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Streaming the Pile: Missing Files
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NONE
|
### Describe the bug
The pile does not stream, a "File not Found error" is returned. It looks like the Pile's files have been moved.
### Steps to reproduce the bug
To reproduce run the following code:
```
from datasets import load_dataset
dataset = load_dataset('EleutherAI/pile', 'en', split='train', streaming=True)
next(iter(dataset))
```
I get the following error:
`FileNotFoundError: https://the-eye.eu/public/AI/pile/train/00.jsonl.zst`
### Expected behavior
Return the data in a stream.
### Environment info
- `datasets` version: 2.12.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.11.5
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.3
|
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fix tests
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Loading HF Hub Dataset (private org repo) fails to load all features
|
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NONE
|
### 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
|
2023-12-21T13:24:31Z
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The order of the splits is not preserved
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MEMBER
|
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 issues:
- https://huggingface.co/datasets/adversarial_qa/discussions/3
- https://huggingface.co/datasets/beans/discussions/4
This is a regression because it was previously fixed. See:
- #6196
- #5728
|
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Support commit_description parameter in push_to_hub
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Support `commit_description` parameter in `push_to_hub`.
CC: @Wauplin
|
2023-12-21T14:49:47Z
|
https://github.com/huggingface/datasets/pull/6520
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Support push_to_hub canonical datasets
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MEMBER
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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
|
2023-12-21T14:48:20Z
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https://github.com/huggingface/datasets/pull/6519
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fix get_metadata_patterns function args error
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Bug get_metadata_patterns arg error https://github.com/huggingface/datasets/issues/6517
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2023-12-21T15:14:17Z
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https://github.com/huggingface/datasets/pull/6518
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Bug get_metadata_patterns arg error
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https://github.com/huggingface/datasets/blob/3f149204a2a5948287adcade5e90707aa5207a92/src/datasets/load.py#L1240C1-L1240C69
metadata_patterns = get_metadata_patterns(base_path, download_config=self.download_config)
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2023-12-22T00:24:23Z
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https://github.com/huggingface/datasets/issues/6517
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Support huggingface-hub pre-releases
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MEMBER
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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.
|
2023-12-20T08:51:34Z
|
https://github.com/huggingface/datasets/pull/6516
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Why call http_head() when fsspec_head() succeeds
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https://github.com/huggingface/datasets/blob/a91582de288d98e94bcb5ab634ca1cfeeff544c5/src/datasets/utils/file_utils.py#L510C1-L523C14
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2023-12-26T05:35:46Z
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Cache backward compatibility with 2.15.0
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...for datasets without scripts
It takes into account the changes in cache from
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- 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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2023-12-21T21:14:11Z
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https://github.com/huggingface/datasets/pull/6514
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Support huggingface-hub 0.20.0
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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
|
2023-12-20T08:44:45Z
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https://github.com/huggingface/datasets/issues/6513
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2023-12-19T20:14:30Z
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PR_kwDODunzps5iYI5z
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closed
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Remove deprecated HfFolder
|
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MEMBER
|
...and use `huggingface_hub.get_token()` instead
|
2023-12-19T20:21:13Z
|
https://github.com/huggingface/datasets/pull/6512
|
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2023-12-21T14:42:41Z
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Implement get dataset default config name
|
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2023-12-19T11:26:19Z
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MEMBER
|
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
|
2023-12-21T14:48:57Z
|
https://github.com/huggingface/datasets/pull/6511
|
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2023-12-19T17:58:34Z
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Replace `list_files_info` with `list_repo_tree` in `push_to_hub`
|
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2023-12-18T15:34:19Z
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CONTRIBUTOR
|
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)
|
2023-12-19T18:05:47Z
|
https://github.com/huggingface/datasets/pull/6510
|
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2023-12-19T09:31:03Z
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Better cast error when generating dataset
|
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MEMBER
|
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
```
|
2023-12-19T09:37:12Z
|
https://github.com/huggingface/datasets/pull/6509
|
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2024-01-26T16:18:41Z
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Read GeoParquet files using parquet reader
|
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CONTRIBUTOR
|
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
|
2024-01-26T18:22:35Z
|
https://github.com/huggingface/datasets/pull/6508
|
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2023-12-18T11:42:49Z
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not_planned
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where is glue_metric.py> @Frankie123421 what was the resolution to this?
|
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|
> @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_
|
2023-12-18T11:42:49Z
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https://github.com/huggingface/datasets/issues/6507
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2023-12-21T09:57:57Z
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Incorrect test set labels for RTE and CoLA datasets via load_dataset
|
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2023-12-16T22:06:08Z
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NONE
|
### 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
|
2023-12-21T09:57:57Z
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https://github.com/huggingface/datasets/issues/6506
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Got stuck when I trying to load a dataset
|
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2023-12-16T11:51:07Z
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NONE
|
### 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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pydub 0.25.1
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python-dateutil 2.8.2
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referencing 0.32.0
regex 2023.10.3
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rich 13.7.0
rouge-chinese 1.0.3
rpds-py 0.13.2
safetensors 0.4.1
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sentencepiece 0.1.99
setuptools 68.2.2
shtab 1.6.5
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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
tyro 0.6.0
tzdata 2023.3
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websockets 11.0.3
wheel 0.41.2
xxhash 3.4.1
yapf 0.40.2
yarl 1.9.4
zipp 3.17.0
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2024-02-08T21:52:05Z
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Error Pushing to Hub
|
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2023-12-16T01:05:22Z
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NONE
|
### 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
|
2023-12-16T06:20:53Z
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https://github.com/huggingface/datasets/issues/6504
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2023-12-15T14:44:47Z
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Fix streaming xnli
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MEMBER
|
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
|
2023-12-15T14:51:06Z
|
https://github.com/huggingface/datasets/pull/6503
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2023-12-15T14:58:22Z
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Pickle support for `torch.Generator` objects
|
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2023-12-15T13:55:12Z
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CONTRIBUTOR
|
Fix for https://discuss.huggingface.co/t/caching-a-dataset-processed-with-randomness/65616
|
2023-12-15T15:04:33Z
|
https://github.com/huggingface/datasets/pull/6502
|
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OverflowError: value too large to convert to int32_t
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NONE
|
### Describe the bug
### 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
|
2023-12-15T10:10:21Z
|
https://github.com/huggingface/datasets/issues/6501
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Enable setting config as default when push_to_hub
|
https://api.github.com/repos/huggingface/datasets/issues/6500/events
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2023-12-15T09:17:41Z
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MEMBER
|
Fix #6497.
|
2023-12-18T11:56:11Z
|
https://github.com/huggingface/datasets/pull/6500
|
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2023-12-15T11:42:38Z
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docs: add reference Git over SSH
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CONTRIBUTOR
|
see https://discuss.huggingface.co/t/update-datasets-getting-started-to-new-git-security/65893
|
2023-12-15T11:48:47Z
|
https://github.com/huggingface/datasets/pull/6499
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2023-12-15T13:10:48Z
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Fallback on dataset script if user wants to load default config
|
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MEMBER
|
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")
```
|
2023-12-15T13:16:56Z
|
https://github.com/huggingface/datasets/pull/6498
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2023-12-18T11:50:04Z
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Support setting a default config name in push_to_hub
|
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MEMBER
|
In order to convert script-datasets to no-script datasets, we need to support setting a default config name for those scripts that set one.
|
2023-12-18T11:50:04Z
|
https://github.com/huggingface/datasets/issues/6497
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Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.
|
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NONE
|
**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
```
|
2023-12-14T12:22:21Z
|
https://github.com/huggingface/datasets/issues/6496
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Image Data loaded Twice
|
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2023-12-13T13:11:42Z
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### Describe the bug
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
|
2023-12-13T13:11:42Z
|
https://github.com/huggingface/datasets/issues/6494
|
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Newline characters don't behave as expected when calling dataset.info
|
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### 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
)
|
2023-12-13T13:24:22Z
|
https://github.com/huggingface/datasets/issues/6495
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2023-12-21T15:13:11Z
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Lazy data files resolution and offline cache reload
|
https://api.github.com/repos/huggingface/datasets/issues/6493/events
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MEMBER
|
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
|
2023-12-21T15:19:20Z
|
https://github.com/huggingface/datasets/pull/6493
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2023-12-13T14:22:41Z
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Make push_to_hub return CommitInfo
|
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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
|
2023-12-13T14:29:01Z
|
https://github.com/huggingface/datasets/pull/6492
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2023-12-21T15:08:53Z
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Fix metrics dead link
|
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`load_dataset(...,save_infos=True)` not working without loading script
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NONE
|
### 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
|
2023-12-12T08:36:22Z
|
https://github.com/huggingface/datasets/issues/6490
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load_dataset imageflder for aws s3 path
|
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2023-12-12T00:08:43Z
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NONE
|
### 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
|
2023-12-12T00:09:27Z
|
https://github.com/huggingface/datasets/issues/6489
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429 Client Error
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NONE
|
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`
|
2024-01-18T02:05:15Z
|
https://github.com/huggingface/datasets/issues/6488
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2023-12-11T11:41:34Z
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Update builder hash with info
|
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MEMBER
|
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
|
2024-01-11T06:35:07Z
|
https://github.com/huggingface/datasets/pull/6487
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2023-12-13T14:15:21Z
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Fix docs phrasing about supported formats when sharing a dataset
|
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MEMBER
|
Fix docs phrasing.
|
2023-12-13T14:21:29Z
|
https://github.com/huggingface/datasets/pull/6486
|
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FileNotFoundError: [Errno 2] No such file or directory: 'nul'
|
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NONE
|
### Describe the bug
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'
### 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
|
2023-12-14T08:09:08Z
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https://github.com/huggingface/datasets/issues/6485
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Iterable Dataset: rename column clashes with remove column
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2023-12-08T16:11:30Z
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CONTRIBUTOR
|
### 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)
# 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
|
2023-12-08T16:27:16Z
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https://github.com/huggingface/datasets/issues/6483
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[Feature Request] Dataset versioning
|
https://api.github.com/repos/huggingface/datasets/issues/6484/events
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https://api.github.com/repos/huggingface/datasets/issues/6484/labels{/name}
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2023-12-08T16:01:35Z
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| null | null | 2,033,333,294
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https://api.github.com/repos/huggingface/datasets
|
NONE
|
**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"]))
```
|
2023-12-11T19:13:46Z
|
https://github.com/huggingface/datasets/issues/6484
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2023-12-12T11:47:27Z
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Fix max lock length on unix
|
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MEMBER
|
reported in https://github.com/huggingface/datasets/pull/6482
|
2023-12-12T11:53:32Z
|
https://github.com/huggingface/datasets/pull/6482
|
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|
using torchrun, save_to_disk suddenly shows SIGTERM
|
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NONE
|
### 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%|██ | 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
|
2023-12-08T13:22:03Z
|
https://github.com/huggingface/datasets/issues/6481
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2023-12-08T13:26:54Z
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|
Add IterableDataset `__repr__`
|
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MEMBER
|
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}
```
|
2023-12-08T13:33:06Z
|
https://github.com/huggingface/datasets/pull/6480
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2023-12-06T19:41:06Z
| null | null |
PR_kwDODunzps5hVLom
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closed
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More robust preupload retry mechanism
|
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CONTRIBUTOR
| null |
2023-12-06T19:47:29Z
|
https://github.com/huggingface/datasets/pull/6479
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How to load data from lakefs
|
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|
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
|
2023-12-07T02:19:44Z
|
https://github.com/huggingface/datasets/issues/6478
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Fix PermissionError on Windows CI
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MEMBER
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Fix #6476.
|
2023-12-06T09:24:11Z
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https://github.com/huggingface/datasets/pull/6477
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MEMBER
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See: https://github.com/huggingface/datasets/actions/runs/7104781624/job/19340572394
```
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'
```
|
2023-12-06T09:17:53Z
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https://github.com/huggingface/datasets/issues/6476
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laion2B-en failed to load on Windows with PrefetchVirtualMemory failed
|
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NONE
|
### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 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
|
2023-12-06T23:26:23Z
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https://github.com/huggingface/datasets/issues/6475
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Deprecate Beam API and download from HF GCS bucket
|
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CONTRIBUTOR
|
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))
- [x] Make [`natural_questions`](https://huggingface.co/datasets/natural_questions) a no-code dataset ([Hub PR](https://huggingface.co/datasets/natural_questions/discussions/7))
- [x] Make [`wiki40b`](https://huggingface.co/datasets/wiki40b) a no-code dataset ([Hub PR](https://huggingface.co/datasets/wiki40b/discussions/5))
- [x] Make [`wiki_dpr`](https://huggingface.co/datasets/wiki_dpr) an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wiki_dpr/discussions/14))
|
2024-03-11T16:21:18Z
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https://github.com/huggingface/datasets/pull/6474
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Fix CI quality
|
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Fix #6472.
|
2023-12-05T18:14:50Z
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https://github.com/huggingface/datasets/pull/6473
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CI quality is broken
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https://api.github.com/repos/huggingface/datasets/issues/6472/events
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MEMBER
|
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
```
|
2023-12-06T08:17:34Z
|
https://github.com/huggingface/datasets/issues/6472
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2023-12-05T12:38:50Z
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Remove delete doc CI
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I_kwDODunzps54rtdf
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open
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If an image in a dataset is corrupted, we get unescapable error
|
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2023-12-04T20:58:49Z
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https://api.github.com/repos/huggingface/datasets
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NONE
|
### Describe the bug
Example discussed in detail here: https://huggingface.co/datasets/sasha/birdsnap/discussions/1
### Steps to reproduce the bug
```
from datasets import load_dataset, VerificationMode
dataset = load_dataset(
'sasha/birdsnap',
split="train",
verification_mode=VerificationMode.ALL_CHECKS,
streaming=True # I recommend using streaming=True when reproducing, as this dataset is large
)
for idx, row in enumerate(dataset):
# Iterating to 9287 took 7 minutes for me
# If you already have the data locally cached and set streaming=False, you see the same error just by with dataset[9287]
pass
# error at 9287 OSError: image file is truncated (45 bytes not processed)
# note that we can't avoid the error using a try/except + continue inside the loop
```
### Expected behavior
Able to escape errors in casting to Image() without killing the whole loop
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-5.15.0-84-generic-x86_64-with-glibc2.31
- Python version: 3.11.5
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.10.0
|
2023-12-04T20:58:49Z
|
https://github.com/huggingface/datasets/issues/6470
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2023-12-15T13:12:30Z
| null | null |
PR_kwDODunzps5hC6xf
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closed
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Don't expand_info in HF glob
|
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MEMBER
|
Finally fix https://github.com/huggingface/datasets/issues/5537
|
2023-12-15T13:18:37Z
|
https://github.com/huggingface/datasets/pull/6469
|
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2023-12-04T17:15:11Z
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PR_kwDODunzps5hCpbN
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closed
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Use auth to get parquet export
|
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2023-12-04T11:18:27Z
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MEMBER
|
added `token` to the `_datasets_server` functions
|
2023-12-04T17:21:22Z
|
https://github.com/huggingface/datasets/pull/6468
|
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2023-12-04T15:42:22Z
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completed
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I_kwDODunzps54lzBZ
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closed
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New version release request
|
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2023-12-04T07:08:26Z
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CONTRIBUTOR
|
### Feature request
Hi!
I am using `datasets` in library `xtuner` and am highly interested in the features introduced since v2.15.0.
To avoid installation from source in our pypi wheels, we are eagerly waiting for the new release. So, Does your team have a new release plan for v2.15.1 and could you please share it with us?
Thanks very much!
### Motivation
.
### Your contribution
.
|
2023-12-04T15:42:22Z
|
https://github.com/huggingface/datasets/issues/6467
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Can't align optional features of struct
|
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2023-12-03T15:57:07Z
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https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
### Describe the bug
Hello!
I'm currently experiencing an issue where I can't concatenate datasets if an inner field of a Feature is Optional.
I have a column named `speaker`, and this holds some information about a speaker.
```python
@dataclass
class Speaker:
name: str
email: Optional[str]
```
If I have two datasets, one happens to have `email` always None, then I get `The features can't be aligned because the key email of features`
### Steps to reproduce the bug
You can run the following script:
```python
ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]})
ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]})
concatenate_datasets([ds, ds2])
>>>The features can't be aligned because the key speaker of features {'speaker': {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)}} has unexpected type - {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)} (expected either {'email': Value(dtype='null', id=None), 'name': Value(dtype='string', id=None)} or Value("null").
```
### Expected behavior
I think this should work; if two top-level columns were in the same situation it would properly cast to `string`.
```python
ds = Dataset.from_dict({'email': [None, None]})
ds2 = Dataset.from_dict({'email': ['abc@aol.com', 'one@yahoo.com']})
concatenate_datasets([ds, ds2])
>>> # Works!
```
### Environment info
- `datasets` version: 2.15.1.dev0
- Platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.35
- Python version: 3.9.13
- `huggingface_hub` version: 0.19.4
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
- `fsspec` version: 2023.6.0
I would be happy to fix this issue.
|
2024-02-15T15:19:33Z
|
https://github.com/huggingface/datasets/issues/6466
|
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`load_dataset` uses out-of-date cache instead of re-downloading a changed dataset
|
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NONE
|
### Describe the bug
When a dataset is updated on the hub, using `load_dataset` will load the locally cached dataset instead of re-downloading the updated dataset
### Steps to reproduce the bug
Here is a minimal example script to
1. create an initial dataset and upload
2. download it so it is stored in cache
3. change the dataset and re-upload
4. redownload
```python
import time
from datasets import Dataset, DatasetDict, DownloadMode, load_dataset
username = "YOUR_USERNAME_HERE"
initial = Dataset.from_dict({"foo": [1, 2, 3]})
print(f"Intial {initial['foo']}")
initial_ds = DatasetDict({"train": initial})
initial_ds.push_to_hub("test")
time.sleep(1)
download = load_dataset(f"{username}/test", split="train")
changed = download.map(lambda x: {"foo": x["foo"] + 1})
print(f"Changed {changed['foo']}")
changed.push_to_hub("test")
time.sleep(1)
download_again = load_dataset(f"{username}/test", split="train")
print(f"Download Changed {download_again['foo']}")
# >>> gives the out-dated [1,2,3] when it should be changed [2,3,4]
```
The redownloaded dataset should be the changed dataset but it is actually the cached, initial dataset. Force-redownloading gives the correct dataset
```python
download_again_force = load_dataset(f"{username}/test", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD)
print(f"Force Download Changed {download_again_force['foo']}")
# >>> [2,3,4]
```
### Expected behavior
I assumed there should be some sort of hashing that should check for changes in the dataset and re-download if the hashes don't match
### Environment info
- `datasets` version: 2.15.0 │
- Platform: Linux-5.15.0-1028-nvidia-x86_64-with-glibc2.17 │
- Python version: 3.8.17 │
- `huggingface_hub` version: 0.19.4 │
- PyArrow version: 13.0.0 │
- Pandas version: 2.0.3 │
- `fsspec` version: 2023.6.0
|
2023-12-04T16:13:10Z
|
https://github.com/huggingface/datasets/issues/6465
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Add concurrent loading of shards to datasets.load_from_disk
|
https://api.github.com/repos/huggingface/datasets/issues/6464/events
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CONTRIBUTOR
|
In some file systems (like luster), memory mapping arrow files takes time. This can be accelerated by performing the mmap in parallel on processes or threads.
- Threads seem to be faster than processes when gathering the list of tables from the workers (see https://github.com/huggingface/datasets/issues/2252).
- I'm not sure if using threads would respect the `IN_MEMORY_MAX_SIZE` config.
- I'm not sure if we need to expose num_proc from `BaseReader.read` to `DatasetBuilder.as_dataset`. Since ` DatasetBuilder.as_dataset` is used in many places beside `load_dataset`.
### Tests on luster file system (on a shared partial node):
Loading 1231 shards of ~2GBs.
The files were pre-loaded in another process before the script runs (couldn't get a fresh node).
```python
import logging
from time import perf_counter
import datasets
logger = datasets.logging.get_logger(__name__)
datasets.logging.set_verbosity_info()
logging.basicConfig(level=logging.DEBUG, format="%(message)s")
class catchtime:
# context to measure loading time: https://stackoverflow.com/questions/33987060/python-context-manager-that-measures-time
def __init__(self, debug_print="Time", logger=logger):
self.debug_print = debug_print
self.logger = logger
def __enter__(self):
self.start = perf_counter()
return self
def __exit__(self, type, value, traceback):
self.time = perf_counter() - self.start
readout = f"{self.debug_print}: {self.time:.3f} seconds"
self.logger.info(readout)
dataset_path=""
# warmup
with catchtime("Loading in parallel", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=16)
# num_proc=16
with catchtime("Loading in parallel", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=16)
# num_proc=32
with catchtime("Loading in parallel", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=32)
# num_proc=1
with catchtime("Loading in conseq", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=1)
```
#### Run 1
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:28<00:00, 13.96shards/s]
Loading in parallel: 88.690 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:48<00:00, 11.31shards/s]
Loading in parallel: 109.339 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:06<00:00, 18.56shards/s]
Loading in parallel: 66.931 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:09<00:00, 3.98shards/s]
Loading in conseq: 309.792 seconds
```
#### Run 2
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:38<00:00, 12.53shards/s]
Loading in parallel: 98.831 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:01<00:00, 10.16shards/s]
Loading in parallel: 121.669 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:07<00:00, 18.18shards/s]
Loading in parallel: 68.192 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:19<00:00, 3.86shards/s]
Loading in conseq: 319.759 seconds
```
#### Run 3
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:36<00:00, 12.74shards/s]
Loading in parallel: 96.936 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:00<00:00, 10.24shards/s]
Loading in parallel: 120.761 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:08<00:00, 18.04shards/s]
Loading in parallel: 68.666 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:35<00:00, 3.67shards/s]
Loading in conseq: 335.777 seconds
```
fix #2252
|
2024-01-26T15:17:43Z
|
https://github.com/huggingface/datasets/pull/6464
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Disable benchmarks in PRs
|
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MEMBER
|
In order to keep PR pages less spammy / more readable.
Having the benchmarks on commits on `main` is enough imo
|
2023-12-01T12:09:09Z
|
https://github.com/huggingface/datasets/pull/6463
|
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2023-11-30T18:30:30Z
| null | null |
PR_kwDODunzps5gz68T
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Missing DatasetNotFoundError
|
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MEMBER
|
continuation of https://github.com/huggingface/datasets/pull/6431
this should fix the CI in https://github.com/huggingface/datasets/pull/6458 too
|
2023-11-30T18:36:40Z
|
https://github.com/huggingface/datasets/pull/6462
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2023-12-01T17:51:33Z
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Fix shard retry mechanism in `push_to_hub`
|
https://api.github.com/repos/huggingface/datasets/issues/6461/events
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2023-11-30T14:57:14Z
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CONTRIBUTOR
|
When it fails, `preupload_lfs_files` throws a [`RuntimeError`](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/_commit_api.py#L402) error and chains the original HTTP error. This PR modifies the retry mechanism's error handling to account for that.
Fix https://github.com/huggingface/datasets/issues/6392
|
2023-12-01T17:57:39Z
|
https://github.com/huggingface/datasets/pull/6461
|
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jsonlines files don't load with `load_dataset`
|
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NONE
|
### Describe the bug
While [the docs](https://huggingface.co/docs/datasets/upload_dataset#upload-dataset) seem to state that `.jsonl` is a supported extension for `datasets`, loading the dataset results in a `JSONDecodeError`.
### Steps to reproduce the bug
Code:
```
from datasets import load_dataset
dset = load_dataset('slotreck/pickle')
```
Traceback:
```
Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 925/925 [00:00<00:00, 3.11MB/s]
Downloading and preparing dataset json/slotreck--pickle to /mnt/home/lotrecks/.cache/huggingface/datasets/slotreck___json/slotreck--pickle-0c311f36ed032b04/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96...
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 589k/589k [00:00<00:00, 18.9MB/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 104k/104k [00:00<00:00, 4.61MB/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 170k/170k [00:00<00:00, 7.71MB/s]
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3.77it/s]
Extracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 523.92it/s]
Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/mnt/home/lotrecks/.cache/huggingface/datasets/downloads/6ec07bb2f279c9377036af6948532513fa8f48244c672d2644a2d7018ee5c9cb' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0
Traceback (most recent call last):
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 144, in _generate_tables
dataset = json.load(f)
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 296, in load
parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 348, in loads
return _default_decoder.decode(s)
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/decoder.py", line 340, in decode
raise JSONDecodeError("Extra data", s, end)
json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 3086)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
for _, table in generator:
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 147, in _generate_tables
raise e
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
File "pyarrow/_json.pyx", line 259, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/load.py", line 1815, in load_dataset
storage_options=storage_options,
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 913, in download_and_prepare
**download_and_prepare_kwargs,
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1004, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1768, in _prepare_split
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1912, 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
```
### Expected behavior
For the dataset to be loaded without error.
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core
- Python version: 3.7.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
|
2023-12-29T02:58:29Z
|
https://github.com/huggingface/datasets/issues/6460
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Retrieve cached datasets that were pushed to hub when offline
|
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https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
I drafted the logic to retrieve a no-script dataset in the cache.
For example 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 from /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/*/*/0b3caccda1725efb(last modified on Wed Nov 29 16:50:27 2023) since it couldn't be found locally at lhoestq/tmp.
DatasetDict({
train: Dataset({
features: ['a'],
num_rows: 2
})
})
```
fix https://github.com/huggingface/datasets/issues/3547
## Implementation details (EDITED)
I continued in https://github.com/huggingface/datasets/pull/6493, see the changes there
TODO:
- [x] tests
- [ ] compatible with https://github.com/huggingface/datasets/pull/6458
|
2023-12-13T13:54:48Z
|
https://github.com/huggingface/datasets/pull/6459
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2024-02-08T14:41:35Z
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Lazy data files resolution
|
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|
MEMBER
|
Related to discussion at https://github.com/huggingface/datasets/pull/6255
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
|
2024-02-08T14:41:35Z
|
https://github.com/huggingface/datasets/pull/6458
|
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2023-11-29T02:02:38Z
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`TypeError`: huggingface_hub.hf_file_system.HfFileSystem.find() got multiple values for keyword argument 'maxdepth'
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NONE
|
### Describe the bug
Please see https://github.com/huggingface/huggingface_hub/issues/1872
### Steps to reproduce the bug
Please see https://github.com/huggingface/huggingface_hub/issues/1872
### Expected behavior
Please see https://github.com/huggingface/huggingface_hub/issues/1872
### Environment info
Please see https://github.com/huggingface/huggingface_hub/issues/1872
|
2023-11-29T15:39:03Z
|
https://github.com/huggingface/datasets/issues/6457
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2023-11-30T10:34:12Z
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PR_kwDODunzps5gmDJY
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Don't require trust_remote_code in inspect_dataset
|
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2023-11-28T19:47:07Z
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MEMBER
|
don't require `trust_remote_code` in (deprecated) `inspect_dataset` (it defeats its purpose)
(not super important but we might as well keep it until the next major release)
this is needed to fix the tests in https://github.com/huggingface/datasets/pull/6448
|
2023-11-30T10:40:23Z
|
https://github.com/huggingface/datasets/pull/6456
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2023-11-28T16:29:31Z
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Refactor `dill` logic
|
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CONTRIBUTOR
|
Refactor the `dill` logic to make it easier to maintain (and fix some issues along the way)
It makes the following improvements to the serialization API:
* consistent order of a `dict`'s keys
* support for hashing `torch.compile`-ed modules and functions
* deprecates `datasets.fingerprint.hashregister` as the `hashregister`-ed reducers are never invoked anyways (does not support nested data as `pickle`/`dill` do)
~~TODO: optimize hashing of `pa.Table` and `datasets.table.Table`~~ The `pa_array.to_string` approach is faster for large arrays because it outputs the first 10 and last 10 elements (by default). The problem is that this can produce identical hashes for non-identical arrays if their differing elements get ellipsed...
Fix https://github.com/huggingface/datasets/issues/6440, fix https://github.com/huggingface/datasets/issues/5839
|
2023-11-28T16:29:58Z
|
https://github.com/huggingface/datasets/pull/6454
|
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Update hub-docs reference
|
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Follow up to huggingface/huggingface.js#296
|
2023-11-27T10:23:44Z
|
https://github.com/huggingface/datasets/pull/6453
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Unable to read "marsyas/gtzan" data
|
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NONE
|
Hi, this is my code and the error:
```
from datasets import load_dataset
gtzan = load_dataset("marsyas/gtzan", "all")
```
[error_trace.txt](https://github.com/huggingface/datasets/files/13464397/error_trace.txt)
[audio_yml.txt](https://github.com/huggingface/datasets/files/13464410/audio_yml.txt)
Python 3.11.5
Jupyter Notebook 6.5.4
Windows 10
I'm able to download and work with other datasets, but not this one. For example, both these below work fine:
```
from datasets import load_dataset
dataset = load_dataset("facebook/voxpopuli", "pl", split="train", streaming=True)
minds = load_dataset("PolyAI/minds14", name="en-US", split="train")
```
Thanks for your help
https://huggingface.co/datasets/marsyas/gtzan/tree/main
|
2023-12-01T12:53:46Z
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https://github.com/huggingface/datasets/issues/6451
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Support multiple image/audio columns in ImageFolder/AudioFolder
|
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CONTRIBUTOR
|
### Feature request
Have a metadata.csv file with multiple columns that point to relative image or audio files.
### Motivation
Currently, ImageFolder allows one column, called `file_name`, pointing to relative image files. On the same model, AudioFolder allows one column, called `file_name`, pointing to relative audio files.
But it's not possible to have two image columns, or to have two audio column, or to have one audio column and one image column.
### Your contribution
no specific contribution
|
2023-11-28T11:07:17Z
|
https://github.com/huggingface/datasets/issues/6450
|
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Fix metadata file resolution when inferred pattern is `**`
|
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CONTRIBUTOR
|
Refetch metadata files in case they were dropped by `filter_extensions` in the previous step.
Fix #6442
|
2023-11-27T10:02:56Z
|
https://github.com/huggingface/datasets/pull/6449
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Use parquet export if possible
|
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MEMBER
|
The idea is to make this code work for datasets with scripts if they have a Parquet export
```python
ds = load_dataset("squad", trust_remote_code=False)
```
And more generally, it means we use the Parquet export whenever it's possible (it's safer and faster than dataset scripts).
I also added a `config.USE_PARQUET_EXPORT` variable to use in the datasets-server parquet conversion job
- [x] Needs https://github.com/huggingface/datasets/pull/6429 to be merged first
cc @severo I use the /parquet and /info endpoints from datasets-server
|
2023-12-01T17:57:17Z
|
https://github.com/huggingface/datasets/pull/6448
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Support one dataset loader per config when using YAML
|
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CONTRIBUTOR
|
### Feature request
See https://huggingface.co/datasets/datasets-examples/doc-unsupported-1
I would like to use CSV loader for the "csv" config, JSONL loader for the "jsonl" config, etc.
### Motivation
It would be more flexible for the users
### Your contribution
No specific contribution
|
2023-11-23T13:03:07Z
|
https://github.com/huggingface/datasets/issues/6447
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Speech Commands v2 dataset doesn't match AST-v2 config
|
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2023-11-22T20:46:36Z
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| null | null | 2,007,092,708
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https://api.github.com/repos/huggingface/datasets/issues/6446
|
[
"",
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https://api.github.com/repos/huggingface/datasets
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NONE
|
### Describe the bug
[According](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) to `MIT/ast-finetuned-speech-commands-v2`, the model was trained on the Speech Commands v2 dataset. However, while the model config says the model should have 35 class labels, the dataset itself has 36 class labels. Moreover, the class labels themselves don't match between the model config and the dataset. It is difficult to reproduce the data used to fine tune `MIT/ast-finetuned-speech-commands-v2`.
### Steps to reproduce the bug
```
>>> model = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-speech-commands-v2")
>>> model.config.id2label
{0: 'backward', 1: 'follow', 2: 'five', 3: 'bed', 4: 'zero', 5: 'on', 6: 'learn', 7: 'two', 8: 'house', 9: 'tree', 10: 'dog', 11: 'stop', 12: 'seven', 13: 'eight', 14: 'down', 15: 'six', 16: 'forward', 17: 'cat', 18: 'right', 19: 'visual', 20: 'four', 21: 'wow', 22: 'no', 23: 'nine', 24: 'off', 25: 'three', 26: 'left', 27: 'marvin', 28: 'yes', 29: 'up', 30: 'sheila', 31: 'happy', 32: 'bird', 33: 'go', 34: 'one'}
>>> dataset = load_dataset("speech_commands", "v0.02", split="test")
>>> torch.unique(torch.Tensor(dataset['label']))
tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,
14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
28., 29., 30., 31., 32., 33., 34., 35.])
```
If you try to explore the [dataset itself](https://huggingface.co/datasets/speech_commands/viewer/v0.02/test), you can see that the id to label does not match what is provided by `model.config.id2label`.
### Expected behavior
The labels should match completely and there should be the same number of label classes between the model config and the dataset itself.
### Environment info
datasets = 2.14.6, transformers = 4.33.3
|
2023-11-28T14:46:08Z
|
https://github.com/huggingface/datasets/issues/6446
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2023-11-23T18:41:23Z
| null | null |
PR_kwDODunzps5gKg2d
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closed
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|
Use `filelock` package for file locking
|
https://api.github.com/repos/huggingface/datasets/issues/6445/events
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https://api.github.com/repos/huggingface/datasets/issues/6445/labels{/name}
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2023-11-22T19:04:45Z
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https://api.github.com/repos/huggingface/datasets/issues/6445
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[
"",
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https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
Use the `filelock` package instead of `datasets.utils.filelock` for file locking to be consistent with `huggingface_hub` and not to be responsible for improving the `filelock` capabilities 🙂.
(Reverts https://github.com/huggingface/datasets/pull/859, but these `INFO` logs are not printed by default (anymore?), so this should be okay)
|
2023-11-23T18:47:30Z
|
https://github.com/huggingface/datasets/pull/6445
|
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2023-11-23T15:13:28Z
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PR_kwDODunzps5gKG_e
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closed
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Remove `Table.__getstate__` and `Table.__setstate__`
|
https://api.github.com/repos/huggingface/datasets/issues/6444/events
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2023-11-22T17:55:10Z
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https://api.github.com/repos/huggingface/datasets/issues/6444
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https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
When using distributed training, the code of `os.remove(filename)` may be executed separately by each rank, leading to `FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmprxxxxxxx.arrow'`
```python
from torch import distributed as dist
if dist.get_rank() == 0:
dataset = process_dataset(*args, **kwargs)
objects = [dataset]
else:
objects = [None]
dist.broadcast_object_list(objects, src=0)
dataset = objects[0]
```
|
2023-11-23T15:19:43Z
|
https://github.com/huggingface/datasets/pull/6444
|
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"followers_url": "https://api.github.com/users/severo/followers",
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"html_url": "https://github.com/severo",
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Trouble loading files defined in YAML explicitly
|
https://api.github.com/repos/huggingface/datasets/issues/6443/events
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2023-11-22T15:18:10Z
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https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
Look at https://huggingface.co/datasets/severo/doc-yaml-2
It's a reproduction of the example given in the docs at https://huggingface.co/docs/hub/datasets-manual-configuration
```
You can select multiple files per split using a list of paths:
my_dataset_repository/
├── README.md
├── data/
│ ├── abc.csv
│ └── def.csv
└── holdout/
└── ghi.csv
---
configs:
- config_name: default
data_files:
- split: train
path:
- "data/abc.csv"
- "data/def.csv"
- split: test
path: "holdout/ghi.csv"
---
```
It raises the following error:
```
Error code: ConfigNamesError
Exception: FileNotFoundError
Message: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response
for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1507, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']
```
|
2023-11-23T09:06:20Z
|
https://github.com/huggingface/datasets/issues/6443
|
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2023-11-24T17:13:03Z
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Trouble loading image folder with additional features - metadata file ignored
|
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2023-11-22T11:01:35Z
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[
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https://api.github.com/repos/huggingface/datasets
|
NONE
|
### Describe the bug
Loading image folder with a caption column using `load_dataset(<image_folder_path>)` doesn't load the captions.
When loading a local image folder with captions using `datasets==2.13.0`
```
from datasets import load_dataset
data = load_dataset(<image_folder_path>)
data.column_names
```
yields
`{'train': ['image', 'prompt']}`
but when using `datasets==2.15.0`
yeilds
`{'train': ['image']}`
Putting the images and `metadata.jsonl` file into a nested `train` folder **or** loading with `load_dataset("imagefolder", data_dir=<image_folder_path>)` solves the issue and
yields
`{'train': ['image', 'prompt']}`
### Steps to reproduce the bug
1. create a folder `<image_folder_path>` that contains images and a metadata file with additional features- e.g. "prompt"
2. run:
```
from datasets import load_dataset
data = load_dataset("<image_folder_path>")
data.column_names
```
### Expected behavior
`{'train': ['image', 'prompt']}`
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0
|
2023-11-24T17:13:03Z
|
https://github.com/huggingface/datasets/issues/6442
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2023-12-13T08:27:16Z
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|
Trouble Loading a Gated Dataset For User with Granted Permission
|
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2023-11-21T19:24:36Z
| null | false
| null | null | 2,004,985,857
|
[] |
https://api.github.com/repos/huggingface/datasets/issues/6441
|
[
"",
""
] |
https://api.github.com/repos/huggingface/datasets
|
NONE
|
### Describe the bug
I have granted permissions to several users to access a gated huggingface dataset. The users accepted the invite and when trying to load the dataset using their access token they get
`FileNotFoundError: Couldn't find a dataset script at .....` . Also when they try to click the url link for the dataset they get a 404 error.
### Steps to reproduce the bug
1. Grant access to gated dataset for specific users
2. Users accept invitation
3. Users login to hugging face hub using cli login
4. Users run load_dataset
### Expected behavior
Dataset is loaded normally for users who were granted access to the gated dataset.
### Environment info
datasets==2.15.0
|
2023-12-13T08:27:16Z
|
https://github.com/huggingface/datasets/issues/6441
|
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2023-11-28T16:29:33Z
| null |
completed
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I_kwDODunzps53emJ1
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closed
|
[] | null | 6,440
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`.map` not hashing under python 3.9
|
https://api.github.com/repos/huggingface/datasets/issues/6440/events
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https://api.github.com/repos/huggingface/datasets/issues/6440/labels{/name}
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2023-11-21T15:14:54Z
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| null | null | 2,004,509,301
|
[] |
https://api.github.com/repos/huggingface/datasets/issues/6440
|
[
"",
""
] |
https://api.github.com/repos/huggingface/datasets
|
NONE
|
### Describe the bug
The `.map` function cannot hash under python 3.9. Tried to use [the solution here](https://github.com/huggingface/datasets/issues/4521#issuecomment-1205166653), but still get the same message:
`Parameter 'function'=<function map_to_pred at 0x7fa0b49ead30> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.`
### Steps to reproduce the bug
```python
def map_to_pred(batch):
"""
Perform inference on an audio batch
Parameters:
batch (dict): A dictionary containing audio data and other related information.
Returns:
dict: The input batch dictionary with added prediction and transcription fields.
"""
audio = batch['audio']
input_features = processor(
audio['array'], sampling_rate=audio['sampling_rate'], return_tensors="pt").input_features
input_features = input_features.to('cuda')
with torch.no_grad():
predicted_ids = model.generate(input_features)
preds = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
batch['prediction'] = processor.tokenizer._normalize(preds)
batch["transcription"] = processor.tokenizer._normalize(batch['transcription'])
return batch
MODEL_CARD = "openai/whisper-small"
MODEL_NAME = MODEL_CARD.rsplit('/', maxsplit=1)[-1]
model = WhisperForConditionalGeneration.from_pretrained(MODEL_CARD)
processor = AutoProcessor.from_pretrained(
MODEL_CARD, language="english", task="transcribe")
model = torch.compile(model)
dt = load_dataset("audiofolder", data_dir=config['DATA']['dataset'], split="test")
dt = dt.cast_column("audio", Audio(sampling_rate=16000))
result = coraal_dt.map(map_to_pred, num_proc=16)
```
### Expected behavior
Hashed and cached dataset starts inferencing
### Environment info
- `transformers` version: 4.35.0
- Platform: Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.18
- Huggingface_hub version: 0.17.3
- Safetensors version: 0.4.0
- Accelerate version: 0.24.1
- Accelerate config: not found
- PyTorch version (GPU?): 2.1.0 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no
|
2023-11-28T16:29:33Z
|
https://github.com/huggingface/datasets/issues/6440
|
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I_kwDODunzps53YhSi
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Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding
|
https://api.github.com/repos/huggingface/datasets/issues/6439/events
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https://api.github.com/repos/huggingface/datasets/issues/6439/labels{/name}
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2023-11-20T20:07:23Z
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https://api.github.com/repos/huggingface/datasets/issues/6439
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https://api.github.com/repos/huggingface/datasets
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NONE
|
### Describe the bug
I am working with a dataset I am trying to publish.
The path is Antreas/TALI.
It's a fairly large dataset, and contains images, video, audio and text.
I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process.
With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths.
Find the script I am using below:
```python
import multiprocessing as mp
import pathlib
from typing import Optional
import datasets
from rich import print
from tqdm import tqdm
def download_dataset_via_hub(
dataset_name: str,
dataset_download_path: pathlib.Path,
num_download_workers: int = mp.cpu_count(),
):
import huggingface_hub as hf_hub
download_folder = hf_hub.snapshot_download(
repo_id=dataset_name,
repo_type="dataset",
cache_dir=dataset_download_path,
resume_download=True,
max_workers=num_download_workers,
ignore_patterns=[],
)
return pathlib.Path(download_folder) / "data"
def load_dataset_via_hub(
dataset_download_path: pathlib.Path,
num_download_workers: int = mp.cpu_count(),
dataset_name: Optional[str] = None,
):
from dataclasses import dataclass, field
from datasets import ClassLabel, Features, Image, Sequence, Value
dataset_path = download_dataset_via_hub(
dataset_download_path=dataset_download_path,
num_download_workers=num_download_workers,
dataset_name=dataset_name,
)
# Building a list of file paths for validation set
train_files = [
file.as_posix()
for file in pathlib.Path(dataset_path).glob("*.parquet")
if "train" in file.as_posix()
]
val_files = [
file.as_posix()
for file in pathlib.Path(dataset_path).glob("*.parquet")
if "val" in file.as_posix()
]
test_files = [
file.as_posix()
for file in pathlib.Path(dataset_path).glob("*.parquet")
if "test" in file.as_posix()
]
print(
f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set"
)
data_files = {
"test": test_files,
"val": val_files,
"train": train_files,
}
features = Features(
{
"image": Image(
decode=True
), # Set `decode=True` if you want to decode the images, otherwise `decode=False`
"image_url": Value("string"),
"item_idx": Value("int64"),
"wit_features": Sequence(
{
"attribution_passes_lang_id": Value("bool"),
"caption_alt_text_description": Value("string"),
"caption_reference_description": Value("string"),
"caption_title_and_reference_description": Value("string"),
"context_page_description": Value("string"),
"context_section_description": Value("string"),
"hierarchical_section_title": Value("string"),
"is_main_image": Value("bool"),
"language": Value("string"),
"page_changed_recently": Value("bool"),
"page_title": Value("string"),
"page_url": Value("string"),
"section_title": Value("string"),
}
),
"wit_idx": Value("int64"),
"youtube_title_text": Value("string"),
"youtube_description_text": Value("string"),
"youtube_video_content": Value("binary"),
"youtube_video_starting_time": Value("string"),
"youtube_subtitle_text": Value("string"),
"youtube_video_size": Value("int64"),
"youtube_video_file_path": Value("string"),
}
)
dataset = datasets.load_dataset(
"parquet" if dataset_name is None else dataset_name,
data_files=data_files,
features=features,
num_proc=1,
cache_dir=dataset_download_path / "cache",
)
return dataset
if __name__ == "__main__":
dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/")
dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[
"test"
]
for sample in tqdm(dataset):
print(list(sample.keys()))
```
Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start!
### Steps to reproduce the bug
1. Run the code I provided to get a sense of how fast snapshot + manual is
2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP.
3. You should now have an appreciation of how long these things take.
### Expected behavior
The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower.
### Environment info
- `datasets` version: 2.14.5
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.13
- Huggingface_hub version: 0.17.3
- PyArrow version: 13.0.0
- Pandas version: 2.1.1
|
2023-11-20T20:07:37Z
|
https://github.com/huggingface/datasets/issues/6439
|
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Support GeoParquet
|
https://api.github.com/repos/huggingface/datasets/issues/6438/events
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2023-11-20T11:54:58Z
| null | false
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https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
### Feature request
Support the GeoParquet format
### Motivation
GeoParquet (https://geoparquet.org/) is a common format for sharing vectorial geospatial data on the cloud, along with "traditional" data columns.
It would be nice to be able to load this format with datasets, and more generally, in the Datasets Hub (see https://huggingface.co/datasets/joshuasundance/govgis_nov2023-slim-spatial/discussions/1).
### Your contribution
I would be happy to help work on a PR (but I don't think I can do one on my own).
Also, we have to define what we want to support:
- load all the columns, but get the "geospatial" column in text-only mode for now
- or, fully support the spatial features, maybe taking inspiration from (or depending upon) https://geopandas.org/en/stable/index.html (which itself depends on https://fiona.readthedocs.io/en/stable/, which requires a local install of https://gdal.org/)
|
2024-02-07T08:36:51Z
|
https://github.com/huggingface/datasets/issues/6438
|
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Problem in training iterable dataset
|
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NONE
|
### Describe the bug
I am using PyTorch DDP (Distributed Data Parallel) to train my model. Since the data is too large to load into memory at once, I am using load_dataset to read the data as an iterable dataset. I have used datasets.distributed.split_dataset_by_node to distribute the dataset. However, I have noticed that this distribution results in different processes having different amounts of data to train on. As a result, when the earliest process finishes training and starts predicting on the test set, other processes are still training, causing the overall training speed to be very slow.
### Steps to reproduce the bug
```
def train(args, model, device, train_loader, optimizer, criterion, epoch, length):
model.train()
idx_length = 0
for batch_idx, data in enumerate(train_loader):
s_time = time.time()
X = data['X']
target = data['y'].reshape(-1, 28)
X, target = X.to(device), target.to(device)
optimizer.zero_grad()
output = model(X)
loss = criterion(output, target)
loss.backward()
optimizer.step()
idx_length += 1
if batch_idx % args.log_interval == 0:
# print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
# epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(),
# 100. * batch_idx * len(
# X) * torch.distributed.get_world_size() / length, loss.item()))
print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\t'.format(
epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(),
100. * batch_idx * len(
X) * torch.distributed.get_world_size() / length))
if args.dry_run:
break
print('Process %s length: %s time: %s' % (torch.distributed.get_rank(), idx_length, datetime.datetime.now()))
train_iterable_dataset = load_dataset("parquet", data_files=data_files, split="train", streaming=True)
test_iterable_dataset = load_dataset("parquet", data_files=data_files, split="test", streaming=True)
train_iterable_dataset = train_iterable_dataset.map(process_fn)
test_iterable_dataset = test_iterable_dataset.map(process_fn)
train_iterable_dataset = train_iterable_dataset.map(scale)
test_iterable_dataset = test_iterable_dataset.map(scale)
train_iterable_dataset = datasets.distributed.split_dataset_by_node(train_iterable_dataset,
world_size=world_size, rank=local_rank).shuffle(seed=1234)
test_iterable_dataset = datasets.distributed.split_dataset_by_node(test_iterable_dataset,
world_size=world_size, rank=local_rank).shuffle(seed=1234)
print(torch.distributed.get_rank(), train_iterable_dataset.n_shards, test_iterable_dataset.n_shards)
train_kwargs = {'batch_size': args.batch_size}
test_kwargs = {'batch_size': args.test_batch_size}
if use_cuda:
cuda_kwargs = {'num_workers': 3,#ngpus_per_node,
'pin_memory': True,
'shuffle': False}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
train_loader = torch.utils.data.DataLoader(train_iterable_dataset, **train_kwargs,
# sampler=torch.utils.data.distributed.DistributedSampler(
# train_iterable_dataset,
# num_replicas=ngpus_per_node,
# rank=0)
)
test_loader = torch.utils.data.DataLoader(test_iterable_dataset, **test_kwargs,
# sampler=torch.utils.data.distributed.DistributedSampler(
# test_iterable_dataset,
# num_replicas=ngpus_per_node,
# rank=0)
)
for epoch in range(1, args.epochs + 1):
start_time = time.time()
train_iterable_dataset.set_epoch(epoch)
test_iterable_dataset.set_epoch(epoch)
train(args, model, device, train_loader, optimizer, criterion, epoch, train_len)
test(args, model, device, criterion2, test_loader)
```
And here’s the part of output:
```
Train Epoch: 1 Batch_idx: 5000 Process: 0 [320000/4710975.0 (7%)]
Train Epoch: 1 Batch_idx: 5000 Process: 1 [320000/4710975.0 (7%)]
Train Epoch: 1 Batch_idx: 5000 Process: 2 [320000/4710975.0 (7%)]
Train Epoch: 1 Batch_idx: 5862 Process: 3 Data_length: 12 coststime: 0.04095172882080078
Train Epoch: 1 Batch_idx: 5862 Process: 0 Data_length: 3 coststime: 0.0751960277557373
Train Epoch: 1 Batch_idx: 5867 Process: 3 Data_length: 49 coststime: 0.0032558441162109375
Train Epoch: 1 Batch_idx: 5872 Process: 1 Data_length: 2 coststime: 0.022842884063720703
Train Epoch: 1 Batch_idx: 5876 Process: 3 Data_length: 63 coststime: 0.002694845199584961
Process 3 length: 5877 time: 2023-11-17 17:03:26.582317
Train epoch 1 costTime: 241.72063446044922s . Process 3 Start to test.
3 0 tensor(45508.8516, device='cuda:3')
3 100 tensor(45309.0469, device='cuda:3')
3 200 tensor(45675.3047, device='cuda:3')
3 300 tensor(45263.0273, device='cuda:3')
Process 3 Reduce metrics.
Train Epoch: 2 Batch_idx: 0 Process: 3 [0/4710975.0 (0%)]
Train Epoch: 1 Batch_idx: 5882 Process: 1 Data_length: 63 coststime: 0.05185818672180176
Train Epoch: 1 Batch_idx: 5887 Process: 1 Data_length: 12 coststime: 0.006895303726196289
Process 1 length: 5888 time: 2023-11-17 17:20:48.578204
Train epoch 1 costTime: 1285.7279663085938s . Process 1 Start to test.
1 0 tensor(45265.9141, device='cuda:1')
```
### Expected behavior
I'd like to know how to fix this problem.
### Environment info
```
torch==2.0
datasets==2.14.0
```
|
2023-11-29T11:11:15Z
|
https://github.com/huggingface/datasets/issues/6437
|
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NONE
|
### Describe the bug
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-35-7b6becee3685>](https://localhost:8080/#) in <cell line: 1>()
----> 1 from datasets import Dataset
9 frames
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
20 __version__ = "2.15.0"
21
---> 22 from .arrow_dataset import Dataset
23 from .arrow_reader import ReadInstruction
24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
61 import pyarrow.compute as pc
62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi
---> 63 from multiprocess import Pool
64 from requests import HTTPError
65
[/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module>
31
32 import sys
---> 33 from . import context
34
35 #
[/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module>
4
5 from . import process
----> 6 from . import reduction
7
8 __all__ = ()
[/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module>
14 import os
15 try:
---> 16 import dill as pickle
17 except ImportError:
18 import pickle
[/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module>
24
25
---> 26 from ._dill import (
27 dump, dumps, load, loads, copy,
28 Pickler, Unpickler, register, pickle, pickles, check,
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module>
166 try:
167 from _pyio import open as _open
--> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open)
169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open)
170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open)
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs)
154 def get_file_type(*args, **kwargs):
155 open = kwargs.pop("open", __builtin__.open)
--> 156 f = open(os.devnull, *args, **kwargs)
157 t = type(f)
158 f.close()
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener)
280 return result
281 encoding = text_encoding(encoding)
--> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering)
283 result = text
284 text.mode = mode
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through)
2043 encoding = "utf-8"
2044 else:
-> 2045 encoding = locale.getpreferredencoding(False)
2046
2047 if not isinstance(encoding, str):
TypeError: <lambda>() takes 0 positional arguments but 1 was given
```
or
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-36-652e886d387f>](https://localhost:8080/#) in <cell line: 1>()
----> 1 import datasets
9 frames
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
20 __version__ = "2.15.0"
21
---> 22 from .arrow_dataset import Dataset
23 from .arrow_reader import ReadInstruction
24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
61 import pyarrow.compute as pc
62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi
---> 63 from multiprocess import Pool
64 from requests import HTTPError
65
[/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module>
31
32 import sys
---> 33 from . import context
34
35 #
[/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module>
4
5 from . import process
----> 6 from . import reduction
7
8 __all__ = ()
[/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module>
14 import os
15 try:
---> 16 import dill as pickle
17 except ImportError:
18 import pickle
[/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module>
24
25
---> 26 from ._dill import (
27 dump, dumps, load, loads, copy,
28 Pickler, Unpickler, register, pickle, pickles, check,
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module>
166 try:
167 from _pyio import open as _open
--> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open)
169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open)
170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open)
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs)
154 def get_file_type(*args, **kwargs):
155 open = kwargs.pop("open", __builtin__.open)
--> 156 f = open(os.devnull, *args, **kwargs)
157 t = type(f)
158 f.close()
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener)
280 return result
281 encoding = text_encoding(encoding)
--> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering)
283 result = text
284 text.mode = mode
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through)
2043 encoding = "utf-8"
2044 else:
-> 2045 encoding = locale.getpreferredencoding(False)
2046
2047 if not isinstance(encoding, str):
TypeError: <lambda>() takes 0 positional arguments but 1 was given
```
### Steps to reproduce the bug
`import datasets` on colab
### Expected behavior
work fine
### Environment info
colab
`!pip install datasets`
|
2023-11-29T16:28:34Z
|
https://github.com/huggingface/datasets/issues/6436
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Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
|
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2023-11-19T04:21:16Z
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NONE
|
### Describe the bug
1. I ran dataset mapping with `num_proc=6` in it and got this error:
`RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method`
I can't actually find a way to run multi-GPU dataset mapping. Can you help?
### Steps to reproduce the bug
1. Rund SDXL training with `num_proc=6`: https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py
### Expected behavior
Should work well
### Environment info
6x A100 SXM, Linux
|
2024-01-27T17:14:20Z
|
https://github.com/huggingface/datasets/issues/6435
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2023-11-21T14:13:13Z
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Use `ruff` for formatting
|
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CONTRIBUTOR
|
Use `ruff` instead of `black` for formatting to be consistent with `transformers` ([PR](https://github.com/huggingface/transformers/pull/27144)) and `huggingface_hub` ([PR 1](https://github.com/huggingface/huggingface_hub/pull/1783) and [PR 2](https://github.com/huggingface/huggingface_hub/pull/1789)).
|
2023-11-21T14:19:21Z
|
https://github.com/huggingface/datasets/pull/6434
|
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2023-11-22T16:42:08Z
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PR_kwDODunzps5fxDoG
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Better `tqdm` wrapper
|
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2023-11-17T15:45:15Z
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CONTRIBUTOR
|
This PR aligns the `tqdm` logic with `huggingface_hub` (without introducing breaking changes), as the current one is error-prone.
Additionally, it improves the doc page about the `datasets`' utilities, and the handling of local `fsspec` paths in `cached_path`.
Fix #6409
|
2023-11-22T16:48:18Z
|
https://github.com/huggingface/datasets/pull/6433
|
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load_dataset does not load all of the data in my input file
|
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NONE
|
### Describe the bug
I have 127 elements in my input dataset. When I do a len on the dataset after loaded, it is only 124 elements.
### Steps to reproduce the bug
train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION)
logger.info(len(train_dataset))
logger.info(len(valid_dataset))
Both train and valid input are 127 items. However, they both only load 124 items. The input format is in json. At the end of the day, I am trying to create .pt files.
### Expected behavior
I see all 127 elements in my dataset when performing len
### Environment info
Python 3.10. CentOS operating system. nlp==0.40, datasets==2.14.5, transformers==4.26.1
|
2023-11-22T17:34:58Z
|
https://github.com/huggingface/datasets/issues/6432
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Create DatasetNotFoundError and DataFilesNotFoundError
|
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MEMBER
|
Create `DatasetNotFoundError` and `DataFilesNotFoundError`.
Fix #6397.
CC: @severo
|
2023-11-22T15:18:51Z
|
https://github.com/huggingface/datasets/pull/6431
|
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2023-11-28T16:03:43Z
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PR_kwDODunzps5fn1r_
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Add trust_remote_code argument
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MEMBER
|
Draft about adding `trust_remote_code` to `load_dataset`.
```python
ds = load_dataset(..., trust_remote_code=True) # run remote code (current default)
```
It would default to `True` (current behavior) and in the next major release it will prompt the user to check the code before running it (we'll communicate on this before doing it of course).
```python
# in the future
ds = load_dataset(...) # prompt the user to check the code before running it (future default)
ds = load_dataset(..., trust_remote_code=True) # run remote code
ds = load_dataset(..., trust_remote_code=False) # disallow remote code
```
Related to https://github.com/huggingface/datasets/issues/6400
Will do a separate PR to use the parquet export when possible
|
2023-11-28T16:10:39Z
|
https://github.com/huggingface/datasets/pull/6429
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2023-11-16T08:13:28Z
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Set dev version
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2023-11-16T07:43:05Z
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Release: 2.15.0
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2023-12-01T15:31:19Z
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More robust temporary directory deletion
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CONTRIBUTOR
|
While fixing the Windows errors in #6362, I noticed that `PermissionError` can still easily be thrown on the session exit by the temporary cache directory's finalizer (we would also have to keep track of intermediate datasets, copies, etc.). ~~Due to the low usage of `datasets` on Windows, this PR takes a simpler approach to the issue than https://github.com/huggingface/datasets/pull/2403 - it tries to delete the temporary cache directory, and if this fails, logs a warning message about using a `delete-temp-cache` CLI command to delete it manually. The problematic references are freed after the session exits, so the CLI command should then succeed.~~ This PR implements `Dataset.__setstate__` to register datasets with temporary cache files for deletion.
|
2023-12-01T15:37:32Z
|
https://github.com/huggingface/datasets/pull/6426
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2023-12-13T14:16:00Z
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PR_kwDODunzps5fi5ye
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Fix deprecation warning when building conda package
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MEMBER
|
When building/releasing conda package, we get this deprecation warning:
```
/usr/share/miniconda/envs/build-datasets/bin/conda-build:11: DeprecationWarning: conda_build.cli.main_build.main is deprecated and will be removed in 4.0.0. Use `conda build` instead.
```
This PR fixes the deprecation warning by using `conda build` instead.
|
2023-12-13T14:22:30Z
|
https://github.com/huggingface/datasets/pull/6425
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2023-11-30T17:23:46Z
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[docs] troubleshooting guide
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CONTRIBUTOR
|
Hi all! This is a PR adding a troubleshooting guide for Datasets docs.
I went through the library's GitHub Issues and Forum questions and identified a few issues that are common enough that I think it would be valuable to include them in the troubleshooting guide. These are:
- creating a dataset from a folder and not following the required format
- authentication issues when using `push_to_hub`
- `Too Many Requests` with `push_to_hub`
- Pickling issues when using Dataset.from_generator()
There's also a section on asking for help. Please let me know if there are other common issues or advice that we can include here.
|
2023-11-30T17:29:55Z
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https://github.com/huggingface/datasets/pull/6424
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2023-11-15T17:09:24Z
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Fix conda release by adding pyarrow-hotfix dependency
|
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"diff_url": "https://github.com/huggingface/datasets/pull/6423.diff",
"html_url": "https://github.com/huggingface/datasets/pull/6423",
"merged_at": "2023-11-15T17:09:24Z",
"patch_url": "https://github.com/huggingface/datasets/pull/6423.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6423"
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| 1,994,946,847
|
[] |
https://api.github.com/repos/huggingface/datasets/issues/6423
|
[
"",
""
] |
https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
Fix conda release by adding pyarrow-hotfix dependency.
Note that conda release failed in latest 2.14.7 release: https://github.com/huggingface/datasets/actions/runs/6874667214/job/18696761723
```
Traceback (most recent call last):
File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/test_tmp/run_test.py", line 2, in <module>
import datasets
File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/__init__.py", line 22, in <module>
from .arrow_dataset import Dataset
File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 67, in <module>
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_writer.py", line 27, in <module>
from .features import Features, Image, Value
File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/__init__.py", line 18, in <module>
from .features import Array2D, Array3D, Array4D, Array5D, ClassLabel, Features, Sequence, Value
File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/features.py", line 34, in <module>
import pyarrow_hotfix # noqa: F401 # to fix vulnerability on pyarrow<14.0.1
^^^^^^^^^^^^^^^^^^^^^
ModuleNotFoundError: No module named 'pyarrow_hotfix'
```
|
2023-11-15T17:15:33Z
|
https://github.com/huggingface/datasets/pull/6423
|
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| true
|
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