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https://api.github.com/repos/huggingface/datasets/issues/6522 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6522/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6522/comments | https://api.github.com/repos/huggingface/datasets/issues/6522/events | https://github.com/huggingface/datasets/issues/6522 | 2,052,332,528 | I_kwDODunzps56VBvw | 6,522 | Loading HF Hub Dataset (private org repo) fails to load all features | {
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} | [] | open | false | null | [] | null | 0 | "2023-12-21T12:26:35" | "2023-12-21T13:24:31" | null | NONE | null | ### Describe the bug
When pushing a `Dataset` with multiple `Features` (`input`, `output`, `tags`) to Huggingface Hub (private org repo), and later downloading the `Dataset`, only `input` and `output` load - I believe the expected behavior is for all `Features` to be loaded by default?
### Steps to reproduce the bug
Pushing the data. `data_concat` is a `list` of `dict`s.
```python
for datum in data_concat:
datum_tags = {d["key"]: d["value"] for d in datum["tags"]}
split_fraction = # some logic that generates a train/test split number
if split_faction < test_fraction:
data_test.append(datum)
else:
data_train.append(datum)
dataset = DatasetDict(
{
"train": Dataset.from_list(data_train),
"test": Dataset.from_list(data_test),
"full": Dataset.from_list(data_concat),
},
)
dataset_shuffled = dataset.shuffle(seed=shuffle_seed)
dataset_shuffled.push_to_hub(
repo_id=hf_repo_id,
private=True,
config_name=m,
revision=revision,
token=hf_token,
)
```
Loading it later:
```python
dataset = datasets.load_dataset(
path=hf_repo_id,
name=name,
token=hf_token,
)
```
Produces:
```
DatasetDict({
train: Dataset({
features: ['input', 'output'],
num_rows: <obfuscated>
})
test: Dataset({
features: ['input', 'output'],
num_rows: <obfuscated>
})
full: Dataset({
features: ['input', 'output'],
num_rows: <obfuscated>
})
})
```
### Expected behavior
The expected result is below:
```
DatasetDict({
train: Dataset({
features: ['input', 'output', 'tags'],
num_rows: <obfuscated>
})
test: Dataset({
features: ['input', 'output', 'tags'],
num_rows: <obfuscated>
})
full: Dataset({
features: ['input', 'output', 'tags'],
num_rows: <obfuscated>
})
})
```
My workaround is as follows:
```python
dsinfo = datasets.get_dataset_config_info(
path=data_files,
config_name=data_config,
token=hf_token,
)
allfeatures = dsinfo.features.copy()
if "tags" not in allfeatures:
allfeatures["tags"] = [{"key": Value(dtype="string", id=None), "value": Value(dtype="string", id=None)}]
dataset = datasets.load_dataset(
path=data_files,
name=data_config,
features=allfeatures,
token=hf_token,
)
```
Interestingly enough (and perhaps a related bug?), if I don't add the `tags` to `allfeatures` above (i.e. only loading `input` and `output`), it throws an error when executing `load_dataset`:
```
ValueError: Couldn't cast
tags: list<element: struct<key: string, value: string>>
child 0, element: struct<key: string, value: string>
child 0, key: string
child 1, value: string
input: <obfuscated>
output: <obfuscated>
-- schema metadata --
huggingface: '{"info": {"features": {"tags": [{"key": {"dtype": "string",' + 532
to
{'input': <obfuscated>, 'output': <obfuscated>
because column names don't match
```
Traceback for this:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/load.py", line 2152, in load_dataset
builder_instance.download_and_prepare(
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 948, in download_and_prepare
self._download_and_prepare(
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1043, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1805, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1950, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Environment info
- `datasets` version: 2.15.0
- Platform: macOS-14.0-arm64-arm-64bit
- Python version: 3.11.5
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0 | {
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] | open | false | null | [] | null | 0 | "2023-12-21T11:17:27" | "2023-12-21T14:04:32" | null | MEMBER | null | We had a regression and the order of the splits is not preserved. They are alphabetically sorted, instead of preserving original "train", "validation", "test" order.
Check: In branch "main"
```python
In [9]: dataset = load_dataset("adversarial_qa", '"adversarialQA")
In [10]: dataset
Out[10]:
DatasetDict({
test: Dataset({
features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
num_rows: 3000
})
train: Dataset({
features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
num_rows: 30000
})
validation: Dataset({
features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
num_rows: 3000
})
})
```
Before (2.15.0) it was:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
num_rows: 30000
})
validation: Dataset({
features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
num_rows: 3000
})
test: Dataset({
features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
num_rows: 3000
})
})
```
See: https://huggingface.co/datasets/adversarial_qa/discussions/3 | {
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CC: @Wauplin | {
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} | [] | closed | false | null | [] | null | 4 | "2023-12-20T15:16:45" | "2023-12-21T14:48:20" | "2023-12-21T14:40:57" | MEMBER | null | Support `push_to_hub` canonical datasets.
This is necessary in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet
Note that before this PR, the `repo_id` "dataset_name" was transformed to "user/dataset_name". This behavior was introduced by:
- #6269 | {
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metadata_patterns = get_metadata_patterns(base_path, download_config=self.download_config) | {
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} | [] | closed | false | null | [] | null | 2 | "2023-12-20T07:52:29" | "2023-12-20T08:51:34" | "2023-12-20T08:44:44" | MEMBER | null | Support `huggingface-hub` pre-releases.
This way we will have our CI green when testing `huggingface-hub` release candidates. See: https://github.com/huggingface/datasets/tree/ci-test-huggingface-hub-v0.20.0.rc1
Close #6513. | {
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https://api.github.com/repos/huggingface/datasets/issues/6515 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6515/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6515/comments | https://api.github.com/repos/huggingface/datasets/issues/6515/events | https://github.com/huggingface/datasets/issues/6515 | 2,049,724,251 | I_kwDODunzps56LE9b | 6,515 | Why call http_head() when fsspec_head() succeeds | {
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} | [] | open | false | null | [] | null | 0 | "2023-12-20T02:25:51" | "2023-12-20T02:25:51" | null | CONTRIBUTOR | null | https://github.com/huggingface/datasets/blob/a91582de288d98e94bcb5ab634ca1cfeeff544c5/src/datasets/utils/file_utils.py#L510C1-L523C14 | {
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https://api.github.com/repos/huggingface/datasets/issues/6514 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6514/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6514/comments | https://api.github.com/repos/huggingface/datasets/issues/6514/events | https://github.com/huggingface/datasets/pull/6514 | 2,049,600,663 | PR_kwDODunzps5ia6Os | 6,514 | Cache backward compatibility with 2.15.0 | {
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} | [] | open | false | null | [] | null | 3 | "2023-12-19T23:52:25" | "2023-12-21T15:04:37" | null | MEMBER | null | ...for datasets without scripts
It takes into account the changes in cache from
- https://github.com/huggingface/datasets/pull/6493: switch to `config/version/commit_sha` schema
- https://github.com/huggingface/datasets/pull/6454: fix `DataFilesDict` keys ordering when hashing
requires https://github.com/huggingface/datasets/pull/6493 to be merged | {
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https://api.github.com/repos/huggingface/datasets/issues/6513 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6513/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6513/comments | https://api.github.com/repos/huggingface/datasets/issues/6513/events | https://github.com/huggingface/datasets/issues/6513 | 2,048,869,151 | I_kwDODunzps56H0Mf | 6,513 | Support huggingface-hub 0.20.0 | {
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} | [] | closed | false | null | [] | null | 0 | "2023-12-19T15:15:46" | "2023-12-20T08:44:45" | "2023-12-20T08:44:45" | MEMBER | null | CI to test the support of `huggingface-hub` 0.20.0: https://github.com/huggingface/datasets/compare/main...ci-test-huggingface-hub-v0.20.0.rc1
We need to merge:
- #6510
- #6512
- #6516 | {
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https://api.github.com/repos/huggingface/datasets/issues/6512 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6512/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6512/comments | https://api.github.com/repos/huggingface/datasets/issues/6512/events | https://github.com/huggingface/datasets/pull/6512 | 2,048,795,819 | PR_kwDODunzps5iYI5z | 6,512 | Remove deprecated HfFolder | {
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} | [] | closed | false | null | [] | null | 2 | "2023-12-19T14:40:49" | "2023-12-19T20:21:13" | "2023-12-19T20:14:30" | MEMBER | null | ...and use `huggingface_hub.get_token()` instead | {
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https://api.github.com/repos/huggingface/datasets/issues/6511 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6511/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6511/comments | https://api.github.com/repos/huggingface/datasets/issues/6511/events | https://github.com/huggingface/datasets/pull/6511 | 2,048,465,958 | PR_kwDODunzps5iXAXR | 6,511 | Implement get dataset default config name | {
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} | [] | closed | false | null | [] | null | 3 | "2023-12-19T11:26:19" | "2023-12-21T14:48:57" | "2023-12-21T14:42:41" | MEMBER | null | Implement `get_dataset_default_config_name`.
Now that we support setting a configuration as default in `push_to_hub` (see #6500), we need a programmatically way to know in advance which is the default configuration. This will be used in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet
Follow-up of:
- #6500
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/6510 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6510/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6510/comments | https://api.github.com/repos/huggingface/datasets/issues/6510/events | https://github.com/huggingface/datasets/pull/6510 | 2,046,928,742 | PR_kwDODunzps5iRyiV | 6,510 | Replace `list_files_info` with `list_repo_tree` in `push_to_hub` | {
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} | [] | closed | false | null | [] | null | 3 | "2023-12-18T15:34:19" | "2023-12-19T18:05:47" | "2023-12-19T17:58:34" | CONTRIBUTOR | null | Starting from `huggingface_hub` 0.20.0, `list_files_info` will be deprecated in favor of `list_repo_tree` (see https://github.com/huggingface/huggingface_hub/pull/1910) | {
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https://api.github.com/repos/huggingface/datasets/issues/6509 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6509/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6509/comments | https://api.github.com/repos/huggingface/datasets/issues/6509/events | https://github.com/huggingface/datasets/pull/6509 | 2,046,720,869 | PR_kwDODunzps5iREyE | 6,509 | Better cast error when generating dataset | {
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} | [] | closed | false | null | [] | null | 3 | "2023-12-18T13:57:24" | "2023-12-19T09:37:12" | "2023-12-19T09:31:03" | MEMBER | null | I want to improve the error message for datasets like https://huggingface.co/datasets/m-a-p/COIG-CQIA
Cc @albertvillanova @severo is this new error ok ? Or should I use a dedicated error class ?
New:
```python
Traceback (most recent call last):
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1920, in _prepare_split_single
writer.write_table(table)
File "/Users/quentinlhoest/hf/datasets/src/datasets/arrow_writer.py", line 574, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2322, in table_cast
return cast_table_to_schema(table, schema)
File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2276, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
instruction: string
other: string
index: string
domain: list<item: string>
child 0, item: string
output: string
task_type: struct<major: list<item: string>, minor: list<item: string>>
child 0, major: list<item: string>
child 0, item: string
child 1, minor: list<item: string>
child 0, item: string
task_name_in_eng: string
input: string
to
{'answer_from': Value(dtype='string', id=None), 'instruction': Value(dtype='string', id=None), 'human_verified': Value(dtype='bool', id=None), 'domain': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'output': Value(dtype='string', id=None), 'task_type': {'major': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'minor': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'copyright': Value(dtype='string', id=None), 'input': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/quentinlhoest/hf/datasets/playground/ttest.py", line 74, in <module>
load_dataset("m-a-p/COIG-CQIA")
File "/Users/quentinlhoest/hf/datasets/src/datasets/load.py", line 2529, in load_dataset
builder_instance.download_and_prepare(
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 936, in download_and_prepare
self._download_and_prepare(
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1031, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1791, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1922, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns (other, index, task_name_in_eng) and 3 missing columns (answer_from, copyright, human_verified).
This happened while the json dataset builder was generating data using
hf://datasets/m-a-p/COIG-CQIA/coig_pc/coig_pc_core_sample.json (at revision b7b7ecf290f6515036c7c04bd8537228ac2eb474)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
```
Previously:
```python
Traceback (most recent call last):
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1931, in _prepare_split_single
writer.write_table(table)
File "/Users/quentinlhoest/hf/datasets/src/datasets/arrow_writer.py", line 574, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2295, in table_cast
return cast_table_to_schema(table, schema)
File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2253, in cast_table_to_schema
raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
ValueError: Couldn't cast
task_type: struct<major: list<item: string>, minor: list<item: string>>
child 0, major: list<item: string>
child 0, item: string
child 1, minor: list<item: string>
child 0, item: string
other: string
instruction: string
task_name_in_eng: string
domain: list<item: string>
child 0, item: string
index: string
output: string
input: string
to
{'human_verified': Value(dtype='bool', id=None), 'task_type': {'major': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'minor': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'answer_from': Value(dtype='string', id=None), 'copyright': Value(dtype='string', id=None), 'instruction': Value(dtype='string', id=None), 'domain': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'output': Value(dtype='string', id=None), 'input': Value(dtype='string', id=None)}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/quentinlhoest/hf/datasets/playground/ttest.py", line 74, in <module>
load_dataset("m-a-p/COIG-CQIA")
File "/Users/quentinlhoest/hf/datasets/src/datasets/load.py", line 2529, in load_dataset
builder_instance.download_and_prepare(
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 949, in download_and_prepare
self._download_and_prepare(
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1044, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1804, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1949, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
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https://api.github.com/repos/huggingface/datasets/issues/6508 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6508/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6508/comments | https://api.github.com/repos/huggingface/datasets/issues/6508/events | https://github.com/huggingface/datasets/pull/6508 | 2,045,733,273 | PR_kwDODunzps5iNvAu | 6,508 | Read GeoParquet files using parquet reader | {
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} | [] | open | false | null | [] | null | 1 | "2023-12-18T04:50:37" | "2023-12-18T10:36:34" | null | NONE | null | Let GeoParquet files with the file extension `*.geoparquet` or `*.gpq` be readable by the default parquet reader.
Those two file extensions are the ones most commonly used for GeoParquet files, and is included in the `gpq` validator tool at https://github.com/planetlabs/gpq/blob/e5576b4ee7306b4d2259d56c879465a9364dab90/cmd/gpq/command/convert.go#L73-L75
Addresses https://github.com/huggingface/datasets/issues/6438 | {
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https://api.github.com/repos/huggingface/datasets/issues/6507 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6507/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6507/comments | https://api.github.com/repos/huggingface/datasets/issues/6507/events | https://github.com/huggingface/datasets/issues/6507 | 2,045,152,928 | I_kwDODunzps555o6g | 6,507 | where is glue_metric.py> @Frankie123421 what was the resolution to this? | {
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} | [] | closed | false | null | [] | null | 0 | "2023-12-17T09:58:25" | "2023-12-18T11:42:49" | "2023-12-18T11:42:49" | NONE | null | > @Frankie123421 what was the resolution to this?
use glue_metric.py instead of glue.py in load_metric
_Originally posted by @Frankie123421 in https://github.com/huggingface/datasets/issues/2117#issuecomment-905093763_
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https://api.github.com/repos/huggingface/datasets/issues/6506 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6506/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6506/comments | https://api.github.com/repos/huggingface/datasets/issues/6506/events | https://github.com/huggingface/datasets/issues/6506 | 2,044,975,038 | I_kwDODunzps5549e- | 6,506 | Incorrect test set labels for RTE and CoLA datasets via load_dataset | {
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} | [] | closed | false | null | [] | null | 1 | "2023-12-16T22:06:08" | "2023-12-21T09:57:57" | "2023-12-21T09:57:57" | NONE | null | ### Describe the bug
The test set labels for the RTE and CoLA datasets when loading via datasets load_dataset are all -1.
Edit: It appears this is also the case for every other dataset except for MRPC (stsb, sst2, qqp, mnli (both matched and mismatched), qnli, wnli, ax). Is this intended behavior to safeguard the test set for evaluation purposes?
### Steps to reproduce the bug
!pip install datasets
from datasets import load_dataset
rte_data = load_dataset('glue', 'rte')
cola_data = load_dataset('glue', 'cola')
print(rte_data['test'][0:30]['label'])
print(cola_data['test'][0:30]['label'])
Output:
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
The non-label test data seems to be fine:
e.g. rte_data['test'][1] is:
{'sentence1': "Authorities in Brazil say that more than 200 people are being held hostage in a prison in the country's remote, Amazonian-jungle state of Rondonia.",
'sentence2': 'Authorities in Brazil hold 200 people as hostage.',
'label': -1,
'idx': 1}
Training and validation data are also fine:
e.g. rte_data['train][0] is:
{'sentence1': 'No Weapons of Mass Destruction Found in Iraq Yet.',
'sentence2': 'Weapons of Mass Destruction Found in Iraq.',
'label': 1,
'idx': 0}
### Expected behavior
Expected the labels to be binary 0/1 values; Got all -1s instead
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0 | {
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} | [] | open | false | null | [] | null | 0 | "2023-12-16T11:51:07" | "2023-12-16T11:51:07" | null | NONE | null | ### Describe the bug
Hello, everyone. I met a problem when I am trying to load a data file using load_dataset method on a Debian 10 system. The data file is not very large, only 1.63MB with 600 records.
Here is my code:
from datasets import load_dataset
dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json')
I waited it for 20 minutes. It still no response. I cannot using Ctrl+C to cancel the command. I have to use Ctrl+Z to kill it. I also try it with a txt file, it still no response in a long time.
I can load the same file successfully using my laptop (windows 10, python 3.8.5, datasets==2.14.5). I can also make it on another computer (Ubuntu 20.04.5 LTS, python 3.10.13, datasets 2.14.7). It only takes me 1-2 miniutes.
Could you give me some suggestions? Thank you.
### Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json')
### Expected behavior
I hope it can load the file successfully.
### Environment info
OS: Debian GNU/Linux 10
Python: Python 3.10.13
Pip list:
Package Version
------------------------- ------------
accelerate 0.25.0
addict 2.4.0
aiofiles 23.2.1
aiohttp 3.9.1
aiosignal 1.3.1
aliyun-python-sdk-core 2.14.0
aliyun-python-sdk-kms 2.16.2
altair 5.2.0
annotated-types 0.6.0
anyio 3.7.1
async-timeout 4.0.3
attrs 23.1.0
certifi 2023.11.17
cffi 1.16.0
charset-normalizer 3.3.2
click 8.1.7
contourpy 1.2.0
crcmod 1.7
cryptography 41.0.7
cycler 0.12.1
datasets 2.14.7
dill 0.3.7
docstring-parser 0.15
einops 0.7.0
exceptiongroup 1.2.0
fastapi 0.105.0
ffmpy 0.3.1
filelock 3.13.1
fonttools 4.46.0
frozenlist 1.4.1
fsspec 2023.10.0
gast 0.5.4
gradio 3.50.2
gradio_client 0.6.1
h11 0.14.0
httpcore 1.0.2
httpx 0.25.2
huggingface-hub 0.19.4
idna 3.6
importlib-metadata 7.0.0
importlib-resources 6.1.1
jieba 0.42.1
Jinja2 3.1.2
jmespath 0.10.0
joblib 1.3.2
jsonschema 4.20.0
jsonschema-specifications 2023.11.2
kiwisolver 1.4.5
markdown-it-py 3.0.0
MarkupSafe 2.1.3
matplotlib 3.8.2
mdurl 0.1.2
modelscope 1.10.0
mpmath 1.3.0
multidict 6.0.4
multiprocess 0.70.15
networkx 3.2.1
nltk 3.8.1
numpy 1.26.2
nvidia-cublas-cu12 12.1.3.1
nvidia-cuda-cupti-cu12 12.1.105
nvidia-cuda-nvrtc-cu12 12.1.105
nvidia-cuda-runtime-cu12 12.1.105
nvidia-cudnn-cu12 8.9.2.26
nvidia-cufft-cu12 11.0.2.54
nvidia-curand-cu12 10.3.2.106
nvidia-cusolver-cu12 11.4.5.107
nvidia-cusparse-cu12 12.1.0.106
nvidia-nccl-cu12 2.18.1
nvidia-nvjitlink-cu12 12.3.101
nvidia-nvtx-cu12 12.1.105
orjson 3.9.10
oss2 2.18.3
packaging 23.2
pandas 2.1.4
peft 0.7.1
Pillow 10.1.0
pip 23.3.1
platformdirs 4.1.0
protobuf 4.25.1
psutil 5.9.6
pyarrow 14.0.1
pyarrow-hotfix 0.6
pycparser 2.21
pycryptodome 3.19.0
pydantic 2.5.2
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python-dateutil 2.8.2
python-multipart 0.0.6
pytz 2023.3.post1
PyYAML 6.0.1
referencing 0.32.0
regex 2023.10.3
requests 2.31.0
rich 13.7.0
rouge-chinese 1.0.3
rpds-py 0.13.2
safetensors 0.4.1
scipy 1.11.4
semantic-version 2.10.0
sentencepiece 0.1.99
setuptools 68.2.2
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six 1.16.0
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sse-starlette 1.8.2
starlette 0.27.0
sympy 1.12
tiktoken 0.5.2
tokenizers 0.15.0
tomli 2.0.1
toolz 0.12.0
torch 2.1.2
tqdm 4.66.1
transformers 4.36.1
triton 2.1.0
trl 0.7.4
typing_extensions 4.9.0
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zipp 3.17.0
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https://api.github.com/repos/huggingface/datasets/issues/6504 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6504/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6504/comments | https://api.github.com/repos/huggingface/datasets/issues/6504/events | https://github.com/huggingface/datasets/issues/6504 | 2,044,541,154 | I_kwDODunzps553Tji | 6,504 | Error Pushing to Hub | {
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} | [] | closed | false | null | [] | null | 0 | "2023-12-16T01:05:22" | "2023-12-16T06:20:53" | "2023-12-16T06:20:53" | NONE | null | ### Describe the bug
Error when trying to push a dataset in a special format to hub
### Steps to reproduce the bug
```
import datasets
from datasets import Dataset
dataset_dict = {
"filename": ["apple", "banana"],
"token": [[[1,2],[3,4]],[[1,2],[3,4]]],
"label": [0, 1],
}
dataset = Dataset.from_dict(dataset_dict)
dataset = dataset.cast_column("token", datasets.features.features.Array2D(shape=(2, 2),dtype="int16"))
dataset.push_to_hub("SequenceModel/imagenet_val_256")
```
Error:
```
...
ConstructorError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/tuple'
in "<unicode string>", line 8, column 16:
shape: !!python/tuple
^
```
### Expected behavior
Dataset being pushed to hub
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-5.19.0-1022-gcp-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0 | {
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} | [] | closed | false | null | [] | null | 2 | "2023-12-15T14:40:57" | "2023-12-15T14:51:06" | "2023-12-15T14:44:47" | MEMBER | null | This code was failing
```python
In [1]: from datasets import load_dataset
In [2]:
...: ds = load_dataset("xnli", "all_languages", split="test", streaming=True)
...:
...: sample_data = next(iter(ds))["premise"] # pick up one data
...: input_text = list(sample_data.values())
```
```
File ~/hf/datasets/src/datasets/features/translation.py:104, in TranslationVariableLanguages.encode_example(self, translation_dict)
102 return translation_dict
103 elif self.languages and set(translation_dict) - lang_set:
--> 104 raise ValueError(
105 f'Some languages in example ({", ".join(sorted(set(translation_dict) - lang_set))}) are not in valid set ({", ".join(lang_set)}).'
106 )
108 # Convert dictionary into tuples, splitting out cases where there are
109 # multiple translations for a single language.
110 translation_tuples = []
ValueError: Some languages in example (language, translation) are not in valid set (ur, fr, hi, sw, vi, el, de, th, en, tr, zh, ar, bg, ru, es).
```
because in streaming mode we expect features encode methods to be no-ops if the example is already encoded.
I fixed `TranslationVariableLanguages` to account for that | {
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https://api.github.com/repos/huggingface/datasets/issues/6501 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6501/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6501/comments | https://api.github.com/repos/huggingface/datasets/issues/6501/events | https://github.com/huggingface/datasets/issues/6501 | 2,043,377,240 | I_kwDODunzps55y3ZY | 6,501 | OverflowError: value too large to convert to int32_t | {
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} | [] | open | false | null | [] | null | 0 | "2023-12-15T10:10:21" | "2023-12-15T10:10:21" | null | NONE | null | ### Describe the bug
![image](https://github.com/huggingface/datasets/assets/47747764/f58044fb-ddda-48b6-ba68-7bbfef781630)
### Steps to reproduce the bug
just loading datasets
### Expected behavior
how can I fix it
### Environment info
pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl
pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl
done | {
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} | [] | closed | false | null | [] | null | 8 | "2023-12-14T16:46:01" | "2023-12-15T13:16:56" | "2023-12-15T13:10:48" | MEMBER | null | Right now this code is failing on `main`:
```python
load_dataset("openbookqa")
```
This is because it tries to load the dataset from the Parquet export but the dataset has multiple configurations and the Parquet export doesn't know which one is the default one.
I fixed this by simply falling back on using the dataset script (which tells the user to pass `trust_remote_code=True`):
```python
load_dataset("openbookqa", trust_remote_code=True)
```
Note that if the user happened to specify a config name I don't fall back on the script since we can use the Parquet export in this case (no need to know which config is the default)
```python
load_dataset("openbookqa", "main")
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6496 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6496/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6496/comments | https://api.github.com/repos/huggingface/datasets/issues/6496/events | https://github.com/huggingface/datasets/issues/6496 | 2,041,589,386 | I_kwDODunzps55sC6K | 6,496 | Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again. | {
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} | [] | open | false | null | [] | null | 1 | "2023-12-14T11:24:54" | "2023-12-14T12:22:21" | null | NONE | null | **Describe the bug**
Getting a `412 Client Error: Precondition Failed` when trying to write a dataset to the HF hub.
```
huggingface_hub.utils._errors.HfHubHTTPError: 412 Client Error: Precondition Failed for url: https://huggingface.co/api/datasets/GLorr/test-dask/commit/main (Request ID: Root=1-657ae26f-3bd92bf861bb254b2cc0826c;50a09ab7-9347-406a-ba49-69f98abee9cc)
A commit has happened since. Please refresh and try again.
```
**Steps to reproduce the bug**
This is a minimal reproducer:
```
import dask.dataframe as dd
import pandas as pd
import random
import os
import huggingface_hub
import datasets
huggingface_hub.login(token=os.getenv("HF_TOKEN"))
data = {"number": [random.randint(0,10) for _ in range(1000)]}
df = pd.DataFrame.from_dict(data)
dataframe = dd.from_pandas(df, npartitions=1)
dataframe = dataframe.repartition(npartitions=3)
schema = datasets.Features({"number": datasets.Value("int64")}).arrow_schema
repo_id = "GLorr/test-dask"
repo_path = f"hf://datasets/{repo_id}"
huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True)
dd.to_parquet(dataframe, path=f"{repo_path}/data", schema=schema)
```
**Expected behavior**
Would expect to write to the hub without any problem.
**Environment info**
```
datasets==2.15.0
huggingface-hub==0.19.4
```
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} | [] | open | false | null | [] | null | 0 | "2023-12-13T13:11:42" | "2023-12-13T13:11:42" | null | NONE | null | ### Describe the bug
![1702472610561](https://github.com/huggingface/datasets/assets/28867010/4b7ef5e7-32c3-4b73-84cb-5de059caa0b6)
When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images
### Steps to reproduce the bug
from datasets import Dataset, load_dataset
dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False)
# print(dataset["train"][0]["image"] == dataset["train"][1]["image"])
print(dataset)
print(dataset["train"]["image"])
print(len(dataset["train"]["image"]))
### Expected behavior
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 8
})
})
[<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>]
8
### Environment info
- `datasets` version: 2.14.5
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.9.17
- Huggingface_hub version: 0.19.4
- PyArrow version: 13.0.0
- Pandas version: 2.0.3 | {
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} | [] | open | false | null | [] | null | 0 | "2023-12-12T23:07:51" | "2023-12-13T13:24:22" | null | NONE | null | ### System Info
- `transformers` version: 4.32.1
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.11.5
- Huggingface_hub version: 0.15.1
- Safetensors version: 0.3.2
- Accelerate version: not installed
- Accelerate config: not found
- PyTorch version (GPU?): 2.1.1+cpu (False)
- Tensorflow version (GPU?): 2.15.0 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no
### Who can help?
@marios
### Information
- [X] The official example scripts
- [ ] My own modified scripts
### Tasks
- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
[Source](https://huggingface.co/docs/datasets/v2.2.1/en/access)
```
from datasets import load_dataset
dataset = load_dataset('glue', 'mrpc', split='train')
dataset.info
```
DatasetInfo(description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='glue', dataset_name=None, config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943843, num_examples=3668, shard_lengths=None, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105879, num_examples=408, shard_lengths=None, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442410, num_examples=1725, shard_lengths=None, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': None}}, download_size=1494541, post_processing_size=None, dataset_size=1492132, size_in_bytes=2986673)
### Expected behavior
```
from datasets import load_dataset
dataset = load_dataset('glue', 'mrpc', split='train')
dataset.info
```
DatasetInfo(
description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n',
citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398',
license='',
features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, builder_name='glue', config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943851, num_examples=3668, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105887, num_examples=408, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442418, num_examples=1725, dataset_name='glue')},
download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': '971d7767d81b997fd9060ade0ec23c4fc31cbb226a55d1bd4a1bac474eb81dc7'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': '60a9b09084528f0673eedee2b69cb941920f0b8cd0eeccefc464a98768457f89'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': 'a04e271090879aaba6423d65b94950c089298587d9c084bf9cd7439bd785f784'}},
download_size=1494541,
post_processing_size=None,
dataset_size=1492156,
size_in_bytes=2986697
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https://api.github.com/repos/huggingface/datasets/issues/6493 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6493/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6493/comments | https://api.github.com/repos/huggingface/datasets/issues/6493/events | https://github.com/huggingface/datasets/pull/6493 | 2,038,221,490 | PR_kwDODunzps5h0XJK | 6,493 | Lazy data files resolution and offline cache reload | {
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} | [] | open | false | null | [] | null | 6 | "2023-12-12T17:15:17" | "2023-12-19T22:46:28" | null | MEMBER | null | Includes both https://github.com/huggingface/datasets/pull/6458 and https://github.com/huggingface/datasets/pull/6459
This PR should be merged instead of the two individually, since they are conflicting
## Offline cache reload
it can reload datasets that were pushed to hub if they exist in the cache.
example:
```python
>>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp")
>>> load_dataset("lhoestq/tmp")
DatasetDict({
train: Dataset({
features: ['a'],
num_rows: 2
})
})
```
and later, without connection:
```python
>>> load_dataset("lhoestq/tmp")
Using the latest cached version of the dataset since lhoestq/tmp couldn't be found on the Hugging Face Hub
Found the latest cached dataset configuration 'default' at /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/default/0.0.0/da0e902a945afeb9 (last modified on Wed Dec 13 14:55:52 2023).
DatasetDict({
train: Dataset({
features: ['a'],
num_rows: 2
})
})
```
- Updated `CachedDatasetModuleFactory` to look for datasets in the cache at `<namespace>___<dataset_name>/<config_id>`
- Since the metadata configs parameters are not available in offline mode, we don't know which folder to load (config_id and hash change), so I simply load the latest one
- I instantiate a BuilderConfig even if there is no metadata config with the right config_name
- Its config_id is equal to the config_name to be able to retrieve it in the cache (no more suffix for configs from metadata configs)
- We can reload this config if offline mode by specifying the right config_name (same as online !)
- Consequences of this change:
- Only when there are user's parameters it creates a custom builder config with config_id = config_name + user parameters hash
- the hash used to name the cache folder takes into account the metadata config and the dataset info, so that the right cache can be reloaded when there is internet connection without redownloading the data or resolving the data files. For local directories I hash the builder configs and dataset info, and for datasets on the hub I use the commit sha as hash.
- cache directories now look like `config/version/commit_sha` for hub datasets which is clean :)
Fix https://github.com/huggingface/datasets/issues/3547
## Lazy data files resolution
this makes this code run in 2sec instead of >10sec
```python
from datasets import load_dataset
ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False)
```
For some datasets with many configs and files it can be up to 100x faster.
This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts.
The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict
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} | [] | closed | false | null | [] | null | 3 | "2023-12-12T15:18:16" | "2023-12-13T14:29:01" | "2023-12-13T14:22:41" | MEMBER | null | Make `push_to_hub` return `CommitInfo`.
This is useful, for example, if we pass `create_pr=True` and we want to know the created PR ID.
CC: @severo for the use case in https://huggingface.co/datasets/jmhessel/newyorker_caption_contest/discussions/4 | {
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https://api.github.com/repos/huggingface/datasets/issues/6491 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6491/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6491/comments | https://api.github.com/repos/huggingface/datasets/issues/6491/events | https://github.com/huggingface/datasets/pull/6491 | 2,037,690,643 | PR_kwDODunzps5hyiTY | 6,491 | Fix metrics dead link | {
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https://api.github.com/repos/huggingface/datasets/issues/6490 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6490/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6490/comments | https://api.github.com/repos/huggingface/datasets/issues/6490/events | https://github.com/huggingface/datasets/issues/6490 | 2,037,204,892 | I_kwDODunzps55bUec | 6,490 | `load_dataset(...,save_infos=True)` not working without loading script | {
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} | [] | open | false | null | [] | null | 1 | "2023-12-12T08:09:18" | "2023-12-12T08:36:22" | null | NONE | null | ### Describe the bug
It seems that saving a dataset infos back into the card file is not working for datasets without a loading script.
After tracking the problem a bit it looks like saving the infos uses `Builder.get_imported_module_dir()` as its destination directory.
Internally this is a call to `inspect.getfile()` but since the actual builder class used is dynamically created (cf. `datasets.load.configure_builder_class`) this method actually return te path to the parent builder class (e.g. `datasets.packaged_modules.json.JSON`).
### Steps to reproduce the bug
1. Have a local dataset without any loading script
2. Make sure there are no dataset infos in the README.md
3. Load with `save_infos=True`
4. No change in the dataset README.md
5. A new README.md file is created in the directory of the parent builder class (e.g. for json in `.../site-packages/datasets/packaged_modules/json/README.md`)
### Expected behavior
The dataset README.md should be updated and no file should be created in the python environment.
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.6.0
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] | open | false | null | [] | null | 0 | "2023-12-12T00:08:43" | "2023-12-12T00:09:27" | null | NONE | null | ### Feature request
I would like to load a dataset from S3 using the imagefolder option
something like
`dataset = datasets.load_dataset('imagefolder', data_dir='s3://.../lsun/train/bedroom', fs=S3FileSystem(), streaming=True) `
### Motivation
no need of data_files
### Your contribution
no experience with this | {
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} | [] | open | false | null | [] | null | 1 | "2023-12-11T15:06:01" | "2023-12-11T15:34:23" | null | NONE | null | Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it?
Thanks
Dataset:
https://huggingface.co/datasets/cerebras/SlimPajama-627B
Error:
`requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`
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} | [] | closed | false | null | [] | null | 2 | "2023-12-11T11:09:16" | "2023-12-11T11:41:34" | "2023-12-11T11:41:34" | MEMBER | null | Currently if you change the `dataset_info` of a dataset (e.g. in the YAML part of the README.md), the cache ignores this change.
This is problematic because you want to regenerate a dataset if you change the features or the split sizes for example (e.g. after push_to_hub)
Ideally we should take the resolved files into account as well but this will be for another PR | {
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https://api.github.com/repos/huggingface/datasets/issues/6486 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6486/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6486/comments | https://api.github.com/repos/huggingface/datasets/issues/6486/events | https://github.com/huggingface/datasets/pull/6486 | 2,035,206,206 | PR_kwDODunzps5hqCSc | 6,486 | Fix docs phrasing about supported formats when sharing a dataset | {
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it seems that sth wrong with my terrible "bug body" life, When i run this code, "import datasets"
i meet this error FileNotFoundError: [Errno 2] No such file or directory: 'nul'
![image](https://github.com/huggingface/datasets/assets/73683903/3973c120-ebb1-42b7-bede-b9de053e861d)
![image](https://github.com/huggingface/datasets/assets/73683903/0496adff-a7a7-4dcb-929e-ec11ede71f04)
### Steps to reproduce the bug
1.import datasets
### Expected behavior
i just run a single line code and stuct in this bug
### Environment info
OS: Windows10
Datasets==2.15.0
python=3.10 | {
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] | closed | false | null | [] | null | 4 | "2023-12-08T16:11:30" | "2023-12-08T16:27:16" | "2023-12-08T16:27:04" | CONTRIBUTOR | null | ### Describe the bug
Suppose I have a two iterable datasets, one with the features:
* `{"audio", "text", "column_a"}`
And the other with the features:
* `{"audio", "sentence", "column_b"}`
I want to combine both datasets using `interleave_datasets`, which requires me to unify the column names. I would typically do this by:
1. Renaming the common columns to the same name (e.g. `"text"` -> `"sentence"`)
2. Removing the unwanted columns (e.g. `"column_a"`, `"column_b"`)
However, the process of renaming and removing columns in an iterable dataset doesn't work, since we need to preserve the original text column, meaning we can't combine the datasets.
### Steps to reproduce the bug
```python
from datasets import load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# check original features
dataset_features = dataset.features.keys()
print("Original features: ", dataset_features)
#Β rename "text" -> "sentence"
dataset = dataset.rename_column("text", "sentence")
# remove unwanted columns
COLUMNS_TO_KEEP = {"audio", "sentence"}
dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP))
# stream first sample, should return "audio" and "sentence" columns
print(next(iter(dataset)))
```
Traceback:
```python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[5], line 17
14 COLUMNS_TO_KEEP = {"audio", "sentence"}
15 dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP))
---> 17 print(next(iter(dataset)))
File ~/datasets/src/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self)
1350 yield formatter.format_row(pa_table)
1351 return
-> 1353 for key, example in ex_iterable:
1354 if self.features:
1355 # `IterableDataset` automatically fills missing columns with None.
1356 # This is done with `_apply_feature_types_on_example`.
1357 example = _apply_feature_types_on_example(
1358 example, self.features, token_per_repo_id=self._token_per_repo_id
1359 )
File ~/datasets/src/datasets/iterable_dataset.py:652, in MappedExamplesIterable.__iter__(self)
650 yield from ArrowExamplesIterable(self._iter_arrow, {})
651 else:
--> 652 yield from self._iter()
File ~/datasets/src/datasets/iterable_dataset.py:729, in MappedExamplesIterable._iter(self)
727 if self.remove_columns:
728 for c in self.remove_columns:
--> 729 del transformed_example[c]
730 yield key, transformed_example
731 current_idx += 1
KeyError: 'text'
```
=> we see that `datasets` is looking for the column "text", even though we've renamed this to "sentence" and then removed the un-wanted "text" column from our dataset.
### Expected behavior
Should be able to rename and remove columns from iterable dataset.
### Environment info
- `datasets` version: 2.15.1.dev0
- Platform: macOS-13.5.1-arm64-arm-64bit
- Python version: 3.11.6
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.2
- `fsspec` version: 2023.9.2 | {
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} | [] | open | false | null | [] | null | 2 | "2023-12-08T16:01:35" | "2023-12-11T19:13:46" | null | NONE | null | **Is your feature request related to a problem? Please describe.**
I am working on a project, where I would like to test different preprocessing methods for my ML-data. Thus, I would like to work a lot with revisions and compare them. Currently, I was not able to make it work with the revision keyword because it was not redownloading the data, it was reading in some cached data, until I put `download_mode="force_redownload"`, even though the reversion was different.
Of course, I may have done something wrong or missed a setting somewhere!
**Describe the solution you'd like**
The solution would allow me to easily work with revisions:
- create a new dataset (by combining things, different preprocessing, ..) and give it a new revision (v.1.2.3), maybe like this:
`dataset_audio.push_to_hub('kenfus/xy', revision='v1.0.2')`
- then, get the current revision as follows:
```
dataset = load_dataset(
'kenfus/xy', revision='v1.0.2',
)
```
this downloads the new version and does not load in a different revision and all future map, filter, .. operations are done on this dataset and not loaded from cache produced from a different revision.
- if I rerun the run, the caching should be smart enough in every step to not reuse a mapping operation on a different revision.
**Describe alternatives you've considered**
I created my own caching, putting `download_mode="force_redownload"` and `load_from_cache_file=False,` everywhere.
**Additional context**
Thanks a lot for your great work! Creating NLP datasets and training a model with them is really easy and straightforward with huggingface.
This is the data loading in my script:
```
## CREATE PATHS
prepared_dataset_path = os.path.join(
DATA_FOLDER, str(DATA_VERSION), "prepared_dataset"
)
os.makedirs(os.path.join(DATA_FOLDER, str(DATA_VERSION)), exist_ok=True)
## LOAD DATASET
if os.path.exists(prepared_dataset_path):
print("Loading prepared dataset from disk...")
dataset_prepared = load_from_disk(prepared_dataset_path)
else:
print("Loading dataset from HuggingFace Datasets...")
dataset = load_dataset(
PATH_TO_DATASET, revision=DATA_VERSION, download_mode="force_redownload"
)
print("Preparing dataset...")
dataset_prepared = dataset.map(
prepare_dataset,
remove_columns=["audio", "transcription"],
num_proc=os.cpu_count(),
load_from_cache_file=False,
)
dataset_prepared.save_to_disk(prepared_dataset_path)
del dataset
if CHECK_DATASET:
## CHECK DATASET
dataset_prepared = dataset_prepared.map(
check_dimensions, num_proc=os.cpu_count(), load_from_cache_file=False
)
dataset_filtered = dataset_prepared.filter(
lambda example: not example["incorrect_dimension"],
load_from_cache_file=False,
)
for example in dataset_prepared.filter(
lambda example: example["incorrect_dimension"], load_from_cache_file=False
):
print(example["path"])
print(
f"Number of examples with incorrect dimension: {len(dataset_prepared) - len(dataset_filtered)}"
)
print("Number of examples train: ", len(dataset_filtered["train"]))
print("Number of examples test: ", len(dataset_filtered["test"]))
```
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https://api.github.com/repos/huggingface/datasets/issues/6482 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6482/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6482/comments | https://api.github.com/repos/huggingface/datasets/issues/6482/events | https://github.com/huggingface/datasets/pull/6482 | 2,032,675,918 | PR_kwDODunzps5hhl23 | 6,482 | Fix max lock length on unix | {
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} | [] | closed | false | null | [] | null | 3 | "2023-12-08T13:39:30" | "2023-12-12T11:53:32" | "2023-12-12T11:47:27" | MEMBER | null | reported in https://github.com/huggingface/datasets/pull/6482 | {
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} | [] | open | false | null | [] | null | 0 | "2023-12-08T13:22:03" | "2023-12-08T13:22:03" | null | NONE | null | ### Describe the bug
When I run my code using the "torchrun" command, when the code reaches the "save_to_disk" part, suddenly I get the following warning and error messages:
Because the dataset is too large, the "save_to_disk" function splits it into 70 parts for saving. However, an error occurs suddenly when it reaches the 14th shard.
WARNING: torch.distributed.elastic.multiprocessing.api: Sending process 2224968 closing signal SIGTERM
ERROR: torch.distributed.elastic.multiprocessing.api: failed (exitcode: -7). traceback: Signal 7 (SIGBUS) received by PID 2224967.
### Steps to reproduce the bug
ds_shard = ds_shard.map(map_fn, *args, **kwargs)
ds_shard.save_to_disk(ds_shard_filepaths[rank])
Saving the dataset (14/70 shards): 20%|ββ | 875350/4376702 [00:19<01:53, 30863.15 examples/s]
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 2224968 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -7) local_rank: 0 (pid: 2224967) of binary: /home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/python
Traceback (most recent call last):
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/torchrun", line 8, in <module>
sys.exit(main())
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
return f(*args, **kwargs)
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 794, in main
run(args)
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 785, in run
elastic_launch(
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
==========================================================
run.py FAILED
----------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
----------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-12-08_20:09:04
rank : 0 (local_rank: 0)
exitcode : -7 (pid: 2224967)
error_file: <N/A>
traceback : Signal 7 (SIGBUS) received by PID 2224967
### Expected behavior
I hope it can save successfully without any issues, but it seems there is a problem.
### Environment info
`datasets` version: 2.14.6
- Platform: Linux-4.19.90-24.4.v2101.ky10.aarch64-aarch64-with-glibc2.28
- Python version: 3.10.11
- Huggingface_hub version: 0.17.3
- PyArrow version: 14.0.0
- Pandas version: 2.1.2 | {
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} | [] | closed | false | null | [] | null | 2 | "2023-12-07T16:31:50" | "2023-12-08T13:33:06" | "2023-12-08T13:26:54" | MEMBER | null | Example for glue sst2:
Dataset
```
DatasetDict({
test: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 1821
})
train: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 67349
})
validation: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 872
})
})
```
IterableDataset (new)
```
IterableDatasetDict({
test: IterableDataset({
features: ['sentence', 'label', 'idx'],
n_shards: 1
})
train: IterableDataset({
features: ['sentence', 'label', 'idx'],
n_shards: 1
})
validation: IterableDataset({
features: ['sentence', 'label', 'idx'],
n_shards: 1
})
})
```
IterableDataset (before)
```
{'test': <datasets.iterable_dataset.IterableDataset object at 0x130d421f0>, 'train': <datasets.iterable_dataset.IterableDataset object at 0x136f3aaf0>, 'validation': <datasets.iterable_dataset.IterableDataset object at 0x136f4b100>}
{'sentence': 'hide new secretions from the parental units ', 'label': 0, 'idx': 0}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6479 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6479/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6479/comments | https://api.github.com/repos/huggingface/datasets/issues/6479/events | https://github.com/huggingface/datasets/pull/6479 | 2,029,040,121 | PR_kwDODunzps5hVLom | 6,479 | More robust preupload retry mechanism | {
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} | [] | closed | false | null | [] | null | 2 | "2023-12-06T17:19:38" | "2023-12-06T19:47:29" | "2023-12-06T19:41:06" | CONTRIBUTOR | null | null | {
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} | [] | open | false | null | [] | null | 2 | "2023-12-06T09:04:11" | "2023-12-07T02:19:44" | null | CONTRIBUTOR | null | My dataset is stored on the company's lakefs server. How can I write code to load the dataset? It would be great if I could provide code examples or provide some references
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```
FAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\RUNNER~1\\AppData\\Local\\Temp\\tmpfcnps56i\\hf-internal-testing___dataset_with_script\\default\\0.0.0\\c240e2be3370bdbd\\dataset_with_script-train.arrow'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6475/comments | https://api.github.com/repos/huggingface/datasets/issues/6475/events | https://github.com/huggingface/datasets/issues/6475 | 2,027,373,734 | I_kwDODunzps5410Sm | 6,475 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed | {
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} | [] | open | false | null | [] | null | 6 | "2023-12-06T00:07:34" | "2023-12-06T23:26:23" | null | NONE | null | ### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|ββββββββββ| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <module>
count = compute_frequencies()
^^^^^^^^^^^^^^^^^^^^^
File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 17, in compute_frequencies
laion2b_dataset = load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\load.py", line 2165, in load_dataset
ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1187, in as_dataset
datasets = map_nested(
^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\utils\py_utils.py", line 456, in map_nested
return function(data_struct)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1217, in _build_single_dataset
ds = self._as_dataset(
^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1291, in _as_dataset
dataset_kwargs = ArrowReader(cache_dir, self.info).read(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 244, in read
return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 265, in read_files
pa_table = self._read_files(files, in_memory=in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 200, in _read_files
pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 336, in _get_table_from_filename
table = ArrowReader.read_table(filename, in_memory=in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 357, in read_table
return table_cls.from_file(filename)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 1059, in from_file
table = _memory_mapped_arrow_table_from_file(filename)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 66, in _memory_mapped_arrow_table_from_file
pa_table = opened_stream.read_all()
^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow\ipc.pxi", line 757, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status
OSError: [WinError 8] PrefetchVirtualMemory failed. Detail: [Windows error 8] Not enough memory resources are available to process this command.
```
This error is probably a red herring: https://stackoverflow.com/questions/50263929/numpy-memmap-returns-not-enough-memory-while-there-are-plenty-available In other words, the issue is related to asking for a memory mapping of length N > M the length of the file on Windows. This gracefully succeeds on Linux.
I have 1024 arrow files in my cache instead of 128 like in the repository for it. Probably related. I don't know why `datasets` reorganized/rewrote the dataset in my cache to be 1024 slices instead of the original 128.
### Steps to reproduce the bug
```
# as a huggingface developer, you may already have laion2B-en somewhere
_CACHE_DIR = "."
from datasets import load_dataset
load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False)
```
### Expected behavior
This should correctly load as a memory mapped Arrow dataset.
### Environment info
- `datasets` version: 2.15.0
- Platform: Windows-10-10.0.20348-SP0 (this is windows 2022)
- Python version: 3.11.4
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.2
- `fsspec` version: 2023.10.0
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https://api.github.com/repos/huggingface/datasets/issues/6474 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6474/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6474/comments | https://api.github.com/repos/huggingface/datasets/issues/6474/events | https://github.com/huggingface/datasets/pull/6474 | 2,027,006,715 | PR_kwDODunzps5hONZc | 6,474 | Deprecate Beam API and download from HF GCS bucket | {
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} | [] | open | false | null | [] | null | 1 | "2023-12-05T19:51:33" | "2023-12-10T17:55:50" | null | CONTRIBUTOR | null | Deprecate the Beam API and download from the HF GCS bucked.
TODO:
- [ ] Deprecate the Beam-based [`wikipedia`](https://huggingface.co/datasets/wikipedia) in favor of [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia) ([Hub PR](https://huggingface.co/datasets/wikipedia/discussions/19))
- [ ] Make [`natural_questions`](https://huggingface.co/datasets/natural_questions) a no-code dataset
- [ ] Make [`wiki40b`](https://huggingface.co/datasets/wiki40b) a no-code dataset
- [ ] Make [`wiki_dpr`](https://huggingface.co/datasets/wiki_dpr) an Arrow-based dataset | {
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] | null | 0 | "2023-12-05T15:35:34" | "2023-12-06T08:17:34" | "2023-12-05T18:08:43" | MEMBER | null | See: https://github.com/huggingface/datasets/actions/runs/7100835633/job/19327734359
```
Would reformat: src/datasets/features/image.py
1 file would be reformatted, 253 files left unchanged
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