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https://api.github.com/repos/huggingface/datasets/issues/3063 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3063/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3063/comments | https://api.github.com/repos/huggingface/datasets/issues/3063/events | https://github.com/huggingface/datasets/issues/3063 | 1,023,588,297 | I_kwDODunzps49ArfJ | 3,063 | Windows CI is unable to test streaming properly because of SSL issues | {
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The issue appears only on windows with asyncio. On Linux it works. With requests it works as well. And with the production environment huggingface.co it also works.
to reproduce on windows:
```python
import fsspec
# use any URL to a file in a dataset repo
url = "https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes"
fsspec.open(url).open()
```
raises
```python
FileNotFoundError: https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes
```
because of
```python
aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')]
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3062 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3062/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3062/comments | https://api.github.com/repos/huggingface/datasets/issues/3062/events | https://github.com/huggingface/datasets/pull/3062 | 1,023,209,592 | PR_kwDODunzps4tCxfK | 3,062 | Update summary on PyPi beyond NLP | {
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https://api.github.com/repos/huggingface/datasets/issues/3061 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3061/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3061/comments | https://api.github.com/repos/huggingface/datasets/issues/3061/events | https://github.com/huggingface/datasets/issues/3061 | 1,023,103,119 | I_kwDODunzps48-1CP | 3,061 | Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?) | {
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] | open | false | null | [] | null | 2 | "2021-10-11T20:49:49Z" | "2021-10-22T09:34:10Z" | null | NONE | null | null | null | **A clear and concise description of what you want to happen.**
It would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions.
**Describe alternatives you've considered**
By the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish?
**Additional context**
Furthering tqdm integration #2374 and huggingface/transformers#11797 solutioned by huggingface/transformers#12226 provided with tqdm description as `desc=`
@sgugger @bhavitvyamalik | {
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https://api.github.com/repos/huggingface/datasets/issues/3060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3060/comments | https://api.github.com/repos/huggingface/datasets/issues/3060/events | https://github.com/huggingface/datasets/issues/3060 | 1,022,936,396 | I_kwDODunzps48-MVM | 3,060 | load_dataset('openwebtext') yields "Compressed file ended before the end-of-stream marker was reached" | {
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] | null | 2 | "2021-10-11T17:05:27Z" | "2021-10-28T05:52:21Z" | "2021-10-28T05:52:21Z" | NONE | null | null | null | ## Describe the bug
When I try `load_dataset('openwebtext')`, I receive a "EOFError: Compressed file ended before the end-of-stream marker was reached" error.
## Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset('openwebtext')
```
## Expected results
I expect the `dataset` variable to be properly constructed.
## Actual results
```
File "/home/rschaef/CoCoSci-Language-Distillation/distillation_v2/ratchet_learning/tasks/base.py", line 37, in create_dataset
dataset_str,
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/load.py", line 1117, in load_dataset
use_auth_token=use_auth_token,
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 637, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 704, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rschaef/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1/openwebtext.py", line 61, in _split_generators
dl_dir = dl_manager.download_and_extract(_URL)
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 261, in extract
partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 197, in map_nested
return function(data_struct)
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 316, in cached_path
output_path, force_extract=download_config.force_extract
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 40, in extract
self.extractor.extract(input_path, output_path, extractor=extractor)
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 179, in extract
return extractor.extract(input_path, output_path)
File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 53, in extract
tar_file.extractall(output_path)
File "/usr/lib/python3.6/tarfile.py", line 2010, in extractall
numeric_owner=numeric_owner)
File "/usr/lib/python3.6/tarfile.py", line 2052, in extract
numeric_owner=numeric_owner)
File "/usr/lib/python3.6/tarfile.py", line 2122, in _extract_member
self.makefile(tarinfo, targetpath)
File "/usr/lib/python3.6/tarfile.py", line 2171, in makefile
copyfileobj(source, target, tarinfo.size, ReadError, bufsize)
File "/usr/lib/python3.6/tarfile.py", line 249, in copyfileobj
buf = src.read(bufsize)
File "/usr/lib/python3.6/lzma.py", line 200, in read
return self._buffer.read(size)
File "/usr/lib/python3.6/_compression.py", line 68, in readinto
data = self.read(len(byte_view))
File "/usr/lib/python3.6/_compression.py", line 99, in read
raise EOFError("Compressed file ended before the "
python-BaseException
EOFError: Compressed file ended before the end-of-stream marker was reached
```
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial
- Python version: 3.6.10
- PyArrow version: 5.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3059 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3059/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3059/comments | https://api.github.com/repos/huggingface/datasets/issues/3059/events | https://github.com/huggingface/datasets/pull/3059 | 1,022,620,057 | PR_kwDODunzps4tA54w | 3,059 | Fix task reloading from cache | {
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} | When reloading a dataset from the cache when doing `map`, the tasks templates were kept instead of being updated regarding the output of the `map` function. This is an issue because we drop the tasks templates that are not compatible anymore after `map`, for example if a column of the template was removed.
This PR fixes this and for convenience introduces a decorator `@transmit_tasks` that takes care of doing this verification, similar to the `@transmit_format` decorator.
This should fix issue https://github.com/huggingface/datasets/issues/3047 cc @sgugger | {
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https://api.github.com/repos/huggingface/datasets/issues/3058 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3058/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3058/comments | https://api.github.com/repos/huggingface/datasets/issues/3058/events | https://github.com/huggingface/datasets/issues/3058 | 1,022,612,664 | I_kwDODunzps4889S4 | 3,058 | Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader. | {
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] | closed | false | null | [] | null | 2 | "2021-10-11T11:54:59Z" | "2022-01-19T14:03:49Z" | "2022-01-19T14:03:49Z" | NONE | null | null | null | ## Describe the bug
I have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`.
## Steps to reproduce the bug
Run the `run_mlm_no_trainer.py` and the given script on this [link](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3.
## Expected results
The data batchs are fetched from the data loader and train.
## Actual results
The first time to fetch data batch occurs error.
`Traceback (most recent call last):
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors
tensor = as_tensor(value)
ValueError: too many dimensions 'str'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "src/original_run_mlm_no_trainer.py", line 528, in <module>
main()
File "src/original_run_mlm_no_trainer.py", line 488, in main
for step, batch in enumerate(train_dataloader):
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/accelerate/data_loader.py", line 303, in __iter__
for batch in super().__iter__():
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
data = self._next_data()
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 557, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
return self.collate_fn(data)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 41, in __call__
return self.torch_call(features)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 671, in torch_call
batch = self.tokenizer.pad(examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2774, in pad
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 210, in __init__
self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 722, in convert_to_tensors
"Unable to create tensor, you should probably activate truncation and/or padding "
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.
`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.6
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3057 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3057/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3057/comments | https://api.github.com/repos/huggingface/datasets/issues/3057/events | https://github.com/huggingface/datasets/issues/3057 | 1,022,508,315 | I_kwDODunzps488j0b | 3,057 | Error in per class precision computation | {
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] | closed | false | null | [] | null | 1 | "2021-10-11T10:05:19Z" | "2021-10-11T10:17:44Z" | "2021-10-11T10:16:16Z" | NONE | null | null | null | ## Describe the bug
When trying to get the per class precision values by providing `average=None`, following error is thrown `ValueError: can only convert an array of size 1 to a Python scalar`
## Steps to reproduce the bug
```python
from datasets import load_dataset, load_metric
precision_metric = load_metric("precision")
predictions = [0, 2, 1, 0, 0, 1]
references = [0, 1, 2, 0, 1, 2]
results = precision_metric.compute(predictions=predictions, references=references, average=None)
```
## Expected results
` {'precision': array([0.66666667, 0. , 0. ])}`
as per https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py
## Actual results
```
output = self._compute(predictions=predictions, references=references, **kwargs)
File "~/.cache/huggingface/modules/datasets_modules/metrics/precision/94709a71c6fe37171ef49d3466fec24dee9a79846c9f176dff66a649e9811690/precision.py", line 110, in _compute
sample_weight=sample_weight,
ValueError: can only convert an array of size 1 to a Python scalar
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.6.9
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3056 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3056/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3056/comments | https://api.github.com/repos/huggingface/datasets/issues/3056/events | https://github.com/huggingface/datasets/pull/3056 | 1,022,345,564 | PR_kwDODunzps4tAB9h | 3,056 | Fix meteor metric for version >= 3.6.4 | {
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} | [] | closed | false | null | [] | null | 0 | "2021-10-11T07:11:44Z" | "2021-10-11T07:29:20Z" | "2021-10-11T07:29:19Z" | MEMBER | null | 0 | {
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} | After `nltk` update, the meteor metric expects pre-tokenized inputs (breaking change).
This PR fixes this issue, while maintaining compatibility with older versions. | {
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https://api.github.com/repos/huggingface/datasets/issues/3055 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3055/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3055/comments | https://api.github.com/repos/huggingface/datasets/issues/3055/events | https://github.com/huggingface/datasets/issues/3055 | 1,022,319,238 | I_kwDODunzps4871qG | 3,055 | CI test suite fails after meteor metric update | {
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CI test suite fails: https://app.circleci.com/pipelines/github/huggingface/datasets/8110/workflows/f059ba43-9154-4632-bebb-82318447ddc9/jobs/50010
Stack trace:
```
___________________ LocalMetricTest.test_load_metric_meteor ____________________
[gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6
self = <tests.test_metric_common.LocalMetricTest testMethod=test_load_metric_meteor>
metric_name = 'meteor'
def test_load_metric(self, metric_name):
doctest.ELLIPSIS_MARKER = "[...]"
metric_module = importlib.import_module(datasets.load.prepare_module(os.path.join("metrics", metric_name))[0])
metric = datasets.load.import_main_class(metric_module.__name__, dataset=False)
# check parameters
parameters = inspect.signature(metric._compute).parameters
self.assertTrue("predictions" in parameters)
self.assertTrue("references" in parameters)
self.assertTrue(all([p.kind != p.VAR_KEYWORD for p in parameters.values()])) # no **kwargs
# run doctest
with self.patch_intensive_calls(metric_name, metric_module.__name__):
with self.use_local_metrics():
> results = doctest.testmod(metric_module, verbose=True, raise_on_error=True)
tests/test_metric_common.py:75:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1951: in testmod
runner.run(test)
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1839: in run
r = DocTestRunner.run(self, test, compileflags, out, False)
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1476: in run
return self.__run(test, compileflags, out)
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1382: in __run
exception)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <doctest.DebugRunner object at 0x7f4c26bd3da0>
out = <built-in method write of _io.TextIOWrapper object at 0x7f51a21852d0>
test = <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Mete...ets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)>
example = <doctest.Example object at 0x7f4c26bd3eb8>
exc_info = (<class 'TypeError'>, TypeError('"hypothesis" expects pre-tokenized hypothesis (Iterable[str]): It is a guide to action which ensures that the military always obeys the commands of the party',), <traceback object at 0x7f4cd01afec8>)
def report_unexpected_exception(self, out, test, example, exc_info):
> raise UnexpectedException(test, example, exc_info)
E doctest.UnexpectedException: <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Meteor from /tmp/pytest-of-circleci/pytest-0/popen-gw1/cache/modules/datasets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)>
../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1845: UnexpectedException
```
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https://api.github.com/repos/huggingface/datasets/issues/3054 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3054/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3054/comments | https://api.github.com/repos/huggingface/datasets/issues/3054/events | https://github.com/huggingface/datasets/pull/3054 | 1,022,108,186 | PR_kwDODunzps4s_TmE | 3,054 | Update Biosses | {
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https://api.github.com/repos/huggingface/datasets/issues/3053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3053/comments | https://api.github.com/repos/huggingface/datasets/issues/3053/events | https://github.com/huggingface/datasets/issues/3053 | 1,022,076,905 | I_kwDODunzps4866fp | 3,053 | load_dataset('the_pile_openwebtext2') produces ArrowInvalid, value too large to fit in C integer type | {
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] | closed | false | null | [] | null | 5 | "2021-10-10T19:55:21Z" | "2023-02-24T14:02:20Z" | "2023-02-24T14:02:20Z" | NONE | null | null | null | ## Describe the bug
When loading `the_pile_openwebtext2`, we get the error `pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type`
## Steps to reproduce the bug
```python
import datasets
ds = datasets.load_dataset('the_pile_openwebtext2')
```
## Expected results
Should download the dataset, convert it to an arrow file, and return a working Dataset object.
## Actual results
The download works, but conversion to the arrow file fails as follows:
```
>>> ds = datasets.load_dataset('the_pile_openwebtext2')
Downloading and preparing dataset openwebtext2/plain_text (download: 27.33 GiB, generated: 63.86 GiB
, post-processed: Unknown size, total: 91.19 GiB) to /home/davidbau/.cache/huggingface/datasets/open
webtext2/plain_text/1.0.0/c48ec73ba3483bac673463f48f67e9a4fd8cb49a9d6ec4fb957f0b424b97cf25...
Traceback (most recent call last):
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/builder.py", line 1133,
in _prepare_split
writer.write(example, key)
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
366, in write
self.write_examples_on_file()
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
311, in write_examples_on_file
pa_array = pa.array(typed_sequence)
File "pyarrow/array.pxi", line 222, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
115, in __arrow_array__
out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)
File "pyarrow/array.pxi", line 305, in pyarrow.lib.array
File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
```
- Platform: Ubuntu 20.04
- Python version: python 3.9
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3052 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3052/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3052/comments | https://api.github.com/repos/huggingface/datasets/issues/3052/events | https://github.com/huggingface/datasets/issues/3052 | 1,021,944,435 | I_kwDODunzps486aJz | 3,052 | load_dataset cannot download the data and hangs on forever if cache dir specified | {
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] | closed | false | null | [] | null | 1 | "2021-10-10T10:31:36Z" | "2021-10-11T10:57:09Z" | "2021-10-11T10:56:36Z" | NONE | null | null | null | ## Describe the bug
After updating datasets, a code that ran just fine for ages began to fail. Specifying _datasets.load_dataset_'s _cache_dir_ optional argument on Windows 10 machine results in data download to hang on forever. Same call without cache_dir works just fine. Surprisingly exact same code just runs perfectly fine on Linux docker instance running in cloud.
Unfortunately I updated Windows also at the same time and I can't remember which version of datasets was running in my conda environment prior to the update otherwise I would have tried both to check this out. :(
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
cache_dir = 'c:/data/datasets'
dataset = load_dataset('wikipedia', '20200501.en', split='train',cache_dir=cache_dir)
```
Note that exact same code without specifying _cache_dir_ argument works perfectly fine.
```
cache_dir = 'c:/data/datasets'
dataset = load_dataset('wikipedia', '20200501.en', split='train')
```
## Expected results
Downloads the dataset and cache is handled in the _cache_dir_ directory
## Actual results
Data download keeps hanging on forever, **NO TRACEBACK**!
## Environment info
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.8.11
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3051 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3051/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3051/comments | https://api.github.com/repos/huggingface/datasets/issues/3051/events | https://github.com/huggingface/datasets/issues/3051 | 1,021,852,234 | I_kwDODunzps486DpK | 3,051 | Non-Matching Checksum Error with crd3 dataset | {
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] | null | 2 | "2021-10-10T01:32:43Z" | "2022-03-15T15:54:26Z" | "2022-03-15T15:54:26Z" | NONE | null | null | null | ## Describe the bug
When I try loading the crd3 dataset (https://huggingface.co/datasets/crd3), an error is thrown.
## Steps to reproduce the bug
```python
dataset = load_dataset('crd3', split='train')
```
## Expected results
I expect no error to be thrown.
## Actual results
A non-matching checksum error is thrown.
```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/RevanthRameshkumar/CRD3/archive/master.zip']
```
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial
- Python version: 3.6.10
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3050 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3050/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3050/comments | https://api.github.com/repos/huggingface/datasets/issues/3050/events | https://github.com/huggingface/datasets/pull/3050 | 1,021,772,622 | PR_kwDODunzps4s-anK | 3,050 | Fix streaming: catch Timeout error | {
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} | [] | closed | false | null | [] | null | 5 | "2021-10-09T18:19:20Z" | "2021-10-12T15:28:18Z" | "2021-10-11T09:35:38Z" | CONTRIBUTOR | null | 0 | {
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fix #3049 | {
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] | closed | false | null | [] | null | 0 | "2021-10-09T18:06:51Z" | "2021-10-11T09:35:38Z" | "2021-10-11T09:35:38Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
I got a TimeoutError after streaming for about 10h.
## Steps to reproduce the bug
Very long code but we could do a test of streaming indefinitely data, though error may take a while to appear.
## Expected results
This error was not expected in the code which considers only `ClientError` but not `TimeoutError`.
See [this line](https://github.com/huggingface/datasets/blob/2814fbd0e18150be409f10804670e98d9ecb87d4/src/datasets/utils/streaming_download_manager.py#L129).
Based on the traceback, it looks like the `TimeoutError` was not captured.
## Actual results
```
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 25, in _runner
result[0] = await coro
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/implementations/http.py", line 614, in async_fetch_range
out = await r.read()
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1032, in read
self._body = await self.content.read()
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 370, in read
block = await self.readany()
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 392, in readany
await self._wait("readany")
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 306, in _wait
await waiter
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/helpers.py", line 656, in __exit__
raise asyncio.TimeoutError from None
asyncio.exceptions.TimeoutError
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 1027, in <module>
main()
File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 991, in main
for batch in tqdm(
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/tqdm/std.py", line 1180, in __iter__
for obj in iterable:
File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 376, in data_loader_streaming
for item in dataset:
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 179, in __iter__
key_examples_list = [(key, example)] + [
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 179, in <listcomp>
key_examples_list = [(key, example)] + [
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 176, in __iter__
for key, example in iterator:
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 225, in __iter__
for x in self.ex_iterable:
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 99, in __iter__
for key, example in self.generate_examples_fn(**kwargs_with_shuffled_shards):
File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 287, in wrapper
for key, table in generate_tables_fn(**kwargs):
File "/home/koush/datasets/src/datasets/packaged_modules/json/json.py", line 107, in _generate_tables
batch = f.read(self.config.chunksize)
File "/home/koush/datasets/src/datasets/utils/streaming_download_manager.py", line 126, in read_with_retries
out = read(*args, **kwargs)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/implementations/http.py", line 572, in read
return super().read(length)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/spec.py", line 1533, in read
out = self.cache._fetch(self.loc, self.loc + length)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/caching.py", line 390, in _fetch
self.cache = self.fetcher(start, bend)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 91, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 69, in sync
raise FSTimeoutError from return_result
fsspec.exceptions.FSTimeoutError
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.4.0-1043-gcp-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 5.0.0
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] | open | false | null | [] | null | 1 | "2021-10-09T17:46:35Z" | "2021-10-09T20:24:17Z" | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
I'm training on a large dataset made of multiple sub-datasets.
During training I can observe some jumps in loss which may correspond to different shards.
My suspicion is that either:
* some of the sub-datasets are harder for the model than others
* some of the sub-datasets are not formatted properly
I'd like to identify which shards correspond to those jumps.
**Describe the solution you'd like**
It would be nice to have a key associated to each data sample or data batch containing details on where the data comes from (shard idx + item idx within the shard).
This should be supported both in local and streaming mode.
**Describe alternatives you've considered**
A fix would be for me to add myself details (shard id, sample id) as part of each data sample.
The inconvenient is that it requires users to process/reupload every dataset when they need this feature. | {
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] | null | 1 | "2021-10-08T18:23:11Z" | "2021-11-03T17:13:08Z" | "2021-11-03T17:13:08Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
Yes, I know, that description sucks. So the problem is arising in the course when we build a masked language modeling dataset using the IMDB dataset. To reproduce (or try since it's a bit fickle).
Create a dataset for masled-language modeling from the IMDB dataset.
```python
from datasets import load_dataset
from transformers import Autotokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased)
imdb_dataset = load_dataset("imdb", split="train")
def tokenize_function(examples):
return tokenizer(examples["text"])
tokenized_dataset = imdb_dataset.map(
tokenize_function, batched=True, remove_columns=["text", "label"]
)
chunk_size = 128
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
# Compute length of concatenated texts
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the last chunk if it's smaller than chunk_size
total_length = (total_length // chunk_size) * chunk_size
# Split by chunks of max_len.
result = {
k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]
for k, t in concatenated_examples.items()
}
# Create a new labels column
result["labels"] = result["input_ids"].copy()
return result
lm_dataset = tokenized_dataset.map(group_texts, batched=True)
```
Until now, all is well. The problem comes when you re-execute that code, more specifically:
```python
tokenized_dataset = imdb_dataset.map(
tokenize_function, batched=True, remove_columns=["text", "label"]
)
lm_dataset = tokenized_dataset.map(group_texts, batched=True)
```
Try several times if the bug doesn't appear instantly, or just each line at a time, ideally in a notebook/Colab and you should get at some point:
```python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-40-357a56ee3d53> in <module>
----> 1 lm_dataset = tokenized_dataset.map(group_texts, batched=True)
~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1947 new_fingerprint=new_fingerprint,
1948 disable_tqdm=disable_tqdm,
-> 1949 desc=desc,
1950 )
1951 else:
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
424 }
425 # apply actual function
--> 426 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
427 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
428 # re-apply format to the output
~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2138 if os.path.exists(cache_file_name) and load_from_cache_file:
2139 logger.warning("Loading cached processed dataset at %s", cache_file_name)
-> 2140 info = self.info.copy()
2141 info.features = features
2142 return Dataset.from_file(cache_file_name, info=info, split=self.split)
~/git/datasets/src/datasets/info.py in copy(self)
278
279 def copy(self) -> "DatasetInfo":
--> 280 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
281
282
~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes)
~/git/datasets/src/datasets/info.py in __post_init__(self)
177 for idx, template in enumerate(self.task_templates):
178 if isinstance(template, TextClassification):
--> 179 labels = self.features[template.label_column].names
180 self.task_templates[idx] = TextClassification(
181 text_column=template.text_column, label_column=template.label_column, labels=labels
KeyError: 'label'
```
It seems that when loading the cache, the dataset tries to access some kind of text classification template (which I imagine comes from the original dataset) and to look at a key that has since been removed. | {
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1. A rough unit test that fails without the fix. It probably doesn't comply with your code standards, but that just to draft the idea.
2. A one liner fix | {
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] | open | false | null | [] | null | 3 | "2021-10-08T09:07:10Z" | "2022-09-07T21:01:36Z" | null | NONE | null | null | null | ## Describe the bug
Caching does not work when using `Dataset.map()` with:
1. a function that cannot be deterministically fingerprinted
2. `num_proc>1`
3. using a custom fingerprint set with the argument `new_fingerprint`.
This means that the dataset will be mapped with the function for each and every call, which does not happen if `num_proc==1`. In that case (`num_proc==1`) subsequent calls will load the transformed dataset from the cache, which is the expected behaviour. The example can easily be translated into a unit test.
I have a fix and will submit a pull request asap.
## Steps to reproduce the bug
```python
import hashlib
import json
import os
from typing import Dict, Any
import numpy as np
from datasets import load_dataset, Dataset
Batch = Dict[str, Any]
filename = 'example.json'
class Transformation():
"""A transformation with a random state that cannot be fingerprinted"""
def __init__(self):
self.state = np.random.random()
def __call__(self, batch: Batch) -> Batch:
batch['x'] = [np.random.random() for _ in batch['x']]
return batch
def generate_dataset():
"""generate a simple dataset"""
rgn = np.random.RandomState(24)
data = {
'data': [{'x': float(y), 'y': -float(y)} for y in
rgn.random(size=(1000,))]}
if not os.path.exists(filename):
with open(filename, 'w') as f:
f.write(json.dumps(data))
return filename
def process_dataset_with_cache(num_proc=1, remove_cache=False,
cache_expected_to_exist=False):
# load the generated dataset
dset: Dataset = next(
iter(load_dataset('json', data_files=filename, field='data').values()))
new_fingerprint = hashlib.md5("static-id".encode("utf8")).hexdigest()
# get the expected cached path
cache_path = dset._get_cache_file_path(new_fingerprint)
if remove_cache and os.path.exists(cache_path):
os.remove(cache_path)
# check that the cache exists, and print a statement
# if was actually expected to exist
cache_exist = os.path.exists(cache_path)
print(f"> cache file exists={cache_exist}")
if cache_expected_to_exist and not cache_exist:
print("=== Cache does not exist! ====")
# apply the transformation with the new fingerprint
dset = dset.map(
Transformation(),
batched=True,
num_proc=num_proc,
new_fingerprint=new_fingerprint,
desc="mapping dataset with transformation")
generate_dataset()
for num_proc in [1, 2]:
print(f"# num_proc={num_proc}, first pass")
# first pass to generate the cache (always create a new cache here)
process_dataset_with_cache(remove_cache=True,
num_proc=num_proc,
cache_expected_to_exist=False)
print(f"# num_proc={num_proc}, second pass")
# second pass, expects the cache to exist
process_dataset_with_cache(remove_cache=False,
num_proc=num_proc,
cache_expected_to_exist=True)
os.remove(filename)
```
## Expected results
In the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache` ("=== Cache does not exist! ====" should not be printed).
When the cache is successfully created, `map()` is called only one time.
## Actual results
In the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing "=== Cache does not exist! ====").
Because the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache.
## Environment info
- `datasets` version: 1.12.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 5.0.0
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] | null | 0 | "2021-10-07T16:43:43Z" | "2022-01-20T16:50:47Z" | "2022-01-20T16:50:47Z" | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** PASS
- **Description:** An ImageNet replacement for self-supervised pretraining without humans
- **Data:** https://www.robots.ox.ac.uk/~vgg/research/pass/ https://github.com/yukimasano/PASS
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/3042 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3042/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3042/comments | https://api.github.com/repos/huggingface/datasets/issues/3042/events | https://github.com/huggingface/datasets/pull/3042 | 1,020,047,289 | PR_kwDODunzps4s5Lxo | 3,042 | Improving elasticsearch integration | {
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} | - adding murmurhash signature to sample in index
- adding optional credentials for remote elasticsearch server
- enabling sample update in index
- upgrade the elasticsearch 7.10.1 python client
- adding ElasticsearchBulider to instantiate a dataset from an index and a filtering query | {
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} | As mentioned in https://github.com/huggingface/datasets/issues/3032 loading data files from private repository isn't working correctly because of the data files resolved.
#2986 did a refactor of the data files resolver. I added authentication to it.
I also improved it to glob inside ZIP archives to look for json/csv/etc. files and infer which dataset builder (json/csv/etc.) to use.
Fix https://github.com/huggingface/datasets/issues/3032
Note that #2986 needs to get merged first | {
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] | null | 5 | "2021-10-06T17:08:47Z" | "2021-11-02T15:41:08Z" | "2021-11-02T15:41:08Z" | MEMBER | null | null | null | ## Describe the bug
When only keeping a dummy size of a dataset (say the first 100 samples), and then saving it to disk to upload it in the following to the hub for easy demo/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.
## Steps to reproduce the bug
E.g. run the following:
```python
from datasets import load_dataset, save_to_disk
nlp = load_dataset("glue", "mnli", split="train")
nlp.save_to_disk("full")
nlp = nlp.select(range(100))
nlp.save_to_disk("dummy")
```
Now one can see that both `"dummy"` and `"full"` have the same size. This shouldn't be the case IMO.
## Expected results
IMO `"dummy"` should be much smaller so that one can easily play around with the dataset on the hub.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3037/comments | https://api.github.com/repos/huggingface/datasets/issues/3037/events | https://github.com/huggingface/datasets/pull/3037 | 1,018,091,919 | PR_kwDODunzps4syi15 | 3,037 | SberQuad | {
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https://api.github.com/repos/huggingface/datasets/issues/3036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3036/comments | https://api.github.com/repos/huggingface/datasets/issues/3036/events | https://github.com/huggingface/datasets/issues/3036 | 1,017,687,944 | I_kwDODunzps48qK-I | 3,036 | Protect master branch to force contributions via Pull Requests | {
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] | closed | false | null | [] | null | 3 | "2021-10-06T07:34:17Z" | "2021-10-07T06:51:47Z" | "2021-10-07T06:49:52Z" | MEMBER | null | null | null | In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed.
- The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution
- The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context/discussions)
Note that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests.
Please note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release.
The only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.
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] | null | 2 | "2021-10-05T20:15:10Z" | "2021-10-06T17:01:37Z" | null | MEMBER | null | null | null | ## Describe the bug
I've preprocessed and uploaded a dataset here: https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed . The dataset is in `.arrow` format.
The dataset can correctly be loaded when doing:
```bash
git lfs install
git clone https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed
```
followed by
```python
from datasets import load_from_disk
ds = load_from_disk("./ami_headset_single_preprocessed")
```
However when I try to directly download the dataset as follows:
```python
from datasets import load_dataset
ds = load_dataset("ami-wav2vec2/ami_headset_single_preprocessed")
```
the following error occurs:
```bash
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
1115 ignore_verifications=ignore_verifications,
1116 try_from_hf_gcs=try_from_hf_gcs,
-> 1117 use_auth_token=use_auth_token,
1118 )
1119
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
635 if not downloaded_from_gcs:
636 self._download_and_prepare(
--> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
638 )
639 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
724 try:
725 # Prepare split will record examples associated to the split
--> 726 self._prepare_split(split_generator, **prepare_split_kwargs)
727 except OSError as e:
728 raise OSError(
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator)
1186 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)
1187 ):
-> 1188 writer.write_table(table)
1189 num_examples, num_bytes = writer.finalize()
1190
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_table(self, pa_table, writer_batch_size)
424 # reorder the arrays if necessary + cast to self._schema
425 # we can't simply use .cast here because we may need to change the order of the columns
--> 426 pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
427 batches: List[pa.RecordBatch] = pa_table.to_batches(max_chunksize=writer_batch_size)
428 self._num_bytes += sum(batch.nbytes for batch in batches)
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib._sanitize_arrays()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast()
/usr/local/lib/python3.7/dist-packages/pyarrow/compute.py in cast(arr, target_type, safe)
279 else:
280 options = CastOptions.unsafe(target_type)
--> 281 return call_function("cast", [arr], options)
282
283
/usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
/usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from struct<train: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, validation: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, test: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>> to list using function cast_list
```
## Expected results
The dataset should be correctly loaded with `load_dataset` IMO.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3034/comments | https://api.github.com/repos/huggingface/datasets/issues/3034/events | https://github.com/huggingface/datasets/issues/3034 | 1,016,759,202 | I_kwDODunzps48moOi | 3,034 | Errors loading dataset using fs = a gcsfs.GCSFileSystem | {
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] | open | false | null | [] | null | 0 | "2021-10-05T20:07:08Z" | "2021-10-05T20:26:39Z" | null | NONE | null | null | null | ## Describe the bug
Cannot load dataset using a `gcsfs.GCSFileSystem`. I'm not sure if this should be a bug in `gcsfs` or here...
Basically what seems to be happening is that since datasets saves datasets as folders and folders aren't "real objects" in gcs, gcsfs raises a 404 error. There are workarounds if you use gcsfs directly to download the file, but as is I can't get `load_from_disk` to work.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# load some dataset
dataset = load_dataset("squad", split="train")
# save it to gcs
import gcsfs
fs = gcsfs.GCSFileSystem(project="my-gs-project")
dataset.save_to_disk("gs://my-bucket/squad", fs=fs)
# try to load it from gcs
from datasets import load_from_disk
dataset2 = load_from_disk("my-bucket/squad", fs=fs)
```
## Expected results
`dataset2` would be a copy of `dataset` but loaded from my bucket.
## Actual results
Long traceback but essentially it's a 404 error from gcsfs saying the object `my-bucket/squad` doesn't exist when this is called:
https://github.com/huggingface/datasets/blob/9c81b7d2e6d9feae69a084a3abda265a4ca07fb5/src/datasets/arrow_dataset.py#L977
This is because there is no actual object called `my-bucket/squad`, there are objects called `my-bucket/squad/dataset.arrow`, etc.
Note that *this* works fine, since it's explicitly saying "download all the objects with this prefix":
```python
fs.download(src_dataset_path + "/*", dataset_path.as_posix(), recursive=True)
```
For example, I can do a workaround this way:
```python
import tempfile
with tempfile.TemporaryDirectory() as temppath:
fs.download("gs://my-bucket/squad/*", temppath)
dataset2 = load_from_disk(temppath)
```
It's unclear to me if it's `gcsfs`'s responsibility to say "hey that's folder not a file, I should try to get objects inside of it not the object itself", or if that's `datasets`'s responsibility... I'm leaning towards the latter since you're never loading a dataset from one file using this function/method, only a dataset folder?
Another minor thing that should maybe should be rolled into this bug...
https://github.com/huggingface/datasets/blob/9c81b7d2e6d9feae69a084a3abda265a4ca07fb5/src/datasets/arrow_dataset.py#L968
These fail if you pass in a `gs://` path, e.g.
```python
dataset2 = load_from_disk("gs://my-bucket/squad", fs=fs)
```
Because at this point, `dataset_info_path` is `gs:/my-bucket/squad/dataset_info.json`, gcsfs throws a:
```
Invalid bucket name: 'gs:'
```
error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: macOS Big Sur 11.6
- Python version: 3.7.12
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3033 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3033/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3033/comments | https://api.github.com/repos/huggingface/datasets/issues/3033/events | https://github.com/huggingface/datasets/pull/3033 | 1,016,619,572 | PR_kwDODunzps4std7u | 3,033 | Actual "proper" install of ruamel.yaml in the windows CI | {
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} | It was impossible to update the package directly with `pip`. Indeed it was installed with `distutils` which prevents `pip` or `conda` to uninstall it.
I had to `rm` a directory from the `site-packages` python directory, and then do `pip install ruamel.yaml`
It's not that "proper" but I couldn't find better solutions | {
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https://api.github.com/repos/huggingface/datasets/issues/3032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3032/comments | https://api.github.com/repos/huggingface/datasets/issues/3032/events | https://github.com/huggingface/datasets/issues/3032 | 1,016,488,475 | I_kwDODunzps48lmIb | 3,032 | Error when loading private dataset with "data_files" arg | {
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] | closed | false | null | [] | null | 1 | "2021-10-05T15:46:27Z" | "2021-10-12T15:26:22Z" | "2021-10-12T15:25:46Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
Private datasets with no loading script can't be loaded using `data_files` parameter.
## Steps to reproduce the bug
```python
from datasets import load_dataset
data_files = {"train": "**/train/*/*.jsonl", "valid": "**/valid/*/*.jsonl"}
dataset = load_dataset('dalle-mini/encoded', data_files=data_files, use_auth_token=True, streaming=True)
```
Same error happens in non-streaming mode.
## Expected results
Files should be loaded (whether in streaming or not).
## Actual results
Error:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)
539 try:
--> 540 local_path = cached_path(file_path, download_config=download_config)
541 except FileNotFoundError:
8 frames
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/dalle-mini/encoded/resolve/main/encoded.py
During handling of the above exception, another exception occurred:
HTTPError Traceback (most recent call last)
HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/datasets/dalle-mini/encoded?full=true
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)
547 except Exception:
548 raise FileNotFoundError(
--> 549 f"Couldn't find a directory or a {resource_type} named '{path}'. "
550 f"It doesn't exist locally at {expected_dir_for_combined_path_abs} or remotely on {hf_api.endpoint}/datasets"
551 )
FileNotFoundError: Couldn't find a directory or a dataset named 'dalle-mini/encoded'. It doesn't exist locally at /content/dalle-mini/encoded or remotely on https://huggingface.co/datasets
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/3031 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3031/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3031/comments | https://api.github.com/repos/huggingface/datasets/issues/3031/events | https://github.com/huggingface/datasets/pull/3031 | 1,016,458,496 | PR_kwDODunzps4ss9jn | 3,031 | Align tqdm control with cache control | {
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} | [] | closed | false | null | [] | null | 1 | "2021-10-05T15:18:49Z" | "2021-10-18T15:00:21Z" | "2021-10-18T14:59:30Z" | CONTRIBUTOR | null | 0 | {
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} | Currently, once disabled with `disable_progress_bar`, progress bars cannot be re-enabled again. To overcome this limitation, this PR introduces the `set_progress_bar_enabled` function that accepts a boolean indicating whether to display progress bars. The goal is to provide a similar API to the existing cache control API. Following the Zen of Python (😄), there should be one and preferably only one obvious way to do it, so I'm also deprecating the aforementioned `disable_progress_bar` function. Additionally, I justify the deprecation with the fact that this function has never been in the docs.
Moreover, similar API changes have recently been introduced to [`tfds`](https://github.com/tensorflow/datasets/blob/a1e8b98f45b0214082b546cc967c67c43fffda55/tensorflow_datasets/core/utils/tqdm_utils.py#L98-L112).
Considering the popularity of the [comment](https://github.com/huggingface/datasets/issues/1627#issuecomment-751383559) I made a while ago, this API (`set_progress_bar_enabled` and `is_progress_bar_enabled`) should be mentioned in the docs, but I'm not sure where to put it exactly. Maybe we can replace the `logging_methods` page under `package_reference` with `utility_methods` and then introduce two subsections on that page: `Logging methods` and `tqdm control`.
Additionally, this PR:
* adds the `disable_tqdm` keyword arg of `Dataset._map_single` to the `ignore_kwargs` list to ignore it when computing the fingerprint (forgot to add it in #2696)
* deletes the unused components in `tqdm_utils.py`, which seem to be inherited from `tfds`
* disables the tqdm output in the test suite. As I see it, this output doesn't seem informative, but let me know if this is not a good idea | {
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} | [] | closed | false | null | [] | null | 4 | "2021-10-05T14:58:33Z" | "2021-10-08T15:33:15Z" | "2021-10-08T15:31:53Z" | CONTRIBUTOR | null | 0 | {
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} | Fixes #2944
WIP
* Not tested yet.
* We might want to allow batched remove for efficiency.
@lhoestq Do you think it should have `batched=` and `batch_size=`? | {
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} | [] | closed | false | null | [] | null | 2 | "2021-10-05T14:19:27Z" | "2021-10-05T14:56:46Z" | "2021-10-05T14:56:45Z" | CONTRIBUTOR | null | 0 | {
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} | The nq_open dataset originally drew the validation set from this file:
https://github.com/google-research-datasets/natural-questions/blob/master/nq_open/NQ-open.efficientqa.dev.1.1.sample.jsonl
However, that's the dev set used specifically and only for the efficientqa competition, and it's not the same dev set as is used in every open-domain question answering paper (including the Lee et al paper that introduced the open-domain variant of NQ, cited at the top of the dataset file). This PR changes nq_open to use the standard validation split and bumps the version to 2.0.0 since this is a breaking change. | {
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```python
load_dataset("lhoestq/demo1")
```
should return two splits "train" and "test" since the dataset repostiory is like
```
data/
├── train.csv
└── test.csv
```
Currently it returns only one split "train" which contains the data of both files
I started playing with this idea on this branch btw: `resolve-data_files-by-split-name`
Basically the idea is that if you named you data files after split names then the default pattern is
```python
{
"train": ["*train*"],
"test": ["*test*"],
"validation": ["*dev*", "valid"],
}
```
otherwise it's
```python
{
"train": ["*"]
}
```
Let me know what you think !
cc @albertvillanova @LysandreJik @vblagoje | {
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Fix #3024. | {
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There is an error during installation of tests dependencies for Windows: https://app.circleci.com/pipelines/github/huggingface/datasets/7981/workflows/9b6a0114-2b8e-4069-94e5-e844dbbdba4e/jobs/49206
```
ERROR: Cannot uninstall 'ruamel-yaml'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
```
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* Dataset Structure
* Data Instances
* Data Splits
* Source Data
* Annotations
* Discussions of Biases
* LIcensing Information | {
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CC: @lewtun | {
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} | If filter is called after using a first transform `shuffle`, `select`, `shard`, `train_test_split`, or `filter`, then it could not work as expected and return examples from before the first transform. This is because the indices mapping was not taken into account when saving the indices to keep when doing the filtering
Affected versions: 1.12.0 and 1.12.1
This should fix #3010 | {
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```python
from datasets import load_dataset
url = "https://domain.org/filename.zip"
data_files = {"train": "train_filename.csv", "test": "test_filename.csv"}
dataset = load_dataset("csv", data_dir=url, data_files=data_files)
```
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Related to #1878. | {
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Related to #2880, #2876, #2874 | {
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Related to #2982. | {
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```
>>> from datasets import get_dataset_infos
>>> get_dataset_infos('wit')
{}
```
While it's totally possible to get it (regenerate it) with:
```
>>> from datasets import load_dataset_builder
>>> builder = load_dataset_builder('wit')
>>> builder.info
DatasetInfo(description='Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set\n of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its\n size enables WIT to be used as a pretraining dataset for multimodal machine learning models.\n', citation='@article{srinivasan2021wit,\n title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},\n author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},\n journal={arXiv preprint arXiv:2103.01913},\n year={2021}\n}\n', homepage='https://github.com/google-research-datasets/wit', license='', features={'b64_bytes': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'image_url': Value(dtype='string', id=None), 'metadata_url': Value(dtype='string', id=None), 'original_height': Value(dtype='int32', id=None), 'original_width': Value(dtype='int32', id=None), 'mime_type': Value(dtype='string', id=None), 'caption_attribution_description': Value(dtype='string', id=None), 'wit_features': Sequence(feature={'language': Value(dtype='string', id=None), 'page_url': Value(dtype='string', id=None), 'attribution_passes_lang_id': Value(dtype='string', id=None), 'caption_alt_text_description': Value(dtype='string', id=None), 'caption_reference_description': Value(dtype='string', id=None), 'caption_title_and_reference_description': Value(dtype='string', id=None), 'context_page_description': Value(dtype='string', id=None), 'context_section_description': Value(dtype='string', id=None), 'hierarchical_section_title': Value(dtype='string', id=None), 'is_main_image': Value(dtype='string', id=None), 'page_changed_recently': Value(dtype='string', id=None), 'page_title': Value(dtype='string', id=None), 'section_title': Value(dtype='string', id=None)}, length=-1, id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='wit', config_name='default', version=0.0.0, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None)
```
Should we test if info is empty, and in that case regenerate it? Or always generate it? | {
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} | As pointed out by @mariosasko in #3001, calling `float()` instad of `.item()` is faster.
Moreover, it might avoid potential issues if any of the third-party functions eventually returns a `float` instead of an `np.float64`.
Related to #3001. | {
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import datasets as ds
builder = ds.load_dataset_builder('sent_comp', name="doesnotexist")
builder.info.config_name
```
returns
```
'doesnotexist'
```
Shouldn't it raise an error instead?
For this dataset, the only valid values for `name` should be: `"default"` or `None` (ie. argument not passed) | {
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] | null | 4 | "2021-10-04T09:04:55Z" | "2022-06-01T17:36:44Z" | "2022-06-01T17:36:44Z" | NONE | null | null | null | ## Describe the bug
As there's no support for lists within dataset fields, I convert my lists to json-string format. However, the bug described is occurring even when the data format is 'string'.
These samples show that filtering behavior diverges from what's expected when chaining filterings.
On sample 2 the second filtering leads to "leaking" of data that should've been filtered on the first filtering into the results.
## Steps to reproduce the bug
Sample 1:
```python
import datasets
import json
items = [[1, 2], [3], [4]]
jsoned_items = map(json.dumps, [[1, 2], [3], [4]])
ds = datasets.Dataset.from_dict({'a': jsoned_items})
print(list(ds))
# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] as expected
filtered = ds
# get all lists that are shorter than 2
filtered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)
print(list(filtered))
# > Prints: [{'a': '[3]'}, {'a': '[4]'}] as expected
# get all lists, which have a value bigger than 3 on its zero index
filtered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)
print(list(filtered))
# > Should be: [{'a': [4]}]
# > Prints: [{'a': [3]}]
```
Sample 2:
```python
import datasets
import json
items = [[1, 2], [3], [4]]
jsoned_items = map(json.dumps, [[1, 2], [3], [4]])
ds = datasets.Dataset.from_dict({'a': jsoned_items})
print(list(ds))
# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}]
filtered = ds
# get all lists, which have a value bigger than 3 on its zero index
filtered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)
print(list(filtered))
# > Prints: [{'a': '[4]'}] as expected
# get all lists that are shorter than 2
filtered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)
print(list(filtered))
# > Prints: [{'a': '[1, 2]'}]
# > Should be: [{'a': '[4]'}] (remain intact)
```
## Expected results
Expected and actual results are attached to the code snippets.
## Actual results
Expected and actual results are attached to the code snippets.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.9.7
- PyArrow version: 5.0.0
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Related to #2884, #2783 and #2619. | {
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https://api.github.com/repos/huggingface/datasets/issues/3006 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3006/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3006/comments | https://api.github.com/repos/huggingface/datasets/issues/3006/events | https://github.com/huggingface/datasets/pull/3006 | 1,014,770,821 | PR_kwDODunzps4snsBm | 3,006 | Fix Windows paths in CommonLanguage dataset | {
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} | Minor fix in CommonLanguage dataset for Windows pathname component separator.
Related to #2989. | {
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The ".filter" method of DatasetDict or Dataset objects fails when passing any "fn_kwargs" argument
## Steps to reproduce the bug
```python
import datasets
example_dataset = datasets.Dataset.from_dict({"a": {1, 2, 3, 4}})
def filter_value(example, value):
return example['a'] == value
filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})
```
## Expected results
`filtered` is a dataset containing {"a": {3}}
## Actual results
> Traceback (most recent call last):
> File "C:\Users\qsemi\Documents\git\nlp_experiments\gpt_celebrity\src\test_faulty_filter.py", line 8, in <module>
> filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper
> out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper
> out = func(self, *args, **kwargs)
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2169, in filter
> indices = self.map(
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1686, in map
> return self._map_single(
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper
> out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper
> out = func(self, *args, **kwargs)
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2048, in _map_single
> batch = apply_function_on_filtered_inputs(
> File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1939, in apply_function_on_filtered_inputs
> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
> TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'value'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.9.7
- PyArrow version: 5.0.0
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} | Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE.
As in GLUE and SuperGLUE (Wang et al., 2019b,a), one of our goals is to push towards generic (or ‘foundation’) models that can cope with multiple NLP tasks, in our case legal NLP tasks possibly with limited task-specific fine-tuning. Another goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways to make it easier for newcomers and generic models to address all tasks.
LexGLUE benchmark is accompanied by experimental infrastructure that relies on Hugging Face Transformers library and resides at: https://github.com/coastalcph/lex-glue. | {
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https://api.github.com/repos/huggingface/datasets/issues/3003 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3003/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3003/comments | https://api.github.com/repos/huggingface/datasets/issues/3003/events | https://github.com/huggingface/datasets/pull/3003 | 1,014,137,933 | PR_kwDODunzps4smExP | 3,003 | common_language: Fix license in README.md | {
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} | This [comment](https://github.com/huggingface/datasets/issues/2934#issuecomment-922970919) explains the issue. This PR fixes that with a `weakref` callback, and additionally:
* renames `TensorflowDatasetMixIn` to `TensorflowDatasetMixin` for consistency
* correctly indents `TensorflowDatasetMixin`'s docstring
* replaces `tf.data.AUTOTUNE` with `tf.data.experimental.AUTOTUNE` (we support TF>=2.2 according to the [setup.py](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/setup.py#L188) and `AUTOTUNE` has been moved to the experimental part of `tf.data` in 1.X if I'm not mistaken)
Fixes #2934 | {
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} | This PR is motivated by issue #2964.
The Matthews Correlation metric relies on sklearn's `matthews_corrcoef` function to compute the result. This function returns either `float` or `np.float64` (see the [source](https://github.com/scikit-learn/scikit-learn/blob/844b4be24d20fc42cc13b957374c718956a0db39/sklearn/metrics/_classification.py#L906-L909)). Obviously, calling `.item()` on the float value will fail, so I'm fixing this with the built-in `float()` function, which covers both cases. Surprisingly, on my machine, casting `np.float64` to a Python scalar with `float()` is even faster than with the `.item()` method. | {
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} | Sometimes the arrow json parser fails if the `block_size` is too small and returns an `ArrowNotImplementedError: JSON conversion to struct...` error.
By increasing the block size it makes it work again.
Hopefully it should help with https://github.com/huggingface/datasets/issues/2799
I tried with the file mentioned in the issue and it worked for me
cc @lewtun can you try again from this branch ? | {
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] | open | false | null | [] | null | 0 | "2021-10-01T13:49:52Z" | "2021-10-01T13:49:52Z" | null | NONE | null | null | null | ## Describe the bug
dataset loaded from disk cannot be shuffled.
## Steps to reproduce the bug
```
my_dataset = load_from_disk('s3://my_file/validate', fs=s3)
sample = my_dataset.select(range(100)).shuffle(seed=1234)
```
## Actual results
```
sample = my_dataset .select(range(100)).shuffle(seed=1234)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper
out = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2494, in shuffle
new_fingerprint=new_fingerprint,
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper
out = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2303, in select
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 547, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 258, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpnnu5uhnx/my_file/validate/tmpy76d70g4'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Python version: 3.7
- PyArrow version: 5.0.0
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This PR fixes it, allowing loading compressed/uncompressed CSV files in streaming/non-streaming mode.
Fix #2977. | {
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] | null | 3 | "2021-09-30T23:00:18Z" | "2021-10-01T19:07:23Z" | "2021-10-01T19:07:22Z" | MEMBER | null | null | null | ## Describe the bug
For some reason, I can't download `trivia_qa/unfilted`. A file seems to be missing... I am able to see it fine though the viewer tough...
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> load_dataset("trivia_qa", "unfiltered")
Downloading and preparing dataset trivia_qa/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to /gpfsscratch/rech/six/commun/datasets/trivia_qa/unfiltered/1.1.0/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6...
Traceback (most recent call last):
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 251, in _add_context
with open(os.path.join(file_dir, fname), encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/gpfsscratch/rech/six/commun/datasets/downloads/extracted/9fcb7eddc6afd46fd074af3c5128931dfe4b548f933c925a23847faf4c1995ad/evidence/wikipedia/Peanuts.txt'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/load.py", line 852, in load_dataset
use_auth_token=use_auth_token,
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 616, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split
disable=bool(logging.get_verbosity() == logging.NOTSET),
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 303, in _generate_examples
example = parse_example(article)
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 274, in parse_example
_add_context(article.get("EntityPages", []), "WikiContext", wiki_dir),
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 253, in _add_context
except (IOError, datasets.Value("errors").NotFoundError):
File "<string>", line 5, in __init__
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/features.py", line 265, in __post_init__
self.pa_type = string_to_arrow(self.dtype)
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/features.py", line 134, in string_to_arrow
f"Neither {datasets_dtype} nor {datasets_dtype + '_'} seems to be a pyarrow data type. "
ValueError: Neither errors nor errors_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
```
## Expected results
I am able to load another subset (`rc`), but unable to load.
I am not sure why the try/except doesn't catch it...
https://github.com/huggingface/datasets/blob/9675a5a1e7b99a86f9c250f6ea5fa5d1e6d5cc7d/datasets/trivia_qa/trivia_qa.py#L253
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-4.18.0-147.51.2.el8_1.x86_64-x86_64-with-redhat-8.1-Ootpa
- Python version: 3.7.10
- PyArrow version: 3.0.0
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] | open | false | null | [] | null | 0 | "2021-09-30T13:22:01Z" | "2021-09-30T13:22:01Z" | null | CONTRIBUTOR | null | null | null | Unless I'm mistaken, it seems that in the new documentation it is no longer mentioned that you can use Unix style pattern matching in the `data_files` argument of the `load_dataset` method.
This feature was mentioned [here](https://huggingface.co/docs/datasets/loading_datasets.html#from-a-community-dataset-on-the-hugging-face-hub) in the previous documentation.
I'd love to hear your opinion @lhoestq , @albertvillanova and @stevhliu | {
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https://api.github.com/repos/huggingface/datasets/issues/2990 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2990/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2990/comments | https://api.github.com/repos/huggingface/datasets/issues/2990/events | https://github.com/huggingface/datasets/pull/2990 | 1,012,097,418 | PR_kwDODunzps4sgLt5 | 2,990 | Make Dataset.map accept list of np.array | {
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https://api.github.com/repos/huggingface/datasets/issues/2989 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2989/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2989/comments | https://api.github.com/repos/huggingface/datasets/issues/2989/events | https://github.com/huggingface/datasets/pull/2989 | 1,011,220,375 | PR_kwDODunzps4sdlt1 | 2,989 | Add CommonLanguage | {
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} | This PR adds the Common Language dataset (https://zenodo.org/record/5036977)
The dataset is intended for language-identification speech classifiers and is already used by models on the Hub:
* https://huggingface.co/speechbrain/lang-id-commonlanguage_ecapa
* https://huggingface.co/anton-l/wav2vec2-base-langid
cc @patrickvonplaten | {
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] | closed | false | null | [] | null | 13 | "2021-09-29T16:04:24Z" | "2022-04-10T14:49:49Z" | "2022-04-10T14:49:49Z" | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
Hi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error, since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq
## Steps to reproduce the bug
```
Traceback (most recent call last):
File "run_clm.py", line 723, in <module>
main()
File "run_clm.py", line 669, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/transformers/trainer.py", line 1258, in train
for step, inputs in enumerate(epoch_iterator):
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1530, in __getitem__
format_kwargs=self._format_kwargs,
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1517, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 368, in query_table
_check_valid_index_key(key, size)
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 311, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 14 is out of bounds for size 0
```
## Expected results
not getting the index error
## Actual results
Please see the above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets 1.12.1
- Platform: linux
- Python version: 3.7.11
- PyArrow version: 5.0.0
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For the ViT and LayoutLMv2 demo notebooks in my [Transformers-Tutorials repo](https://github.com/NielsRogge/Transformers-Tutorials), people reported an ArrowInvalid issue after applying the following function to a Dataset:
```
def preprocess_data(examples):
images = [Image.open(path).convert("RGB") for path in examples['image_path']]
words = examples['words']
boxes = examples['bboxes']
word_labels = examples['ner_tags']
encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,
padding="max_length", truncation=True)
return encoded_inputs
```
```
Full trace:
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-8-0fc3efc6f0c2> in <module>()
27
28 train_dataset = datasets['train'].map(preprocess_data, batched=True, remove_columns=datasets['train'].column_names,
---> 29 features=features)
30 test_dataset = datasets['test'].map(preprocess_data, batched=True, remove_columns=datasets['test'].column_names,
31 features=features)
13 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1701 new_fingerprint=new_fingerprint,
1702 disable_tqdm=disable_tqdm,
-> 1703 desc=desc,
1704 )
1705 else:
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
183 }
184 # apply actual function
--> 185 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
186 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
187 # re-apply format to the output
/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
396 # Call actual function
397
--> 398 out = func(self, *args, **kwargs)
399
400 # Update fingerprint of in-place transforms + update in-place history of transforms
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2063 writer.write_table(batch)
2064 else:
-> 2065 writer.write_batch(batch)
2066 if update_data and writer is not None:
2067 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
409 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
410 typed_sequence_examples[col] = typed_sequence
--> 411 pa_table = pa.Table.from_pydict(typed_sequence_examples)
412 self.write_table(pa_table, writer_batch_size)
413
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
106 storage = numpy_to_pyarrow_listarray(self.data, type=type.value_type)
107 else:
--> 108 storage = pa.array(self.data, type.storage_dtype)
109 out = pa.ExtensionArray.from_storage(type, storage)
110 elif isinstance(self.data, np.ndarray):
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Can only convert 1-dimensional array values
```
It can be fixed by adding the following line:
```diff
def preprocess_data(examples):
images = [Image.open(path).convert("RGB") for path in examples['image_path']]
words = examples['words']
boxes = examples['bboxes']
word_labels = examples['ner_tags']
encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,
padding="max_length", truncation=True)
+ encoded_inputs["image"] = np.array(encoded_inputs["image"])
return encoded_inputs
```
However, would be great if this can be fixed within Datasets itself. | {
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} | ## Refactor the module factory
When trying to extend the `data_files` logic to avoid doing unnecessary ETag requests, I noticed that the module preparation mechanism needed a refactor:
- the function was 600 lines long
- it was not readable
- it contained many different cases that made it complex to maintain
- it was hard to properly test it
- it was hard to extend without breaking anything
The module preparation mechanism is in charge of taking the name of a dataset or a metric given by the user (ex: "squad", "accuracy", "lhoestq/test", "path/to/my/script.py", "path/to/my/data/directory", "json", "csv") and return a module (possibly downloaded from the Hub) that contains the dataset builder or the metric class to use.
### Implementation details
I decided to separate all these use cases into different dataset/metric module factories.
First, the metric module factories:
- **CanonicalMetricModuleFactory**: "accuracy", "rouge", ...
- **LocalMetricModuleFactory**: "path/to/my/metric.py"
Then, the dataset module factories:
- **CanonicalDatasetModuleFactory**: "squad", "glue", ...
- **CommunityDatasetModuleFactoryWithScript**: "lhoestq/test"
- **CommunityDatasetModuleFactoryWithoutScript**: "lhoestq/demo1"
- **PackagedDatasetModuleFactory**: "json", "csv", ...
- **LocalDatasetModuleFactoryWithScript**: "path/to/my/script.py"
- **LocalDatasetModuleFactoryWithoutScript**: "path/to/my/data/directory"
And finally, additional factories when users have no internet:
- **CachedDatasetModuleFactory**
- **CachedMetricModuleFactory**
### Breaking changes
One thing is that I still don't know at what extent we want to keep backward compatibility for `prepare_module`. For now I just kept it (except I removed two parameters) just in case, but it's not used anywhere anymore.
## Avoid etag requests for hub datasets
To do this I added a class `DataFilesDict` that can be hashed to define the cache directory of the dataset.
It contains the usual data files formatted as `{"train": ["train.txt"]}` for example.
But each list of file is a `DataFilesList` that also has a `origin_metadata` attribute that contains metadata about the origin of each file:
- for URLs: it stores the ETags of the files
- for local files: it stores the last modification data
- for files from a Hugging Face repository on the Hub: it stores the pattern (`*`, `*.csv`, "train.txt", etc.) and the commit sha of the repository (so there're no ETag requests !)
This way if any file changes, the hash of the `DataFilesDict` changes too !
You can instantiate a `DataFilesDict` by using patterns for local/remote files or files in a HF repository:
- for local/remote files: `DataFilesDict.from_local_or_remote(patterns)`
- for files in a HF repository: `DataFilesDict.from_hf_repo(patterns, dataset_info)`
Fix #2859
## TODO
Fix the latest test:
- [x] fix the call to dataset_info in offline mode (related to https://github.com/huggingface/huggingface_hub/issues/372)
Add some more tests:
- [x] test all the factories
- [x] test the new data files logic
Other:
- [x] docstrings
- [x] comments | {
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} | ## Adding a Dataset
- **Name:** *KanHope*
- **Description:** *A code-mixed English-Kannada dataset for Hope speech detection*
- **Task:** *Binary Text Classification*
- **Paper:** *https://arxiv.org/abs/2108.04616*
- **Data:** *https://github.com/adeepH/kan_hope/tree/main/dataset*
- **Motivation:** *The dataset is amongst the very few resources available for code-mixed low-resourced Dravidian languages of India* | {
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] | null | 1 | "2021-09-29T04:49:22Z" | "2021-10-12T06:05:42Z" | "2021-10-12T06:05:42Z" | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
When using `load_dataset` with json files, if the files are too large, there will be "Exceeded maximum rows" error.
## Steps to reproduce the bug
```python
dataset = load_dataset('json', data_files=data_files) # data files have 3M rows in a single file
```
## Expected results
No error
## Actual results
```
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
134 with open(file, encoding="utf-8") as f:
--> 135 dataset = json.load(f)
136 except json.JSONDecodeError:
~/anaconda3/envs/python/lib/python3.9/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
292 """
--> 293 return loads(fp.read(),
294 cls=cls, object_hook=object_hook,
~/anaconda3/envs/python/lib/python3.9/json/__init__.py in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
345 parse_constant is None and object_pairs_hook is None and not kw):
--> 346 return _default_decoder.decode(s)
347 if cls is None:
~/anaconda3/envs/python/lib/python3.9/json/decoder.py in decode(self, s, _w)
339 if end != len(s):
--> 340 raise JSONDecodeError("Extra data", s, end)
341 return obj
JSONDecodeError: Extra data: line 2 column 1 (char 20321)
During handling of the above exception, another exception occurred:
ArrowInvalid Traceback (most recent call last)
<ipython-input-20-ab3718a6482f> in <module>
----> 1 dataset = load_dataset('json', data_files=data_files)
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
841
842 # Download and prepare data
--> 843 builder_instance.download_and_prepare(
844 download_config=download_config,
845 download_mode=download_mode,
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
606 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
607 if not downloaded_from_gcs:
--> 608 self._download_and_prepare(
609 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
610 )
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
684 try:
685 # Prepare split will record examples associated to the split
--> 686 self._prepare_split(split_generator, **prepare_split_kwargs)
687 except OSError as e:
688 raise OSError(
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _prepare_split(self, split_generator)
1153 generator = self._generate_tables(**split_generator.gen_kwargs)
1154 with ArrowWriter(features=self.info.features, path=fpath) as writer:
-> 1155 for key, table in utils.tqdm(
1156 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)
1157 ):
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
135 dataset = json.load(f)
136 except json.JSONDecodeError:
--> 137 raise e
138 raise ValueError(
139 f"Not able to read records in the JSON file at {file}. "
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
114 while True:
115 try:
--> 116 pa_table = paj.read_json(
117 BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
118 )
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Exceeded maximum rows
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: 3.9
- PyArrow version: 4.0.1
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} | Resolves #2902 based on conversation there - would also close #2810. Open to suggestions/help 😀
CC @hassiahk @lhoestq @yjernite | {
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] | open | false | null | [] | null | 3 | "2021-09-28T15:04:36Z" | "2021-09-29T17:25:14Z" | null | CONTRIBUTOR | null | null | null | ## Adding a Dataset
- **Name:** *SLR25*
- **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https://huggingface.co/datasets/openslr, 25 covers Amharic, Swahili and Wolof data*
- **Paper:** *https://www.openslr.org/25/ has citations for each of the three subsubsets. *
- **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps://www.openslr.org/25/*
- **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.*
https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do.
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] | null | 1 | "2021-09-28T11:34:53Z" | "2021-10-01T14:17:38Z" | "2021-10-01T14:17:38Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
When I try to compute the f1 score for each class in a multiclass classification problem, I get a ValueError. The same happens with recall and precision. I traced the error to the `.item()` in these scripts, which is probably there for the other averages. E.g. from f1.py:
```python
return {
"f1": f1_score(
references,
predictions,
labels=labels,
pos_label=pos_label,
average=average,
sample_weight=sample_weight,
).item(),
}
```
Since the result is an array with more than one item, the `.item()` throws the error. I didn't submit a PR because this might be needed for the other averages, I'm not very familiar with the library
## Steps to reproduce the bug
```python
from datasets import load_metric
metric = load_metric("f1")
metric.add_batch(predictions=[2,34,1,34,1,2,3], references=[23,52,1,3,523,5,8])
metric.compute(average=None)
```
## Expected results
`array([0.66666667, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ])`
## Actual results
ValueError: can only convert an array of size 1 to a Python scalar
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.5
- PyArrow version: 5.0.0
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As discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`. | {
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It is not possible to load from a compressed csv anymore.
## Steps to reproduce the bug
```python
load_dataset('csv', data_files=['/path/to/csv.bz2'])
```
## Problem and possible solution
This used to work, but the commit that broke it is [this one](https://github.com/huggingface/datasets/commit/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782).
`pandas` usually gets the compression information from the filename itself (which was previously directly passed). Now, since it gets a file descriptor, it might be good to auto-infer the compression or let the user pass the `compression` kwarg to `load_dataset` (or maybe warn the user if the file ends with a commonly known compression scheme?).
## Environment info
- `datasets` version: 1.10.0 (and over)
- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 3.0.0
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```
from datasets import load_dataset
raw_datasets = load_dataset("wikitext")
```
ValueError: Config name is missing.
Please pick one among the available configs: ['wikitext-103-raw-v1', 'wikitext-2-raw-v1', 'wikitext-103-v1', 'wikitext-2-v1']
Example of usage:
`load_dataset('wikitext', 'wikitext-103-raw-v1')`.
If I try
```
from datasets import load_dataset
raw_datasets = load_dataset("wikitext-2-v1")
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.12.1/datasets/wikitext-2-v1/wikitext-2-v1.py
#### Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic (colab)
- Python version: 3.7.12
- PyArrow version: 3.0.0
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Added the `dataset_infos.json` to the ignored files list and also added check to ignore files which have parent directory as `dummy`.
Let me know if it is correct. Thanks :) | {
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@lhoestq I'm sorry >.> | {
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I'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `/` disk having no enough space.
The file system structure is like below. The root `/` has `115G` disk space available, and the `sda1` is mounted to `/mnt`, which has `1.2T` disk space available:
```
/
/mnt/sda1/path/to/args.dataset_cache_dir
```
## Steps to reproduce the bug
```python
dataset_config = DownloadConfig(
cache_dir=os.path.abspath(args.dataset_cache_dir),
resume_download=True,
)
dataset = load_dataset("natural_questions", download_config=dataset_config)
```
## Expected results
Can download the dataset without an error.
## Actual results
The following error raised:
```
OSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Ubuntu 18.04
- Python version: 3.8.10
- PyArrow version:
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Masakhaner dataset is not loading
## Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("masakhaner",'amh')
```
## Expected results
Expected the return of a dataset
## Actual results
```
NonMatchingSplitsSizesError Traceback (most recent call last)
<ipython-input-3-a6abc1161d4c> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("masakhaner",'amh')
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py
in verify_splits(expected_splits, recorded_splits)
72 ]
73 if len(bad_splits) > 0:
---> 74 raise NonMatchingSplitsSizesError(str(bad_splits))
75 logger.info("All the splits matched successfully.")
76
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=639927, num_examples=1751, dataset_name='masakhaner'), 'recorded': SplitInfo(name='train', num_bytes=639911, num_examples=1750, dataset_name='masakhaner')}, {'expected': SplitInfo(name='validation', num_bytes=92768, num_examples=251, dataset_name='masakhaner'), 'recorded': SplitInfo(name='validation', num_bytes=92753, num_examples=250, dataset_name='masakhaner')}, {'expected': SplitInfo(name='test', num_bytes=184286, num_examples=501, dataset_name='masakhaner'), 'recorded': SplitInfo(name='test', num_bytes=184271, num_examples=500, dataset_name='masakhaner')}]
```
## Environment info
Google Colab
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https://api.github.com/repos/huggingface/datasets/issues/2970 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2970/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2970/comments | https://api.github.com/repos/huggingface/datasets/issues/2970/events | https://github.com/huggingface/datasets/issues/2970 | 1,007,340,089 | I_kwDODunzps48Cso5 | 2,970 | Magnet’s | {
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- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | {
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A clear and concise description of what the bug is.
When I attempt to download the huggingface datatset medical_dialog it errors out midway through
## Steps to reproduce the bug
```python
raw_datasets = load_dataset("medical_dialog", "en", split="train", download_mode="force_redownload", data_dir="./Medical-Dialogue-Dataset-English")
```
## Expected results
A clear and concise description of the expected results.
No error
## Actual results
```
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_splits(expected_splits, recorded_splits)
72 ]
73 if len(bad_splits) > 0:
---> 74 raise NonMatchingSplitsSizesError(str(bad_splits))
75 logger.info("All the splits matched successfully.")
76
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=295097913, num_examples=229674, dataset_name='medical_dialog')}]
```
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.21.1
- Platform: colab
- Python version: colab 3.7
- PyArrow version: N/A
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I'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly.
For `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.
## Steps to reproduce the bug
The following works as expected:
```python
from datasets import load_dataset
ds = load_dataset("lhoestq/custom_squad")
ds['train'].to_parquet("./ds/train/split.parquet")
ds['validation'].to_parquet("./ds/validation/split.parquet")
brand_new_dataset = load_dataset("ds")
```
Modifying a single split to add a new feature ends up in a crash:
```python
from datasets import load_dataset
ds = load_dataset("lhoestq/custom_squad")
def identical_answers(e):
e['identical_answers'] = len(set(e['answers']['text'])) == 1
return e
ds['validation'] = ds['validation'].map(identical_answers)
ds['train'].to_parquet("./ds/train/split.parquet")
ds['validation'].to_parquet("./ds/validation/split.parquet")
brand_new_dataset = load_dataset("ds")
```
```
File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 26, in <module>
brand_new_dataset = load_dataset("ds")
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1151, in load_dataset
builder_instance.download_and_prepare(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 642, in download_and_prepare
self._download_and_prepare(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 732, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 1194, in _prepare_split
writer.write_table(table)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in <listcomp>
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1257, in pyarrow.lib.Table.__getitem__
File "pyarrow/table.pxi", line 1833, in pyarrow.lib.Table.column
File "pyarrow/table.pxi", line 1808, in pyarrow.lib.Table._ensure_integer_index
KeyError: 'Field "identical_answers" does not exist in table schema'
```
It does work, however, to use the `save_to_disk` and `load_from_disk` methods:
```py
from datasets import load_from_disk
ds = load_dataset("lhoestq/custom_squad")
def identical_answers(e):
e['identical_answers'] = len(set(e['answers']['text'])) == 1
return e
ds['validation'] = ds['validation'].map(identical_answers)
ds.save_to_disk("local_path")
brand_new_dataset = load_from_disk("local_path")
```
## Expected results
The saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.
If it's helpful, I've traced a possible patch to the `write_table` method here:
https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L424-L425
The writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:
https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L254-L255
but the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:
https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/builder.py#L1190
Updating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:
```
File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 27, in <module>
brand_new_dataset = load_dataset("ds")
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1163, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 819, in as_dataset
datasets = utils.map_nested(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 207, in map_nested
mapped = [
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 208, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 143, in _single_map_nested
return function(data_struct)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 850, in _build_single_dataset
ds = self._as_dataset(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 920, in _as_dataset
dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 217, in read
return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 238, in read_files
pa_table = self._read_files(files, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 173, in _read_files
pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 308, in _get_table_from_filename
table = ArrowReader.read_table(filename, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 327, in read_table
return table_cls.from_file(filename)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 458, in from_file
table = _memory_mapped_arrow_table_from_file(filename)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 45, in _memory_mapped_arrow_table_from_file
pa_table = opened_stream.read_all()
File "pyarrow/ipc.pxi", line 563, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow/error.pxi", line 114, in pyarrow.lib.check_status
OSError: Header-type of flatbuffer-encoded Message is not RecordBatch.
```
## Environment info
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
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] | closed | false | null | [] | null | 0 | "2021-09-25T20:58:15Z" | "2021-10-03T20:34:22Z" | "2021-10-03T20:34:22Z" | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
Would you like to add any vision-and-language datasets (e.g., VQA, VCR) to Huggingface Datasets?
**Describe the solution you'd like**
N/A
**Describe alternatives you've considered**
N/A
**Additional context**
This is Da Yin at UCLA. Recently, we have published an EMNLP 2021 paper about geo-diverse visual commonsense reasoning (https://arxiv.org/abs/2109.06860). We propose a new dataset called GD-VCR, a vision-and-language dataset to evaluate how well V&L models perform on scenarios involving geo-location-specific commonsense. We hope to have our V&L dataset incorporated into Huggingface to further promote our project, but I haven't seen much V&L datasets in the current package. Is it possible to add V&L datasets, and if so, how should we prepare for the loading? Thank you very much!
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https://api.github.com/repos/huggingface/datasets/issues/2965 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2965/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2965/comments | https://api.github.com/repos/huggingface/datasets/issues/2965/events | https://github.com/huggingface/datasets/issues/2965 | 1,007,084,153 | I_kwDODunzps48BuJ5 | 2,965 | Invalid download URL of WMT17 `zh-en` data | {
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"name": "dataset bug",
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"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
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] | closed | false | null | [] | null | 1 | "2021-09-25T13:17:32Z" | "2022-08-31T06:47:11Z" | "2022-08-31T06:47:10Z" | NONE | null | null | null | ## Describe the bug
Partial data (wmt17 zh-en) cannot be downloaded due to an invalid URL.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('wmt17','zh-en')
```
## Expected results
ConnectionError: Couldn't reach ftp://cwmt-wmt:[email protected]/parallel/casia2015.zip | {
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] | closed | false | null | [] | null | 2 | "2021-09-24T15:55:21Z" | "2021-09-25T08:06:07Z" | "2021-09-25T08:06:07Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
After loading the metric named "[Matthews Correlation Coefficient](https://huggingface.co/metrics/matthews_correlation)" from `🤗datasets`, the `.compute` method fails with the following exception `AttributeError: 'float' object has no attribute 'item'` (complete stack trace can be provided if required).
## Steps to reproduce the bug
```python
import torch
predictions = torch.ones((10,))
references = torch.zeros((10,))
from datasets import load_metric
METRIC = load_metric("matthews_correlation")
result = METRIC.compute(predictions=predictions, references=references)
```
## Expected results
We should expect a Python `dict` as it follows:
```
{
"matthews_correlation": float()
}
```
as defined in https://github.com/huggingface/datasets/blob/master/metrics/matthews_correlation/matthews_correlation.py, so the fix will imply removing `.item()`, since the value returned by the `scikit-learn` function is not a `torch.Tensor` but a `float`, which means that the `.item()` will fail.
## Actual results
```
Traceback (most recent call last):
File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 59, in main
app()
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 214, in __call__
return get_command(self)(*args, **kwargs)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1137, in __call__
return self.main(*args, **kwargs)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1062, in main
rv = self.invoke(ctx)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1668, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 763, in invoke
return __callback(*args, **kwargs)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 500, in wrapper
return callback(**use_params) # type: ignore
File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 43, in train
metrics = trainer.evaluate()
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2051, in evaluate
output = eval_loop(
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2292, in evaluation_loop
metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))
File "/home/alvaro.bartolome/XXX/xxx/metrics.py", line 20, in compute_metrics
res = METRIC.compute(predictions=predictions, references=eval_preds.label_ids)
File "/home/alvaro.bartolome/miniconda3/envs/lang/lib/python3.9/site-packages/datasets/metric.py", line 402, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "/home/alvaro.bartolome/.cache/huggingface/modules/datasets_modules/metrics/matthews_correlation/0275f1e9a4d318e3ea8cdd87547ee0d58d894966616052e3d18444ac8ddd2357/matthews_correlation.py", line 88, in _compute
"matthews_correlation": matthews_corrcoef(references, predictions, sample_weight=sample_weight).item(),
AttributeError: 'float' object has no attribute 'item'
```
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-4.15.0-1113-azure-x86_64-with-glibc2.23
- Python version: 3.9.7
- PyArrow version: 5.0.0
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