adding support for the metadata format from Kaggle (one metadata file for each split with the name equal to the split name) to ImageFolder/AudioFolder? I also think we can relax some requirements a bit by:\r\n* allowing `filename` as the name of the main metadata column (currently, only `file_path` is allowed)\r\n* not requiring that the features of all the given metadata files are equal. Instead, we can have a soft check by using `_check_if_features_can_be_aligned` + `_align_features`. The rationale is that train/val metadata often has extra columns compared to test metadata.\r\n\r\nThese changes would allow us to load the Kaggle dataset linked in the forum thread without any \"interventions\".\r\n\r\nPS: this metadata format for ImageFolder was also proposed by @abhishekkrthakur initially.\r\n",
"Can you give more details about the Kaggle format ? I'm down to discuss it in a separate issue if you don't mind.\r\n\r\n> allowing filename as the name of the main metadata column (currently, only file_path is allowed)\r\n\r\n`filename` refers to the name of the file, so there's no logic about relative path or directories. If I recall correctly this is what we're doing right now so why not\r\n\r\n> not requiring that the features of all the given metadata files are equal. Instead, we can have a soft check by using _check_if_features_can_be_aligned + _align_features. The rationale is that train/val metadata often has extra columns compared to test metadata.\r\n\r\n+1 and we can set to None the missing features",
"I'm not sure if this is worth opening a new issue :).\r\n\r\nWhat I mean by the Kaggle format is the structure like this one (the name of a metadata file is equal to the directory it \"references\"):\r\n```\r\n- train\r\n - img1.jpeg\r\n - img2.jpeg\r\n - ...\r\n- test\r\n - img1.jpeg\r\n - img2.jpeg\r\n - ... \r\n- train.csv\r\n- test.csv\r\n```\r\n\r\n\r\n",
"Sounds nice !",
"@mariosasko +1 to allowing different features set and metadata filenames corresponding to split names\r\n\r\nConsidering filename column - right now it's even called `file_name` now, which is not nice because in fact it's a relative file path indeed, so I think it should be `file_path` (and I don't know why I haven't thought about it before the release...)",
"@lewtun don't you mind if I close this pull request as I've integrated your changes in https://github.com/huggingface/datasets/pull/4872 ? (it doesn't have a link to a kaggle example though)"
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## Describe the bug
When loading the datasets seperately and saving them on disk, I want to concatenate them. But `concatenate_datasets` is filling up my RAM and the process gets killed. Is there a way to prevent this from happening or is this intended behaviour? Thanks in advance
## Steps to reproduce the bug
```python
gcs = gcsfs.GCSFileSystem(project='project')
datasets = [load_from_disk(f'path/to/slice/of/data/{i}', fs=gcs, keep_in_memory=False) for i in range(10)]
dataset = concatenate_datasets(datasets)
```
## Expected results
A concatenated dataset which is stored on my disk.
## Actual results
Concatenated dataset gets loaded into RAM and overflows it which gets the process killed.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 8.0.1
- Pandas version: 1.4.3
|
2022-09-01T11:50:54Z
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Concatenate_datasets loads everything into RAM
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`torchaudio>0.12` fails with decoding mp3 files if `ffmpeg<4`. currently we ask users to downgrade torchaudio, but sometimes it's not possible as torchaudio version is binded to torch version. as a temporary workaround we can decode mp3 with librosa (though it 60 times slower, at least it works)
another option would be to ask users to install the required version of `ffmpeg`, but is non-trivial on colab: it's not in apt packages in ubuntu 18 and `conda` is not preinstalled (with `conda` it would be easily installable)
- [x] decode with torchaudio anyway if the version of ffmpeg is correct? it's 60 times faster
- [x] tests
- [x] DO NOT FORGET to get back all the tests
see https://github.com/huggingface/datasets/issues/4776 and https://github.com/huggingface/datasets/issues/3663#issuecomment-1225797165 (there is a Colab notebook to reproduce the error)
|
2022-11-02T11:54:33Z
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decode mp3 with librosa if torchaudio is > 0.12 as a temporary workaround
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CONTRIBUTOR
|
2022-09-20T13:12:52Z
|
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks ! Should we still support torchaudio>0.12 if it works ? And if it doesn't we can explain that downgrading is the right solution, or alternatively use librosa",
"@lhoestq \r\n\r\n> Should we still support torchaudio>0.12 if it works ? And if it doesn't we can explain that downgrading is the right solution, or alternatively use librosa\r\n\r\nI'm not sure here, because from the one hand, if `torchaudio` works - it works 60 times faster then `librosa`.\r\nBut from the other hand, we will get inconsistent behavior (=different results of decoding) for users of `torchaudio>=0.12`. \r\nI'd better go for using `librosa` only to avoid inconsistency then. wdyt?",
"It seems a bit too constraining to not allow users who have a working torchaudio 0.12 setup to not use it. \r\n\r\nIf the issue is about avoiding silent errors if the decoding changes, maybe we can log which back-end is used ? It can even be a warning with performance suggestions (\"you're using librosa but torchaudio 0.xx is recommended\").\r\n\r\nNote that users can still have a requirements.txt or whatever in their projects if they really want full reproducibility (and it's the bare minimum imo)\r\n\r\nThere are multiple possible back-ends so it's maybe not reasonable to only allow one back-end, especially since each back-end has installation constrains and there's no \"best\" back-end.",
"Woohoo all green ! Feel free to merge if it's all good for you :)"
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## Describe the bug
When trying to load a streaming dataset in Google Colab the loading fails with an I/O error
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION)
list(hf_ds.take(5))
```
## Expected results
It should load five data points
## Actual results
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-13-7b5b8b1e7e58>](https://localhost:8080/#) in <module>
2 from datasets import load_dataset
3 hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION)
----> 4 list(hf_ds.take(5))
6 frames
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self)
716
717 def __iter__(self):
--> 718 for key, example in self._iter():
719 if self.features:
720 # `IterableDataset` automatically fills missing columns with None.
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in _iter(self)
706 else:
707 ex_iterable = self._ex_iterable
--> 708 yield from ex_iterable
709
710 def _iter_shard(self, shard_idx: int):
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self)
582
583 def __iter__(self):
--> 584 yield from islice(self.ex_iterable, self.n)
585
586 def shuffle_data_sources(self, generator: np.random.Generator) -> "TakeExamplesIterable":
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self)
110
111 def __iter__(self):
--> 112 yield from self.generate_examples_fn(**self.kwargs)
113
114 def shuffle_data_sources(self, generator: np.random.Generator) -> "ExamplesIterable":
[~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _generate_examples(self, split_subsets, extraction_map, with_translation)
845 raise ValueError("Invalid number of files: %d" % len(files))
846
--> 847 for sub_key, ex in sub_generator(*sub_generator_args):
848 if not all(ex.values()):
849 continue
[~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _parse_parallel_sentences(f1, f2, filename1, filename2)
923 l2_sentences, l2 = parse_file(f2_i, filename2)
924
--> 925 for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)):
926 key = f"{f_id}/{line_id}"
927 yield key, {l1: s1, l2: s2}
[~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in gen()
895
896 def gen():
--> 897 with open(path, encoding="utf-8") as f:
898 for line in f:
899 seg_match = re.match(seg_re, line)
ValueError: I/O operation on closed file.
```
## Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.4.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 9.0.0. (the same error happened with PyArrow version 6.0.0)
- Pandas version: 1.3.5
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I/O error on Google Colab in streaming mode
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Fix missing tags in dataset cards:
- eraser_multi_rc
- hotpot_qa
- metooma
- movie_rationales
- qanta
- quora
- quoref
- race
- ted_hrlr
- ted_talks_iwslt
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
- #4908
|
2022-09-22T14:34:11Z
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## Describe the bug
Unable to load local tsv files through load_dataset method.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
data_files = {
'train': 'train.tsv',
'test': 'test.tsv'
}
raw_datasets = load_dataset('tsv', data_files=data_files)
## Expected results
I am pretty sure the data files exist in the current directory. The above code should load them as Datasets, but threw exceptions.
## Actual results
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
[<ipython-input-9-24207899c1af>](https://localhost:8080/#) in <module>
----> 1 raw_datasets = load_dataset('tsv', data_files='train.tsv')
2 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1244 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1245 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
-> 1246 ) from None
1247 raise e1 from None
1248 else:
FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/tsv/tsv.py
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
|
2022-09-01T05:31:30Z
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2022-08-31T16:13:39Z
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Unable to load local tsv files through load_dataset method
|
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2022-09-01T05:31:30Z
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I_kwDODunzps5Q6sqt
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[
"Hi @DataNoob0723,\r\n\r\nUnder the hood, we use `pandas` to load CSV/TSV files. Therefore, you should use \"csv\" and pass `sep=\"\\t\"`, as explained in our docs: https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/loading_methods#from-files\r\n```python\r\nds = load_dataset('csv', sep=\"\\t\", data_files=data_files)\r\n``` "
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Hi @lhoestq what do you think?
Let me give you a code sample:
```py
>>> import datasets
>>> foo = datasets.Dataset.from_dict({'foo':[0,1], 'bar':[2,3]})
>>> foo.save_to_disk('foo')
# edit foo/dataset_info.json e.g. rename the 'foo' feature to 'baz'
>>> datasets.load_from_disk('foo')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-4863e606b330> in <module>
----> 1 datasets.load_from_disk('foo')
~/code/datasets/src/datasets/load.py in load_from_disk(dataset_path, fs, keep_in_memory)
1851 raise FileNotFoundError(f"Directory {dataset_path} not found")
1852 if fs.isfile(Path(dest_dataset_path, config.DATASET_INFO_FILENAME).as_posix()):
-> 1853 return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)
1854 elif fs.isfile(Path(dest_dataset_path, config.DATASETDICT_JSON_FILENAME).as_posix()):
1855 return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)
~/code/datasets/src/datasets/arrow_dataset.py in load_from_disk(dataset_path, fs, keep_in_memory)
1230 info=dataset_info,
1231 split=split,
-> 1232 fingerprint=state["_fingerprint"],
1233 )
1234
~/code/datasets/src/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)
687 self.info.features = inferred_features
688 else: # make sure the nested columns are in the right order
--> 689 self.info.features = self.info.features.reorder_fields_as(inferred_features)
690
691 # Infer fingerprint if None
~/code/datasets/src/datasets/features/features.py in reorder_fields_as(self, other)
1771 return source
1772
-> 1773 return Features(recursive_reorder(self, other))
1774
1775 def flatten(self, max_depth=16) -> "Features":
~/code/datasets/src/datasets/features/features.py in recursive_reorder(source, target, stack)
1760 f"{source.keys()-target.keys()} are missing from dataset.arrow "
1761 f"and {target.keys()-source.keys()} are missing from dataset_info.json"+stack_position)
-> 1762 raise ValueError(message)
1763 return {key: recursive_reorder(source[key], target[key], stack + f".{key}") for key in target}
1764 elif isinstance(source, list):
ValueError: Keys mismatch: between {'baz': Value(dtype='int64', id=None), 'bar': Value(dtype='int64', id=None)} (dataset_info.json) and {'foo': Value(dtype='int64', id=None), 'bar': Value(dtype='int64', id=None)} (inferred from dataset.arrow).
{'baz'} are missing from dataset.arrow and {'foo'} are missing from dataset_info.json
```
|
2022-09-05T08:46:01Z
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2022-08-31T14:41:36Z
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https://api.github.com/repos/huggingface/datasets/issues/4919/comments
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https://api.github.com/repos/huggingface/datasets/issues/4919/timeline
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feat: improve error message on Keys mismatch. closes #4917
|
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CONTRIBUTOR
|
2022-09-05T08:43:33Z
|
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PR_kwDODunzps4-IxDZ
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"We are having an unrelated issue that makes several tests fail. We are working on that. Once fixed, you will be able to merge the main branch into this, so that you get the fix and the tests pass..."
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### Link
https://huggingface.co/datasets/pysentimiento/spanish-targeted-sentiment-headlines
### Description
After moving the dataset from my user (`finiteautomata`) to the `pysentimiento` organization, the dataset viewer says that it doesn't exist.
### Owner
_No response_
|
2022-09-05T21:36:34Z
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2022-08-31T12:09:07Z
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Dataset Viewer issue for pysentimiento/spanish-targeted-sentiment-headlines
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2022-09-05T16:32:44Z
| null |
I_kwDODunzps5Q5eGF
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[
"Thanks for reporting, it's fixed now (I refreshed it manually). It's a known issue; we hope it will be fixed permanently in a few days.\r\n\r\n<img width=\"1508\" alt=\"Capture d’écran 2022-09-05 à 18 31 22\" src=\"https://user-images.githubusercontent.com/1676121/188489762-0ed86a7e-dfb3-46e8-a125-43b815a2c6f4.png\">\r\n",
"Thanks @severo! "
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**Is your feature request related to a problem? Please describe.**
When loading a dataset from disk with a defect in its `dataset_info.json` describing its features (I don’t know when/why/how this happens but it deserves its own issue), you will get an error message like:
`ValueError: Keys mismatch: between {'bar': Value(dtype='int64', id=None)} and {'foo': Value(dtype='int64', id=None)}`
Which is fine when you have only a few features like in the example but it gets very hard to read when you have a lot of features in your dataset.
**Describe the solution you'd like**
The error message should give the difference between the features (what keys are in A but missing in B and vice-versa). It should also tell which keys are inferred from `dataset.arrow` and which come from `dataset_info.json`.
Willing to help :)
|
2022-09-05T08:43:38Z
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2022-08-31T11:24:34Z
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Keys mismatch: make error message more informative
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CONTRIBUTOR
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2022-09-05T08:43:38Z
| null |
I_kwDODunzps5Q5SJx
|
[
"Good idea ! I think this can be improved in `Features.reorder_fields_as()` indeed at\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/features/features.py#L1739-L1740\r\n\r\nIs it something you would be interested in contributing ?",
"Is this open to work on? I'd love to take on this as my first issue.",
"Hi @daspartho I’ve opened a PR #4919 \r\nI don’t think there’s much left to do",
"ok : )"
] |
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## Describe the bug
Hi, I am currently trying to download wikipedia dataset using
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download. It downloads the file but while saving it in hugging face cache it fails to write. This happens for any available date of any language in wikipedia dump. I had raised another issue earlier #4915 but probably was not that clear and the solution provider misunderstood my problem. Hence raising one more issue. Any help is appreciated.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner')
```
## Expected results
to load the dataset
## Actual results
I am pasting the error trace here:
Downloading builder script: 35.9kB [00:00, ?B/s]
Downloading metadata: 30.4kB [00:00, 1.94MB/s]
Using custom data configuration 20220401.aa-date=20220401,language=aa
Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it]
Traceback (most recent call last):
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "G:/abc/temp.py", line 32, in
beam_runner='DirectRunner')
File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare
pipeline_results = pipeline.run()
File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run
return self.runner.run_pipeline(self, self._options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline
return runner.run_pipeline(pipeline, options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline
options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api
return self.run_stages(stage_context, stages)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages
runner_execution_context, bundle_context_manager, bundle_input)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle
bundle_manager))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle
data_input, data_output, input_timers, expected_timer_output)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle
result_future = self._worker_handler.control_conn.push(process_bundle_req)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push
response = self.worker.do_instruction(request)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction
getattr(request, request_type), request.instruction_id)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle
element.data)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded
self.output(decoded_value)
File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 324, in apache_beam.runners.worker.operations.GeneralPurposeConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 905, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1507, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
## Environment info
Python: 3.7.6
Windows 10 Pro
datasets :2.4.0
apache_beam: 2.41.0
mwparserfromhell: 0.6.4
|
2022-08-31T10:53:19Z
| 4,916
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https://api.github.com/repos/huggingface/datasets
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2022-08-31T09:39:25Z
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Apache Beam unable to write the downloaded wikipedia dataset
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2022-08-31T10:53:19Z
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## Describe the bug
Hi, I am currently trying to download wikipedia dataset using
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download.
Environment:
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner')
```
## Expected results
to load the dataset
## Actual results
I am pasting the error trace here:
Downloading builder script: 35.9kB [00:00, ?B/s]
Downloading metadata: 30.4kB [00:00, 1.94MB/s]
Using custom data configuration 20220401.aa-date=20220401,language=aa
Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it]
Traceback (most recent call last):
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "G:/abc/temp.py", line 32, in <module>
beam_runner='DirectRunner')
File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare
pipeline_results = pipeline.run()
File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run
return self.runner.run_pipeline(self, self._options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline
return runner.run_pipeline(pipeline, options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline
options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api
return self.run_stages(stage_context, stages)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages
runner_execution_context, bundle_context_manager, bundle_input)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle
bundle_manager))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle
data_input, data_output, input_timers, expected_timer_output)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle
result_future = self._worker_handler.control_conn.push(process_bundle_req)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push
response = self.worker.do_instruction(request)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction
getattr(request, request_type), request.instruction_id)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle
element.data)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded
self.output(decoded_value)
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File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
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File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
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File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__
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File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
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temp_path, self.mime_type, self.compression_type)
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return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
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File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
## Environment info
Python: 3.7.6
Windows 10 Pro
datasets :2.4.0
apache_beam: 2.41.0
mwparserfromhell: 0.6.4
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2022-12-04T22:20:33Z
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FileNotFoundError while downloading wikipedia dataset for any language
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I_kwDODunzps5Q0w5S
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"Hi @Shilpac20,\r\n\r\nAs explained in the Wikipedia dataset card: https://huggingface.co/datasets/wikipedia\r\n> You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).\r\n\r\nThis means that, before passing a specific date, you should first make sure it is available online, as Wikimedia only keeps last X months (depending on the size of the corresponding language dump)): e.g. to see which dates \"aa\" Wikipedia is available online, see https://dumps.wikimedia.org/aawiki/ (as of today 2022-08-31, the available dates are from [20220401](https://dumps.wikimedia.org/aawiki/20220401/) to [20220820](https://dumps.wikimedia.org/aawiki/20220820/)).",
"Hi, the date that I have specified \"20220401\" is available for the language \"aa\". The error persists for any other available dates as present in https://dumps.wikimedia.org/aawiki/. The error is mainly due to apache beam not able to write the downloaded files. Any help on this?",
"I see, sorry, I misread your issue.\r\n\r\nWe are investigating this.",
"I am struggling with basically the same issue. I am trying to download the German Wikipedia dump.\r\n\r\nAs per the [documentation](https://huggingface.co/datasets/wikipedia), `\"20220301.de\"` should be available as a pre-processed dataset.\r\n\r\nIssuing the command mentioned in the documentation cited above\r\n\r\n from datasets import load_dataset\r\n load_dataset(\"wikipedia\", \"20220301.de\")\r\n\r\nraises the following `FileNotFound` error\r\n\r\n FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/dewiki/20220301/dumpstatus.json\r\n\r\nUsing the ([undocumented](https://huggingface.co/docs/datasets/v1.2.1/package_reference/loading_methods.html#datasets.load_dataset)?) call to `load_dataset()` with `language` and `date` parameters\r\n\r\n load_dataset(\"wikipedia\", language=\"de\", date=\"20220301\", beam_runner=\"DirectRunner\")\r\n\r\nproduces the same error.\r\n\r\nEDIT: as I am using `datasets` v2.7.1, I should be looking at [that version's documentation](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/loading_methods#datasets.load_dataset)! It is mentioned there, that additional `kwargs` are \"passed to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/builder_classes#datasets.BuilderConfig) and used in the [DatasetBuilder](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/builder_classes#datasets.DatasetBuilder)\". So I guess that is how `language` and `date` are used.\r\n\r\nAs I can see a folder `20221130` on `https://dumps.wikimedia.org/dewiki/`, I also tried\r\n\r\n from datasets import load_dataset\r\n load_dataset(\"wikipedia\", \"20221130.de\")\r\n\r\nwhich throws another error:\r\n\r\n ValueError: BuilderConfig 20221120.de not found. Available: ['20220301.aa', ... '20220301.de', ...\r\n\r\nbasically telling me that the dataset I originally requested (`'20220301.de'`) is available...\r\n\r\nIt seems that `load_dataset` is not handling the vanishing older dumps for Wikipedia correctly?",
"I am able to start downloading the dataset when trying anything with the recent dumps for 20221201. But obviously, those are the big wiki dumps and I need the smaller preloaded version.\r\n\r\nI am now getting some error when the files show up in my cache but it will say FileNotFoundError at the end of the download for some reason. The cache directory to the datasets\\wikipedia\\date.bn\\ had something in it, then when the error came up it disappeared. \r\n\r\nIt is easy to test with the langauge \"bn\" because the amount of files is low.\r\n\r\ndataset = load_dataset('wikipedia', date=\"20221201\", language=\"bn\", split='train', beam_runner='DirectRunner')"
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This PR adds the license information to `cosmos_qa` dataset, once reported via email by Yejin Choi, the dataset is licensed under CC BY 4.0.
This PR also updates the citation information.
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2022-08-30T09:49:31Z
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**1. Background**
Huggingface datasets package advises using `map()` to process data in batches. In the example code on pretraining masked language model, they use `map()` to tokenize all data at a stroke before the train loop.
The corresponding code:
```
with accelerator.main_process_first():
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on every text in dataset"
)
```
**2. The problem**
Thus, when I try the same pertaining code with a much larger corpus, it takes quite a long time to tokenize.
Also, we can choose to tokenize data in `data-collator`. In this way, the program only tokenizes one batch in the next training step and avoids getting stuck in tokenization.
**3. My question**
As described above, my questions are:
* **Which is better? Process in `map()` or in `data-collator`**
* **Why huggingface advises `map()` function?** There should be some advantages to using `map()`
Thanks for your answers!
|
2023-04-06T09:43:58Z
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datasets map() handles all data at a stroke and takes long time
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"Hi ! Interesting question ;)\r\n\r\n> Which is better? Process in map() or in data-collator\r\n\r\nAs you said, both can be used in practice: map() if you want to preprocess before training, or a data-collator (or the equivalent `dataset.set_transform`) if you want to preprocess on-the-fly during training. Both options are great and really depend on your case.\r\n\r\nTo choose between the two, here are IMO the main caveats of each approach:\r\n- if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n- on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\n> Why huggingface advises map() function? There should be some advantages to using map()\r\n\r\nTo get the best throughput when training a model, it is often recommended to preprocess your dataset before training. Note that preprocessing may include other steps before tokenization such as data filtering, cleaning, chunking etc. which are often done before training.",
"Thanks for your clear explanation @lhoestq ! \r\n> * if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n> * on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\nI really agree with you. There should be some trade-off between processing before and during the train loop.\r\nBesides, I find `map()` function can cache the results once it has been executed. Very useful!",
"I'm closing this issue if you don't mind, feel free to reopen if needed ;)",
"@lhoestq How to preprocess on-the-fly during training？my data is about 1w hours, when I use map to preprocess, and It's not finished yet， but all disk space(2T) is full.",
"Hi ! You can do that using `set_transform`, see https://huggingface.co/docs/datasets/process#format-transform for more info :)",
"unfortunately , it not work.",
"Could you share more details ?"
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On https://hf.co/datasets you can rename a dataset (or sometimes move it to another user/org). The website handles redirections correctly and AFAIK `datasets` does as well.
However it would be nice to have an integration test to make sure we don't break support for renamed datasets.
To implement this we can use the /api/repos/move endpoint on hub-ci to rename/move a repo (it is documented at https://huggingface.co/docs/hub/api)
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## Describe the bug
In `load_dataset_builder()`, `build_kwargs` and `config_kwargs` can contain the same keywords leading to a TypeError("type object got multiple values for keyword argument "xyz").
I ran into this problem with the keyword: `base_path`. It might happen with other kwargs as well. I think a quickfix would be
```python
builder_cls = import_main_class(dataset_module.module_path)
builder_kwargs = dataset_module.builder_kwargs
data_files = builder_kwargs.pop("data_files", data_files)
config_name = builder_kwargs.pop("config_name", name)
hash = builder_kwargs.pop("hash")
base_path = builder_kwargs.pop("base_path")
```
and then pass base_path into `builder_cls`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("rotten_tomatoes", base_path="./sample_data")
```
## Expected results
The docs state: `**config_kwargs` — Keyword arguments to be passed to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.BuilderConfig) and used in the [DatasetBuilder](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.DatasetBuilder).
So I would expect to be able to pass the base_path into `load_dataset()`.
## Actual results
TypeError("type object got multiple values for keyword argument "base_path").
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.8.9
- PyArrow version: 9.0.0
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[
"I am getting similar error - `TypeError: type object got multiple values for keyword argument 'name'` while following this [tutorial](https://huggingface.co/docs/datasets/dataset_script#create-a-dataset-loading-script). I am getting this error with the `dataset-cli test` command.\r\n\r\n`datasets` version: 2.4.0",
"In my case, this was happening because I defined multiple `BuilderConfig` for multiple types, but didn't had all the data files that are requierd by those configs. \r\n\r\nI think this is different than the original issue by @bablf .",
"Hi ! I think this can be fixed by letting the config_kwargs take over the builder kwargs here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/load.py#L1533-L1534\r\n\r\nmaybe something like this ?\r\n```python\r\n **{**builder_kwargs, **config_kwargs}\r\n```\r\n\r\nLet me know if you'd like to contribute and fix this bug, so I can assign you :)\r\n\r\n> In my case, this was happening because I defined multiple BuilderConfig for multiple types, but didn't had all the data files that are requierd by those configs.\r\n> \r\n> I think this is different than the original issue by @bablf .\r\n\r\nFeel free to to open an new issue, I'd be happy to help\r\n",
"@lhoestq Yeah, I want to, please assign.",
"Cool thank you ! Let me know if you have questions or if I can help",
"@lhoestq On second thoughts, I think this might be expected behavior; although a better error message might help.\r\n\r\nReasoning: Given n configs, if no data file is provided for any config, then it should be an error. Then why it should not be the case if out of n configs, for some data files are provided but not for others. Also, I was using `--all_configs` flag with `dataset-cli test`.",
"Ok I see - maybe we should check the values of builder_kwargs raise an error if any key in config_kwargs tries to overwrite it ? The builder kwargs are determined from the builder's type and location (in some cases it forces the base_path, data_files and config name for example)"
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This PR updates the evaluation metadata for GLUE to:
* Include defaults for all configs except `ax` (which only has a `test` split with no known labels)
* Fix the default split from `test` to `validation` since `test` splits in GLUE have no labels (they're private)
* Fix the `task_id` for some existing defaults
cc @sashavor @douwekiela
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Update GLUE evaluation metadata
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Fix missing tags in dataset cards:
- asnq
- clue
- common_gen
- cosmos_qa
- guardian_authorship
- hindi_discourse
- py_ast
- x_stance
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
|
2022-09-22T14:35:56Z
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Fix missing tags in dataset cards
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## Describe the bug
I got `'NoneType' object is not callable` error while calling the swda datasets.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("swda")
```
## Expected results
Run without error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Python version: 3.8.10
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2022-08-30T14:43:41Z
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2022-08-29T07:05:20Z
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None Type error for swda datasets
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2022-08-30T14:43:41Z
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"Thanks for reporting @hannan72 ! I couldn't reproduce the error on my side, can you share the full stack trace please ?",
"Thanks a lot for your response @lhoestq \r\nThe problem is solved accidentally today and I don't know exactly why it was happened yesterday.\r\nThe issue can be closed.",
"Ok, let us know if you encounter the issue again ;)"
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## Describe the bug
A clear and concise description of what the bug is.
Not able to import datasets
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import os
os.environ["WANDB_API_KEY"] = "0" ## to silence warning
import numpy as np
import random
import sklearn
import matplotlib.pyplot as plt
import pandas as pd
import sys
import tensorflow as tf
import plotly.express as px
import transformers
import tokenizers
import nlp as nlp
import utils
import datasets
```
## Expected results
A clear and concise description of the expected results.
import should work normal
## Actual results
Specify the actual results or traceback.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-21-b3b5b0b62103> in <module>
13 import nlp as nlp
14 import utils
---> 15 import datasets
~\anaconda3\lib\site-packages\datasets\__init__.py in <module>
44 from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
45 from .info import DatasetInfo, MetricInfo
---> 46 from .inspect import (
47 get_dataset_config_info,
48 get_dataset_config_names,
~\anaconda3\lib\site-packages\datasets\inspect.py in <module>
28 from .download.streaming_download_manager import StreamingDownloadManager
29 from .info import DatasetInfo
---> 30 from .load import dataset_module_factory, import_main_class, load_dataset_builder, metric_module_factory
31 from .utils.file_utils import relative_to_absolute_path
32 from .utils.logging import get_logger
~\anaconda3\lib\site-packages\datasets\load.py in <module>
53 from .iterable_dataset import IterableDataset
54 from .metric import Metric
---> 55 from .packaged_modules import (
56 _EXTENSION_TO_MODULE,
57 _MODULE_SUPPORTS_METADATA,
~\anaconda3\lib\site-packages\datasets\packaged_modules\__init__.py in <module>
4 from typing import List
5
----> 6 from .csv import csv
7 from .imagefolder import imagefolder
8 from .json import json
~\anaconda3\lib\site-packages\datasets\packaged_modules\csv\csv.py in <module>
13
14
---> 15 logger = datasets.utils.logging.get_logger(__name__)
16
17 _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS = ["names", "prefix"]
AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0
- Python version: 3.8.8
- PyArrow version: 9.0.0
- Pandas version: 1.2.4
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2023-10-27T20:08:28Z
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2022-08-28T02:23:24Z
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Can't import datasets AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)
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2022-10-03T12:22:50Z
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[
"Thanks for reporting, @OPterminator.\r\n\r\nHowever, we are not able to reproduce this issue.\r\n\r\nThere might be 2 reasons why you get this exception:\r\n- Either the name of your local Python file: if it is called `datasets.py` this could generate a circular import when trying to import the Hugging Face `datasets` library.\r\n - You could try to rename it and run it again.\r\n- Another cause could be the simultaneous use of the packages `nlp` and `datasets`. Please note that we renamed the Hugging Face `nlp` library to `datasets` more than 2 years ago: they are 2 versions of the same library.\r\n - Please try to update your script and use only `datasets` (`nlp` name is no longer in use and is out of date).",
"i am also facing this issue\r\n\r\n\r\n```\r\n----> 1 import datasets\r\n 3 dataset = datasets.load_dataset(\"ucberkeley-dlab/measuring-hate-speech\", \"binary\")\r\n 4 df = dataset[\"train\"].to_pandas()\r\n\r\nFile ~/.pyenv/versions/3.10.9/lib/python3.10/site-packages/datasets/__init__.py:52\r\n 50 from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled\r\n 51 from .info import DatasetInfo, MetricInfo\r\n---> 52 from .inspect import (\r\n 53 get_dataset_config_info,\r\n 54 get_dataset_config_names,\r\n 55 get_dataset_infos,\r\n 56 get_dataset_split_names,\r\n 57 inspect_dataset,\r\n 58 inspect_metric,\r\n 59 list_datasets,\r\n 60 list_metrics,\r\n 61 )\r\n 62 from .iterable_dataset import IterableDataset\r\n 63 from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric\r\n\r\nFile ~/.pyenv/versions/3.10.9/lib/python3.10/site-packages/datasets/inspect.py:30\r\n 28 from .download.streaming_download_manager import StreamingDownloadManager\r\n...\r\n---> 16 logger = datasets.utils.logging.get_logger(__name__)\r\n 19 if datasets.config.PYARROW_VERSION.major >= 7:\r\n 21 def pa_table_to_pylist(table):\r\n```",
"I am facing the same question. And this happens when i installing `evaluate` package while `jupyter notebook` running. I'm not sure if the error occured because of trying to import the package installed when the notebook is running. Surpringly when i stop the notebook and rerun, the issue has been solved itself. Hope this will be helpful : )",
"I also got this error.\r\nIt helped me to find the python process and kill it, then restart the kernel and the error disappeared.",
"> I also got this error. It helped me to find the python process and kill it, then restart the kernel and the error disappeared.\r\n\r\nYes!",
"> I am facing the same question. And this happens when i installing `evaluate` package while `jupyter notebook` running. I'm not sure if the error occured because of trying to import the package installed when the notebook is running. Surpringly when i stop the notebook and rerun, the issue has been solved itself. Hope this will be helpful : )\r\n\r\nThank you! :)"
] |
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We define the keys for the `_DL_URLS` of the dev split as `dev.clean` and `dev.other`:
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L60-L61
These keys get forwarded to the `dl_manager` and thus the `local_extracted_archive`.
However, when calling `SplitGenerator` for the dev sets, we query the `local_extracted_archive` keys `validation.clean` and `validation.other`:
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L212
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L219
The consequence of this is that the `local_extracted_archive` arg passed to `_generate_examples` is always `None`, as the keys `validation.clean` and `validation.other` do not exists in the `local_extracted_archive`.
When defining the `audio_file` in `_generate_examples`, since `local_extracted_archive` is always `None`, we always omit the `local_extracted_archive` path from the `audio_file` path, **even** if in non-streaming mode:
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L259-L263
Thus, `audio_file` will only ever be the streaming path (`audio_file`, not `os.path.join(local_extracted_archive, audio_file)`).
This PR fixes the `.get()` keys for the `local_extracted_archive` for the dev splits.
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2022-08-30T10:06:21Z
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[LibriSpeech] Fix dev split local_extracted_archive for 'all' config
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2022-08-30T10:03:25Z
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Fix CI so that it reports defaults (failed and error) besides the custom (xfailed and xpassed) in the test summary.
This PR fixes a regression introduced by:
- #4845
This introduced the reporting of xfailed and xpassed, but wrongly removed the reporting of the defaults failed and error.
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2022-08-26T17:49:33Z
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Fix CI reporting
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Currently, if a dataset has no configuration, a default configuration is created from the dataset name.
For example, for a dataset loaded from the hub repository, such as https://huggingface.co/datasets/user/dataset (repo id is `user/dataset`), the default configuration will be `user--dataset`.
It might be easier to handle to set it to `default`, or another reserved word.
|
2023-07-24T21:15:31Z
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2022-08-26T16:16:22Z
|
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Name the default config `default`
|
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CONTRIBUTOR
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2023-07-24T21:15:31Z
| null |
I_kwDODunzps5QnQrM
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[
"Addressed in #5331."
] |
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This PRs raises a specific `ManualDownloadError` when `get_dataset_config_info` is called for a dataset that requires manual download.
Related to:
- #4898
CC: @severo
|
2022-08-30T10:42:21Z
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Raise ManualDownloadError from get_dataset_config_info
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2023-07-24T15:42:09Z
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Dataset Viewer issue for asaxena1990/Dummy_dataset
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2023-07-24T15:42:09Z
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"Seems to be linked to the use of the undocumented `_resolve_features` method in the dataset viewer backend:\r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"asaxena1990/Dummy_dataset\", name=\"asaxena1990--Dummy_dataset\", split=\"train\", streaming=True)\r\nUsing custom data configuration asaxena1990--Dummy_dataset-4a704ed7e5627563\r\n>>> dataset._resolve_features()\r\nFailed to read file 'https://huggingface.co/datasets/asaxena1990/Dummy_dataset/resolve/06885879a8bdd767d2d27695484fc6c83244617a/dummy_dataset_train.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column() changed from object to array in row 0\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py\", line 109, in _generate_tables\r\n pa_table = paj.read_json(\r\n File \"pyarrow/_json.pyx\", line 246, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 1261, in _resolve_features\r\n features = _infer_features_from_batch(self._head())\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 686, in _head\r\n return _examples_to_batch([x for key, x in islice(self._iter(), n)])\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 686, in <listcomp>\r\n return _examples_to_batch([x for key, x in islice(self._iter(), n)])\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 708, in _iter\r\n yield from ex_iterable\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 112, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 651, in wrapper\r\n for key, table in generate_tables_fn(**kwargs):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py\", line 137, in _generate_tables\r\n f\"This JSON file contain the following fields: {str(list(dataset.keys()))}. \"\r\nAttributeError: 'list' object has no attribute 'keys'\r\n```\r\n\r\nPinging @huggingface/datasets",
"Hi ! JSON files containing a list of object are not supported yet, you can use JSON Lines files instead in the meantime\r\n```json\r\n{\"text\": \"can I know this?\", \"intent\": \"Know\", \"type\": \"Test\"}\r\n{\"text\": \"can I know this?\", \"intent\": \"Know\", \"type\": \"Test\"}\r\n...\r\n```",
"A JSON list of objects is supported as of version 2.5.0."
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This PR fixes the removal of 2 language tags done by:
- #4882
The tags are:
- "code": this is not a IANA tag but needed
- "und": this is one of the special scoped tags removed by 0d53202b9abce6fd0358cb00d06fcfd904b875af
- used in "mc4" and "udhr" datasets
|
2022-08-26T10:27:18Z
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2022-08-26T09:48:57Z
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Re-add code and und language tags
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2022-10-03T12:40:28Z
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Dataset Viewer issue for timit_asr
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2022-10-03T12:40:27Z
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I_kwDODunzps5Qk5z2
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[
"Yes, the dataset viewer is based on `datasets`, and the following does not work:\r\n\r\n```\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names('timit_asr')\r\nDownloading builder script: 7.48kB [00:00, 6.69MB/s]\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/timit_asr/43f9448dd5db58e95ee48a277f466481b151f112ea53e27f8173784da9254fb2/timit_asr.py\", line 117, in _split_generators\r\n data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))\r\n File \"/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/posixpath.py\", line 231, in expanduser\r\n path = os.fspath(path)\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\ncc @huggingface/datasets ",
"Due to license restriction, this dataset needs manual downloading of the original data.\r\n\r\nThis information is in the dataset card: https://huggingface.co/datasets/timit_asr\r\n> The dataset needs to be downloaded manually from https://catalog.ldc.upenn.edu/LDC93S1",
"Maybe a better error message for datasets that need manual downloading? @severo \r\n\r\nMaybe we can raise a specific excpetion as done from `load_dataset`...",
"Yes, ideally something like https://github.com/huggingface/datasets/blob/main/src/datasets/builder.py#L81\r\n",
"The preview is now disabled (and a descriptive warning is displayed) for datasets requiring manual download. See:\r\n\r\n\r\n"
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Checking the large file in disk, and found the large cache file in the cifar10 data directory:
As we know, the size of cifar10 dataset is ~130MB, but the cache file has almost 30GB size, there may be some problems here.
|
2022-09-18T05:07:52Z
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datasets generate large arrow file
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2022-09-18T05:07:52Z
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I_kwDODunzps5QkpkX
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[
"Hi ! The cache files are the results of all the transforms you applied to the dataset using `map` for example.\r\nDid you run a transform that could potentially blow up the size of the dataset ?",
"@lhoestq,\r\nI don't remember, but I can't imagine what kind of transform may generate data that grow over 200 times in size. \r\nI think maybe it doesn' matter, it's just cache after all."
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Fix missing tags in dataset cards:
- anli
- coarse_discourse
- commonsense_qa
- cos_e
- ilist
- lc_quad
- web_questions
- xsum
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
|
2022-09-22T14:37:16Z
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## Describe the bug
The `datasets.load_dataset` returns a `ValueError: Unknown split "validation". Should be one of ['train', 'test'].` when running `load_dataset(local_data_dir_path, split="validation")` even if the `validation` sub-directory exists in the local data path.
The data directories are as follows and attached to this issue:
```
test_data1
|_ train
|_ 1012.png
|_ metadata.jsonl
...
|_ test
...
|_ validation
|_ 234.png
|_ metadata.jsonl
...
test_data2
|_ train
|_ train_1012.png
|_ metadata.jsonl
...
|_ test
...
|_ validation
|_ val_234.png
|_ metadata.jsonl
...
```
They contain the same image files and `metadata.jsonl` but the images in `test_data2` have the split names prepended i.e.
`train_1012.png, val_234.png` and the images in `test_data1` do not have the split names prepended to the image names i.e. `1012.png, 234.png`
I actually saw in another issue `val` was not recognized as a split name but here I would expect the files to take the split from the parent directory name i.e. val should become part of the validation split?
## Steps to reproduce the bug
```python
import datasets
datasets.logging.set_verbosity_error()
from datasets import load_dataset, get_dataset_split_names
# the following only finds train, validation and test splits correctly
path = "./test_data1"
print("######################", get_dataset_split_names(path), "######################")
dataset_list = []
for spt in ["train", "test", "validation"]:
dataset = load_dataset(path, split=spt)
dataset_list.append(dataset)
# the following only finds train and test splits
path = "./test_data2"
print("######################", get_dataset_split_names(path), "######################")
dataset_list = []
for spt in ["train", "test", "validation"]:
dataset = load_dataset(path, split=spt)
dataset_list.append(dataset)
```
## Expected results
```
###################### ['train', 'test', 'validation'] ######################
###################### ['train', 'test', 'validation'] ######################
```
## Actual results
```
Traceback (most recent call last):
File "test_data_loader.py", line 11, in <module>
dataset = load_dataset(path, split=spt)
File "/home/venv/lib/python3.8/site-packages/datasets/load.py", line 1758, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 893, in as_dataset
datasets = map_nested(
File "/home/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 385, in map_nested
return function(data_struct)
File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 924, in _build_single_dataset
ds = self._as_dataset(
File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 993, in _as_dataset
dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 211, in read
files = self.get_file_instructions(name, instructions, split_infos)
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 184, in get_file_instructions
file_instructions = make_file_instructions(
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 107, in make_file_instructions
absolute_instructions = instruction.to_absolute(name2len)
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in to_absolute
return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions]
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in <listcomp>
return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions]
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 433, in _rel_to_abs_instr
raise ValueError(f'Unknown split "{split}". Should be one of {list(name2len)}.')
ValueError: Unknown split "validation". Should be one of ['train', 'test'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux Ubuntu 18.04
- Python version: 3.8.12
- PyArrow version: 9.0.0
Data files
[test_data1.zip](https://github.com/huggingface/datasets/files/9424463/test_data1.zip)
[test_data2.zip](https://github.com/huggingface/datasets/files/9424468/test_data2.zip)
|
2024-03-26T16:47:48Z
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load_dataset method returns Unknown split "validation" even if this dir exists
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2022-09-29T08:07:50Z
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[
"I don't know the main problem but it looks like, it is ignoring the last directory in your case. So, create a directory called 'zzz' in the same folder as train, validation and test. if it doesn't work, create a directory called \"aaa\". It worked for me.\r\n",
"@SamSamhuns could you please try to load it with the current main-branch version of `datasets`? I suppose the problem is that it tries to get splits names from filenames in this case, ignoring directories names, but `val` wasn't in keywords at that time, but it was fixed recently in this PR https://github.com/huggingface/datasets/pull/4844. ",
"I have a similar problem.\r\nWhen I try to create `data_infos.json` using `datasets-cli test Peter.py --save_infos --all_configs` I get an error:\r\n`ValueError: Unknown split \"test\". Should be one of ['train'].`\r\n\r\nThe `data_infos.json` is created perfectly fine when I use only one split - `datasets.Split.TRAIN`\r\n\r\n@polinaeterna Could you help here please?\r\n\r\nYou can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch)",
"@skalinin It seems the `dataset_infos.json` of your dataset is missing the info on the test split (and `datasets-cli` doesn't ignore the cached infos at the moment, which is a known bug), so your issue is not related to this one. I think you can fix your issue by deleting all the cached `dataset_infos.json` (in the local repo and in `~/.cache/huggingface/modules`) before running the `datasets-cli test` command. Let us know if that doesn't help, and I can try to generate it myself.",
"This code indeed behaves as expected on `main`. But suppose the `val_234.png` is renamed to some other value not containing one of [these](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L31) keywords, in that case, this issue becomes relevant again because the real cause of it is the order in which we check the predefined split patterns to assign data files to each split - first we assign data files based on filenames, and only if this fails meaning not a single split found (`val` is not recognized here in the older versions of `datasets`, which results in an empty `validation` split), do we assign based on directory names.\r\n\r\n@polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if `data_dir` is specified (or if `load_dataset(data_dir)` is called)? ",
"> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nyes that makes sense !",
"Looks like the `val/validation` dir name issue is fixed with the current main-branch version of the `datasets` repository. \r\n\r\n> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nI agree with this as well. I would expect higher precedence to the directory name over the file name. Right now if I place a single file named `train_00001.jpg` under the `validation` directory, `load_dataset` cannot find the validation split.",
"Thanks for the reply\r\n\r\nI've created a separate [issue](https://github.com/huggingface/datasets/issues/4982#issue-1375604693) for my problem.",
"> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nSounds good to me! opened a PR: https://github.com/huggingface/datasets/pull/4985",
"Hi there @polinaeterna @mariosasko ! I have installed 5.2.3.dev0, which should have this fix. Unfortunately, I am still getting the error:\r\n`ValueError: Unknown split \"validation\". Should be one of ['train'].` When I call `load_dataset(\"csv\", data_files=files, split=split)`\r\n\r\nAny help would be greatly appreciated!",
"hi @shaneacton ! could you please show your dataset structure?",
"Hi there @polinaeterna . My local CSV files are stored as follows:\r\nbinding:\r\n---------- tune.csv\r\n---------- public_data:\r\n--------------------------- train.csv\r\n\r\n`self.list_shards(split)` sucessfully finds the relevant data files",
"@shaneacton do you have `validation.csv`/`val.csv`/`valid.csv`/`dev.csv` file in your data folder? I can't find it in the structure you provided",
"@polinaeterna no, does the name of the split need to match the name of the file exactly?\r\n\r\nBut my train file is not actually named 'train.py' its called 'XXXXXXXXX_train_XXXXXXXX.csv'\r\nAnd the code works fine for train, but fails for validation.\r\nDoes the file name need to _contain_ the split name?",
"@shaneacton what files do you expect to be included in \"validation\" split? yes, you should somehow indicate that a file belongs to a certain split - either by including split name in a filename or by putting it into a folder with split name, you can also check out [this documentation page](https://huggingface.co/docs/datasets/main/en/repository_structure) :)\r\nby default all the data goes to a single `train` split",
"@polinaeterna I have specified my train/test/tune files via the `split_to_filepattern` argument when initialising my `FileDataSource` class. This is how `list_shards` is able to find the right files.\r\nAfter your last message, I have tried renaminig my data files to simply `train.csv` and `validation.csv`, however I am still getting the same error: `Unknown split \"validation\". Should be one of ['train']`",
"@polinaeterna I have solved the issue. The solution was to call:\r\n`load_dataset(\"csv\", data_files={split: files}, split=split)`",
"For me it resolved by adding the verification_mode param:\r\n```\r\n imdb_ds = load_dataset(\r\n \"imdb\", verification_mode=\"no_checks\"\r\n )\r\n imdb_ds\r\n```"
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This PR adds the citation information to `makhzan` dataset, once they have replied to our request for that information:
- https://github.com/zeerakahmed/makhzan/issues/43
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In https://github.com/huggingface/datasets/pull/4831 @ylacombe added an oversampling strategy for `interleave_datasets`. However right now it doesn't work for datasets loaded using `load_dataset(..., streaming=True)`, which are `IterableDataset` objects.
It would be nice to expand `interleave_datasets` for iterable datasets as well to support this oversampling strategy
```python
>>> from datasets.iterable_dataset import IterableDataset, ExamplesIterable
>>> d1 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [0, 1, 2]], {}))
>>> d2 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [10, 11, 12, 13]], {}))
>>> d3 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [20, 21, 22, 23, 24]], {}))
>>> dataset = interleave_datasets([d1, d2, d3]) # is supported
>>> [x["a"] for x in dataset]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") # is not supported yet
>>> [x["a"] for x in dataset]
[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24]
```
This can be implemented by adding the strategy to both `CyclingMultiSourcesExamplesIterable` and `RandomlyCyclingMultiSourcesExamplesIterable` used in `_interleave_iterable_datasets` in `iterable_dataset.py`
I would be happy to share some guidance if anyone would like to give it a shot :)
|
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"Hi @lhoestq,\r\nI plunged into the code and it should be manageable for me to work on it!\r\n#take\r\n\r\nAlso, setting `d1`, `d2` and `d3` as you did raised a `SyntaxError: 'yield' inside list comprehension` for me, on Python 3.8.10.\r\nThe following snippet works for me though:\r\n```\r\nd1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [0, 1, 2]])), {}))\r\nd2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [10, 11, 12, 13]])), {}))\r\nd3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [20, 21, 22, 23, 24]])), {}))\r\n```\r\n\r\n",
"Great @ylacombe thanks ! I'm assigning you this issue",
"Hi @ylacombe :) Is there anything I can do to help ? Feel free to ping me if you have any question :)",
"Hi @lhoestq,\r\n\r\nI actually have already wrote the code last time [on this commit](https://github.com/ylacombe/datasets/commit/84769db97facc78a33ec53f7b1b395951e1804df) but I still have to change the docs and write some tests though. I'm working on it.\r\n\r\nHowever, I still your advice on one matter. \r\nIn #4831, when using a `Dataset` list with probabilities, I had change the original behavior so that it stops as soon as one or all datasets are out of samples. By nature, this behavior can't be applied with an `IterableDataset` because one only knows an iterable dataset is out of sample when receiving a StopIteration error after calling the iterator once again. \r\nTo sum up, as it is right know, the behavior is not consistent with an `IterableDataset` list or a `Dataset` list, when using probabilities.\r\nTo be honest, I think that the current behavior with a `Dataset` list is desirable and avoid having too many samples, so I would recommand keeping that as it is, but I can understand the desire to have the same behavior for both classes. \r\nWhat do you think ? Please let me know if you need more details.\r\n\r\n\r\nEDIT:\r\nHere is an example:\r\n```\r\n>>> from tests.test_iterable_dataset import *\r\n>>> d1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [0, 1, 2]])), {}))\r\n>>> d2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [10, 11, 12, 13]])), {}))\r\n>>> d3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [20, 21, 22, 23, 24]])), {}))\r\n>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)\r\n>>> [x[\"a\"] for x in dataset]\r\n[10, 0, 11, 1, 2, 20, 12, 13]\r\n>>> from tests.test_arrow_dataset import *\r\n>>> d1 = Dataset.from_dict({\"a\": [0, 1, 2]})\r\n>>> d2 = Dataset.from_dict({\"a\": [10, 11, 12]})\r\n>>> d3 = Dataset.from_dict({\"a\": [20, 21, 22]})\r\n>>> interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)[\"a\"]\r\n[10, 0, 11, 1, 2]\r\n[10, 0, 11, 1, 2]\r\n```\r\n ",
"Hi ! Awesome :) \r\n\r\nMaybe you can pre-load the next sample to know if the dataset is empty or not ?\r\nThis way it should be possible to have the same behavior for `IterableDataset`",
"Hi @ylacombe let us know if we can help with anything :)",
"Hi @lhoestq, I've finally made some advances in the matter. I've modified the `IterableDataset` behavior so that it aligns with the `Dataset` behavior as we have discussed. The documentation has been dealt with too. \r\nIt works as expected on my examples. However I'm having trouble figuring out how to test `interleave_datasets` on `test_iterable_datasets.py` as I have never worked with pytest. Could you help me on that or give me some indications? \r\n",
"Thanks @ylacombe :)\r\n\r\nUsing the `pytest` command, you can run all the functions in a python file that start with \"test_*\" and make sure they return not errors:\r\n```\r\npytest tests/test_iterable_dataset.py\r\n```\r\n\r\nIn our case it can be nice to define a `test_interleave_datasets_with_oversampling` function. This function can contain the code example that we mentioned earlier in this github issue to make sure it works as expected.",
"Resolved via #5036."
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This PR adds the citation information to `ro_sts_parallel` and `ro_sts_parallel` datasets, once they have replied our request for that information:
- https://github.com/dumitrescustefan/RO-STS/issues/4
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4892). All of your documentation changes will be reflected on that endpoint."
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Fix missing tags in dataset cards:
- aslg_pc12
- librispeech_lm
- mwsc
- opus100
- qasc
- quail
- squadshifts
- winograd_wsc
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
|
2022-09-22T14:39:02Z
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As discussed in #4885
I initially added this bit at the end, thinking filling this field was necessary as it is done in from_dict.
However, it seems the constructor takes care of filling info when it is empty.
```
if info.features is None:
info.features = Features(
{
col: generate_from_arrow_type(coldata.type)
for col, coldata in zip(pa_table.column_names, pa_table.columns)
}
)
```
|
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add Dataset.from_list
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2022-09-02T10:20:33Z
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"@albertvillanova it seems tests fail on pyarrow 6, perhaps from_pylist is a v7 method? How do you usually handle these version differences?\r\nAdded something that at least works"
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## Describe the bug
When loading Common Voice with torchaudio 0.11.0 the results are different to 0.12.1 which leads to problems in transformers see: https://github.com/huggingface/transformers/pull/18749
## Steps to reproduce the bug
If you run the following code once with `torchaudio==0.11.0+cu102` and `torchaudio==0.12.1+cu102` you can see that the tensors differ. This is a pretty big breaking change and makes some integration tests fail in Transformers.
```python
#!/usr/bin/env python3
from datasets import load_dataset
import datasets
import numpy as np
import torch
import torchaudio
print("torch vesion", torch.__version__)
print("torchaudio vesion", torchaudio.__version__)
save_audio = True
load_audios = False
if save_audio:
ds = load_dataset("common_voice", "en", split="train", streaming=True)
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
ds_iter = iter(ds)
sample = next(ds_iter)
np.save(f"audio_sample_{torch.__version__}", sample["audio"]["array"])
print(sample["audio"]["array"])
if load_audios:
array_torch_11 = np.load("/home/patrick/audio_sample_1.11.0+cu102.npy")
print("Array 11 Shape", array_torch_11.shape)
print("Array 11 abs sum", np.sum(np.abs(array_torch_11)))
array_torch_12 = np.load("/home/patrick/audio_sample_1.12.1+cu102.npy")
print("Array 12 Shape", array_torch_12.shape)
print("Array 12 abs sum", np.sum(np.abs(array_torch_12)))
```
Having saved the tensors the print output yields:
```
torch vesion 1.12.1+cu102
torchaudio vesion 0.12.1+cu102
Array 11 Shape (122880,)
Array 11 abs sum 1396.4988
Array 12 Shape (123264,)
Array 12 abs sum 1396.5193
```
## Expected results
torchaudio 11.0 and 12.1 should yield same results.
## Actual results
See above.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.1.dev0
- Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.34
- Python version: 3.9.7
- PyArrow version: 6.0.1
- Pandas version: 1.4.2
|
2023-03-02T15:33:05Z
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torchaudio 11.0 yields different results than torchaudio 12.1 when loading MP3
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2023-03-02T15:33:04Z
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[
"Maybe we can just pass this along to torchaudio @lhoestq @albertvillanova ? It be great if you could investigate if the errors lies in datasets or in torchaudio.",
"torchaudio did a change in [0.12](https://github.com/pytorch/audio/releases/tag/v0.12.0) on MP3 decoding (which affects common voice):\r\n> MP3 decoding is now handled by FFmpeg in sox_io backend. (https://github.com/pytorch/audio/pull/2419, https://github.com/pytorch/audio/pull/2428)\r\n> - FFmpeg is now used as fallback in sox_io backend, and now MP3 decoding is handled by FFmpeg. To load MP3 audio with torchaudio.load, please install a compatible version of FFmpeg (Version 4 when using an official binary distribution).\r\n> - Note that, whereas the previous MP3 decoding scheme pads the output audio, the new scheme does not. As a consequence, the new version returns shorter audio tensors.",
"Do we have a solution for this now? Should we just upgrade to `torchaudio 0.12.0` then? ",
"`datasets` supports `torchaudio` 0.12 if you have an environment that supports reading MP3 with `torchaudio`, i.e. if you have `ffmpeg>=4`",
"Closing as we no longer use `torchaudio` for decoding."
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### Link
https://huggingface.co/datasets/subjqa
### Description
Getting the following error for this dataset:
```
Status code: 500
Exception: Status500Error
Message: 2 or more items returned, instead of 1
```
Not sure what's causing it though 🤔
### Owner
Yes
|
2022-09-08T08:23:42Z
| 4,888
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Dataset Viewer issue for subjqa
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"It's a bug in the viewer, thanks for reporting it. We're hoping to update to a new version in the next few days which should fix it.",
"Fixed \r\n\r\nhttps://huggingface.co/datasets/subjqa\r\n\r\n<img width=\"1040\" alt=\"Capture d’écran 2022-09-08 à 10 23 26\" src=\"https://user-images.githubusercontent.com/1676121/189073210-2a57ff88-8bb1-44bd-851e-0e75473cea3f.png\">\r\n"
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Sorry for the issue @albertvillanova! I think it's now fixed! :heart: "
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## Describe the bug
Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('huggan/CelebA-HQ')
```
## Expected results
See https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing#scrollTo=N3ml_7f8kzDd
## Actual results
```
File "/home/jean/projects/cold_diffusion/celebA.py", line 4, in <module>
dataset = load_dataset('huggan/CelebA-HQ')
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/load.py", line 1793, in load_dataset
builder_instance.download_and_prepare(
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 1274, in _prepare_split
for key, table in logging.tqdm(
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in _generate_tables
parquet_file = pq.ParquetFile(f)
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/pyarrow/parquet/__init__.py", line 286, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open
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.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets-2.4.1.dev0
- Platform: Ubuntu 18.04
- Python version: 3.10
- PyArrow version: pyarrow 9.0.0
|
2023-02-02T02:40:53Z
| 4,886
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Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid
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I_kwDODunzps5QbHbB
|
[
"Hi! IIRC one of the files in this dataset is corrupted due to https://github.com/huggingface/datasets/pull/4081 (fixed now).\r\n\r\n@NielsRogge Could you please re-generate and re-push this dataset (or I can do it if you share the generation script)?",
"Could you put something in place to catch these problems? I'm seeing this on another dataset consistently too and I guess I can't fix it in code?",
"Hey,\r\n\r\nYes the notebook I used to upload this dataset can be found here: https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing.\r\n\r\nIf you have time to regenerate the dataset, would be great.",
"Sorry, maybe I wasn't clear enough that it's a different dataset `laion2B-multi-joined-translated-to-en`. I think there should be checks in the upload, tests on the server, or validation after download (hashes) to catch these problems.\r\n\r\nLots of bandwidth wasted otherwise! /cc @mariosasko",
"Yes @alexjc sorry was more a reply to @JeanKaddour.\r\n\r\nAnd indeed it'd be great to have additional checks to avoid these errors. ",
"cc @severo since such checks should probably be implemented on the datasets-server side.",
"Hi,\r\n\r\nIt seems the problem is still persist. I have encountered the exact same problem using just 2 line of code above. \r\n\r\nThe error code is as follows:\r\n\r\n```\r\n發生例外狀況: DatasetGenerationError\r\nAn error occurred while generating the dataset\r\npyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\n File \"/code/ddpm_learn/train.py\", line 65, in <module>\r\n dataset = load_dataset(\"huggan/CelebA-HQ\", cache_dir=\"./CelebA-HQ\"\r\ndatasets.builder.DatasetGenerationError: An error occurred while generating the dataset\r\n```",
"Yes for the moment refer to the notebook linked above if you want to create a HF dataset yourself",
"Hi @NielsRogge ,\r\nI can help to push the dataset to the cloud. However, I cannot locate the situation so far. I wonder if \r\n1. the downloaded files so far has corruption s.t. the file cannot generate properly, or\r\n2. the downloaded files has no bug, the bug is caused by buggy upload program so that I can use what I have just downloaded to re-upload to cloud\r\n\r\nThank, \r\nAllan"
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I often find myself with data from a variety of sources, and a list of dicts is very common among these.
However, converting this to a Dataset is a little awkward, requiring either
```Dataset.from_pandas(pd.DataFrame(formatted_training_data))```
Which can error out on some more exotic values as 2-d arrays for reasons that are not entirely clear
> ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column labels with type object')
Alternatively:
```Dataset.from_dict({k: [s[k] for s in formatted_training_data] for k in formatted_training_data[0].keys()})```
Which works, but is a little ugly.
**Describe the solution you'd like**
Either `.from_dict` accepting a list of dicts, or a `.from_records` function accepting such.
I am happy to PR this, just wanted to check you are happy to accept this I haven't missed something obvious, and which of the solutions would be prefered.
|
2022-09-08T16:02:52Z
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Create dataset from list of dicts
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CONTRIBUTOR
|
2022-09-08T16:02:52Z
| null |
I_kwDODunzps5QauAI
|
[
"Hi @sanderland, thanks for your enhancement proposal.\r\n\r\nI agree with you that this would be useful.\r\n\r\nPlease note that under the hood, we use PyArrow tables as backend:\r\n- The implementation of `Dataset.from_dict` uses the PyArrow `Table.from_pydict`\r\n\r\nTherefore, I would suggest:\r\n- Implementing `Dataset.from_list` using the PyArrow `Table.from_pylist`\r\n\r\nWhat do you think?\r\nLet's see if other people have other suggestions...",
"Thanks for the quick and positive reply @albertvillanova! \r\n`from_list` seems sensible. Have opened a PR so we can discuss details there.",
"Resolved via #4890."
] |
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