id
int64 599M
3.26B
| number
int64 1
7.7k
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stringlengths 1
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stringlengths 0
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⌀ | state
stringclasses 2
values | html_url
stringlengths 46
51
| created_at
timestamp[s]date 2020-04-14 10:18:02
2025-07-23 08:04:53
| updated_at
timestamp[s]date 2020-04-27 16:04:17
2025-07-23 18:53:44
| closed_at
timestamp[s]date 2020-04-14 12:01:40
2025-07-23 16:44:42
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classes | comments
listlengths 0
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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,746,249,161
| 5,932
|
[doc build] Use secrets
|
Companion pr to https://github.com/huggingface/doc-builder/pull/379
|
closed
|
https://github.com/huggingface/datasets/pull/5932
| 2023-06-07T16:09:39
| 2023-06-09T10:16:58
| 2023-06-09T09:53:16
|
{
"login": "mishig25",
"id": 11827707,
"type": "User"
}
|
[] | true
|
[] |
1,745,408,784
| 5,931
|
`datasets.map` not reusing cached copy by default
|
### Describe the bug
When I load the dataset from local directory, it's cached copy is picked up after first time. However, for `map` operation, the operation is applied again and cached copy is not picked up. Is there any way to pick cached copy instead of processing it again? The only solution I could think of was to use `save_to_disk` after my last transform and then use that in my DataLoader pipeline. Are there any other solutions for the same?
One more thing, my dataset is occupying 6GB storage memory after I use `map`, is there any way I can reduce that memory usage?
### Steps to reproduce the bug
```
# make sure that dataset decodes audio with correct sampling rate
dataset_sampling_rate = next(iter(self.raw_datasets.values())).features["audio"].sampling_rate
if dataset_sampling_rate != self.feature_extractor.sampling_rate:
self.raw_datasets = self.raw_datasets.cast_column(
"audio", datasets.features.Audio(sampling_rate=self.feature_extractor.sampling_rate)
)
vectorized_datasets = self.raw_datasets.map(
self.prepare_dataset,
remove_columns=next(iter(self.raw_datasets.values())).column_names,
num_proc=self.num_workers,
desc="preprocess datasets",
)
# filter data that is longer than max_input_length
self.vectorized_datasets = vectorized_datasets.filter(
self.is_audio_in_length_range,
num_proc=self.num_workers,
input_columns=["input_length"],
)
def prepare_dataset(self, batch):
# load audio
sample = batch["audio"]
inputs = self.feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
batch["input_values"] = inputs.input_values[0]
batch["input_length"] = len(batch["input_values"])
batch["labels"] = self.tokenizer(batch["target_text"]).input_ids
return batch
```
### Expected behavior
`map` to use cached copy and if possible an alternative technique to reduce memory usage after using `map`
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-3.10.0-1160.71.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.16
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.2
|
closed
|
https://github.com/huggingface/datasets/issues/5931
| 2023-06-07T09:03:33
| 2023-06-21T16:15:40
| 2023-06-21T16:15:40
|
{
"login": "bhavitvyamalik",
"id": 19718818,
"type": "User"
}
|
[] | false
|
[] |
1,745,184,395
| 5,930
|
loading private custom dataset script - authentication error
|
### Describe the bug
Train model with my custom dataset stored in HuggingFace and loaded with the loading script requires authentication but I am not sure how ?
I am logged in in the terminal, in the browser. I receive this error:
/python3.8/site-packages/datasets/utils/file_utils.py", line 566, in get_from_cache
raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels `(ConnectionError('Unauthorized for URL `https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels. Please use the parameter `**`use_auth_token=True`**` after logging in with `**`huggingface-cli login`**`'))
when I added: `use_auth_token=True` and logged in via terminal then I received error:
or the same error in different format:
raise ConnectionError(f"`Couldn't reach {url} (error {response.status_code}`)")
ConnectionError: Couldn't reach https://huggingface.co/datasets/fkov/s/blob/main/data/s/train/labels (`error 401`)
### Steps to reproduce the bug
1. cloned transformers library locally:
https://huggingface.co/docs/transformers/v4.15.0/examples :
> git clone https://github.com/huggingface/transformers
> cd transformers
> pip install .
> cd /transformers/examples/pytorch/audio-classification
> pip install -r requirements.txt
2. created **loading script**
> https://huggingface.co/docs/datasets/dataset_script added next to dataset:
3. uploaded **private custom dataset** with loading script to HuggingFace
> https://huggingface.co/docs/datasets/dataset_script
4. added dataset loading script to **local directory** in the above cloned transformers library:
> cd /transformers/examples/pytorch/audio-classification
5. logged in to HuggingFace on local terminal with :
> **huggingface-cli login**
6. run the model with the custom dataset stored on HuggingFace with code: https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/README.md
cd /transformers/examples/pytorch/audio-classification
> python run_audio_classification.py \
> --model_name_or_path facebook/wav2vec2-base \
> --output_dir l/users/flck/outputs/wav2vec2-base-s \
> --overwrite_output_dir \
> --dataset_name s \
> --dataset_config_name s \
> --remove_unused_columns False \
> --do_train \
> --do_eval \
> --fp16 \
> --learning_rate 3e-5 \
> --max_length_seconds 1 \
> --attention_mask False \
> --warmup_ratio 0.1 \
> --num_train_epochs 5 \
> --per_device_train_batch_size 32 \
> --gradient_accumulation_steps 4 \
> --per_device_eval_batch_size 32 \
> --dataloader_num_workers 4 \
> --logging_strategy steps \
> --logging_steps 10 \
> --evaluation_strategy epoch \
> --save_strategy epoch \
> --load_best_model_at_end True \
> --metric_for_best_model accuracy \
> --save_total_limit 3 \
> --seed 0 \
> --push_to_hub \
> **--use_auth_token=True**
### Expected behavior
Be able to train a model the https://github.com/huggingface/transformers/blob/main/examples/pytorch/audio-classification/ run_audio_classification.py with private custom dataset stored on HuggingFace.
### Environment info
- datasets version: 2.12.0
- `transformers` version: 4.30.0.dev0
- Platform: Linux-5.4.204-ql-generic-12.0-19-x86_64-with-glibc2.17
- Python version: 3.8.12
- Huggingface_hub version: 0.15.1
- Safetensors version: 0.3.1
- PyTorch version (GPU?): 2.0.1+cu117 (True)
Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] torch==2.0.1
[pip3] torchaudio==2.0.2
[conda] numpy 1.24.3 pypi_0 pypi
[conda] torch 2.0.1 pypi_0 pypi
[conda] torchaudio 2.0.2 pypi_0 pypi
|
closed
|
https://github.com/huggingface/datasets/issues/5930
| 2023-06-07T06:58:23
| 2023-06-15T14:49:21
| 2023-06-15T14:49:20
|
{
"login": "flckv",
"id": 103381497,
"type": "User"
}
|
[] | false
|
[] |
1,744,478,456
| 5,929
|
Importing PyTorch reduces multiprocessing performance for map
|
### Describe the bug
I noticed that the performance of my dataset preprocessing with `map(...,num_proc=32)` decreases when PyTorch is imported.
### Steps to reproduce the bug
I created two example scripts to reproduce this behavior:
```
import datasets
datasets.disable_caching()
from datasets import Dataset
import time
PROC=32
if __name__ == "__main__":
dataset = [True] * 10000000
dataset = Dataset.from_dict({'train': dataset})
start = time.time()
dataset.map(lambda x: x, num_proc=PROC)
end = time.time()
print(end - start)
```
Takes around 4 seconds on my machine.
While the same code, but with an `import torch`:
```
import datasets
datasets.disable_caching()
from datasets import Dataset
import time
import torch
PROC=32
if __name__ == "__main__":
dataset = [True] * 10000000
dataset = Dataset.from_dict({'train': dataset})
start = time.time()
dataset.map(lambda x: x, num_proc=PROC)
end = time.time()
print(end - start)
```
takes around 22 seconds.
### Expected behavior
I would expect that the import of torch to not have such a significant effect on the performance of map using multiprocessing.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.35
- Python version: 3.11.3
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.2
- torch: 2.0.1
|
closed
|
https://github.com/huggingface/datasets/issues/5929
| 2023-06-06T19:42:25
| 2023-06-16T13:09:12
| 2023-06-16T13:09:12
|
{
"login": "Maxscha",
"id": 12814709,
"type": "User"
}
|
[] | false
|
[] |
1,744,098,371
| 5,928
|
Fix link to quickstart docs in README.md
| null |
closed
|
https://github.com/huggingface/datasets/pull/5928
| 2023-06-06T15:23:01
| 2023-06-06T15:52:34
| 2023-06-06T15:43:53
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,744,009,032
| 5,927
|
`IndexError` when indexing `Sequence` of `Array2D` with `None` values
|
### Describe the bug
Having `None` values in a `Sequence` of `ArrayND` fails.
### Steps to reproduce the bug
```python
from datasets import Array2D, Dataset, Features, Sequence
data = [
[
[[0]],
None,
None,
]
]
feature = Sequence(Array2D((1, 1), dtype="int64"))
dataset = Dataset.from_dict({"a": data}, features=Features({"a": feature}))
dataset[0] # error raised only when indexing
```
```
Traceback (most recent call last):
File "/Users/quentingallouedec/gia/c.py", line 13, in <module>
dataset[0] # error raised only when indexing
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2658, in __getitem__
return self._getitem(key)
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2643, in _getitem
formatted_output = format_table(
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 634, in format_table
return formatter(pa_table, query_type=query_type)
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 406, in __call__
return self.format_row(pa_table)
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 441, in format_row
row = self.python_arrow_extractor().extract_row(pa_table)
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 144, in extract_row
return _unnest(pa_table.to_pydict())
File "pyarrow/table.pxi", line 4146, in pyarrow.lib.Table.to_pydict
File "pyarrow/table.pxi", line 1312, in pyarrow.lib.ChunkedArray.to_pylist
File "pyarrow/array.pxi", line 1521, in pyarrow.lib.Array.to_pylist
File "pyarrow/scalar.pxi", line 675, in pyarrow.lib.ListScalar.as_py
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/features/features.py", line 760, in to_pylist
return self.to_numpy(zero_copy_only=zero_copy_only).tolist()
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/datasets/features/features.py", line 725, in to_numpy
numpy_arr = np.insert(numpy_arr.astype(np.float64), null_indices, np.nan, axis=0)
File "<__array_function__ internals>", line 200, in insert
File "/Users/quentingallouedec/gia/env/lib/python3.10/site-packages/numpy/lib/function_base.py", line 5426, in insert
old_mask[indices] = False
IndexError: index 3 is out of bounds for axis 0 with size 3
```
AFAIK, the problem only occurs when you use a `Sequence` of `ArrayND`.
I strongly suspect that the problem comes from this line, or `np.insert` is misused:
https://github.com/huggingface/datasets/blob/02ee418831aba68d0be93227bce8b3f42ef8980f/src/datasets/features/features.py#L729
To put t simply, you want something that do that:
```python
import numpy as np
numpy_arr = np.zeros((1, 1, 1))
null_indices = np.array([1, 2])
np.insert(numpy_arr, null_indices, np.nan, axis=0)
# raise an error, instead of outputting
# array([[[ 0.]],
# [[nan]],
# [[nan]]])
```
### Expected behavior
The previous code should not raise an error.
### Environment info
- Python 3.10.11
- datasets 2.10.0
- pyarrow 12.0.0
|
closed
|
https://github.com/huggingface/datasets/issues/5927
| 2023-06-06T14:36:22
| 2023-06-13T12:39:39
| 2023-06-09T13:23:50
|
{
"login": "qgallouedec",
"id": 45557362,
"type": "User"
}
|
[] | false
|
[] |
1,743,922,028
| 5,926
|
Uncaught exception when generating the splits from a dataset that miss data
|
### Describe the bug
Dataset https://huggingface.co/datasets/blog_authorship_corpus has an issue with its hosting platform, since https://drive.google.com/u/0/uc?id=1cGy4RNDV87ZHEXbiozABr9gsSrZpPaPz&export=download returns 404 error.
But when trying to generate the split names, we get an exception which is now correctly caught.
Seen originally in https://github.com/huggingface/datasets-server/blob/adbdcd6710ffed4e2eb2e4cd905b5e0dff530a15/services/worker/src/worker/job_runners/config/parquet_and_info.py#L435
### Steps to reproduce the bug
```python
>>> from datasets import StreamingDownloadManager, load_dataset_builder
>>> builder = load_dataset_builder(path="blog_authorship_corpus")
Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.60k/5.60k [00:00<00:00, 23.1MB/s]
Downloading metadata: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.81k/2.81k [00:00<00:00, 14.7MB/s]
Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.30k/7.30k [00:00<00:00, 30.8MB/s]
>>> dl_manager = StreamingDownloadManager(base_path=builder.base_path)
>>> builder._split_generators(dl_manager)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/blog_authorship_corpus/6f5d78241afd8313111956f877a57db7a0e9fc6718255dc85df0928197feb683/blog_authorship_corpus.py", line 79, in _split_generators
data = dl_manager.download_and_extract(_DATA_URL)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1087, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1039, in extract
urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 435, in map_nested
return function(data_struct)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1044, in _extract
protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 433, in _get_extraction_protocol
with fsspec.open(urlpath, **kwargs) as f:
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 439, in open
return open_files(
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 194, in __getitem__
out = super().__getitem__(item)
IndexError: list index out of range
```
### Expected behavior
We should have an Exception raised by the datasets library.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.19.0-1026-aws-x86_64-with-glibc2.35
- Python version: 3.9.15
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.2
|
open
|
https://github.com/huggingface/datasets/issues/5926
| 2023-06-06T13:51:01
| 2023-06-07T07:53:16
| null |
{
"login": "severo",
"id": 1676121,
"type": "User"
}
|
[] | false
|
[] |
1,741,941,436
| 5,925
|
Breaking API change in datasets.list_datasets caused by change in HfApi.list_datasets
|
### Describe the bug
Hi all,
after an update of the `datasets` library, we observer crashes in our code. We relied on `datasets.list_datasets` returning a `list`. Now, after the API of the HfApi.list_datasets was changed and it returns a `list` instead of an `Iterable`, the `datasets.list_datasets` now sometimes returns a `list` and somesimes an `Iterable`.
It would be helpful to indicate that by the return type of the `datasets.list_datasets` function.
Thanks,
Martin
### Steps to reproduce the bug
Here, the code crashed after we updated the `datasets` library:
```python
# list_datasets no longer returns a list, which leads to an error when one tries to slice it
for datasets.list_datasets(with_details=True)[:limit]:
...
```
### Expected behavior
It would be helpful to indicate that by the return type of the `datasets.list_datasets` function.
### Environment info
Ubuntu 22.04
datasets 2.12.0
|
closed
|
https://github.com/huggingface/datasets/issues/5925
| 2023-06-05T14:46:04
| 2023-06-19T17:22:43
| 2023-06-19T17:22:43
|
{
"login": "mtkinit",
"id": 78868366,
"type": "User"
}
|
[] | false
|
[] |
1,738,889,236
| 5,924
|
Add parallel module using joblib for Spark
|
Discussion in https://github.com/huggingface/datasets/issues/5798
|
closed
|
https://github.com/huggingface/datasets/pull/5924
| 2023-06-02T22:25:25
| 2023-06-14T10:25:10
| 2023-06-14T10:15:46
|
{
"login": "es94129",
"id": 12763339,
"type": "User"
}
|
[] | true
|
[] |
1,737,436,227
| 5,923
|
Cannot import datasets - ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility
|
### Describe the bug
When trying to import datasets, I get a pyarrow ValueError:
Traceback (most recent call last):
File "/Users/edward/test/test.py", line 1, in <module>
import datasets
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/__init__.py", line 43, in <module>
from .arrow_dataset import Dataset
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 65, in <module>
from .arrow_reader import ArrowReader
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/datasets/arrow_reader.py", line 28, in <module>
import pyarrow.parquet as pq
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/parquet/__init__.py", line 20, in <module>
from .core import *
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 45, in <module>
from pyarrow.fs import (LocalFileSystem, FileSystem, FileType,
File "/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/fs.py", line 49, in <module>
from pyarrow._gcsfs import GcsFileSystem # noqa
File "pyarrow/_gcsfs.pyx", line 1, in init pyarrow._gcsfs
ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject
### Steps to reproduce the bug
`import datasets`
### Expected behavior
Successful import
### Environment info
Conda environment, MacOS
python 3.9.12
datasets 2.12.0
|
closed
|
https://github.com/huggingface/datasets/issues/5923
| 2023-06-02T04:16:32
| 2024-06-27T10:07:49
| 2024-02-25T16:38:03
|
{
"login": "ehuangc",
"id": 71412682,
"type": "User"
}
|
[] | false
|
[] |
1,736,898,953
| 5,922
|
Length of table does not accurately reflect the split
|
### Describe the bug
I load a Huggingface Dataset and do `train_test_split`. I'm expecting the underlying table for the dataset to also be split, but it's not.
### Steps to reproduce the bug
### Expected behavior
The expected behavior is when `len(hf_dataset["train"].data)` should match the length of the train split, and not be the entire unsplit dataset.
### Environment info
datasets 2.10.1
python 3.10.11
|
closed
|
https://github.com/huggingface/datasets/issues/5922
| 2023-06-01T18:56:26
| 2023-06-02T16:13:31
| 2023-06-02T16:13:31
|
{
"login": "amogkam",
"id": 8068268,
"type": "User"
}
|
[
{
"name": "wontfix",
"color": "ffffff"
}
] | false
|
[] |
1,736,563,023
| 5,921
|
Fix streaming parquet with image feature in schema
|
It was not reading the feature type from the parquet arrow schema
|
closed
|
https://github.com/huggingface/datasets/pull/5921
| 2023-06-01T15:23:10
| 2023-06-02T10:02:54
| 2023-06-02T09:53:11
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,736,196,991
| 5,920
|
Optimize IterableDataset.from_file using ArrowExamplesIterable
|
following https://github.com/huggingface/datasets/pull/5893
|
closed
|
https://github.com/huggingface/datasets/pull/5920
| 2023-06-01T12:14:36
| 2023-06-01T12:42:10
| 2023-06-01T12:35:14
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,735,519,227
| 5,919
|
add support for storage_options for load_dataset API
|
to solve the issue in #5880
1. add s3 support in the link check step, previous we only check `http` and `https`,
2. change the parameter of `use_auth_token` to `download_config` to support both `storage_options` and `use_auth_token` parameter when trying to handle(list, open, read, etc,.) the remote files.
3. integrate the check part's duplicate code to make adding or deleting other sources easier.
|
closed
|
https://github.com/huggingface/datasets/pull/5919
| 2023-06-01T05:52:32
| 2023-07-18T06:14:32
| 2023-07-17T17:02:00
|
{
"login": "janineguo",
"id": 59083384,
"type": "User"
}
|
[] | true
|
[] |
1,735,313,549
| 5,918
|
File not found for audio dataset
|
### Describe the bug
After loading an audio dataset, and looking at a sample entry, the `path` element, which is supposed to be the path to the audio file, doesn't actually exist.
### Steps to reproduce the bug
Run bug.py:
```py
import os.path
from datasets import load_dataset
def run() -> None:
cv13 = load_dataset(
"mozilla-foundation/common_voice_13_0",
"hi",
split="train",
)
print(cv13[0])
audio_file = cv13[0]["path"]
if not os.path.exists(audio_file):
raise ValueError(f'File {audio_file} does not exist.')
if __name__ == "__main__":
run()
```
The result (on my machine):
```json
{'client_id': '0f018a99663f33afbb7d38aee281fb1afcfd07f9e7acd00383f604e1e17c38d6ed8adf1bd2ccbf927a52c5adefb8ac4b158ce27a7c2ed9581e71202eb302dfb3', 'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'audio': {'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'array': array([ 6.46234854e-26, -1.35709319e-25, -8.07793567e-26, ...,
1.06425944e-07, 4.46417090e-08, 2.61451660e-09]), 'sampling_rate': 48000}, 'sentence': 'हमने उसका जन्मदिन मनाया।', 'up_votes': 2, 'down_votes': 0, 'age': '', 'gender': '', 'accent': '', 'locale': 'hi', 'segment': '' ', 'variant': ''}
```
```txt
Traceback (most recent call last):
File "F:\eo-reco\bug.py", line 18, in <module>
run()
File "F:\eo-reco\bug.py", line 15, in run
raise ValueError(f'File {audio_file} does not exist.')
ValueError: File C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\common_voice_hi_26008353.mp3 does not exist.
```
### Expected behavior
The `path` element points to the correct file, which happens to be:
```
C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\hi_train_0\common_voice_hi_26008353.mp3
```
That is, there's an extra directory `hi_train_0` that is not in the `path` element.
### Environment info
- `datasets` version: 2.12.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.11.3
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
-
|
open
|
https://github.com/huggingface/datasets/issues/5918
| 2023-06-01T02:15:29
| 2023-06-11T06:02:25
| null |
{
"login": "RobertBaruch",
"id": 1783950,
"type": "User"
}
|
[] | false
|
[] |
1,733,661,588
| 5,917
|
Refactor extensions
|
Related to:
- #5850
|
closed
|
https://github.com/huggingface/datasets/pull/5917
| 2023-05-31T08:33:02
| 2023-05-31T13:34:35
| 2023-05-31T13:25:57
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,732,456,392
| 5,916
|
Unpin responses
|
Fix #5906
|
closed
|
https://github.com/huggingface/datasets/pull/5916
| 2023-05-30T14:59:48
| 2023-05-30T18:03:10
| 2023-05-30T17:53:29
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,732,389,984
| 5,915
|
Raise error in `DatasetBuilder.as_dataset` when `file_format` is not `"arrow"`
|
Raise an error in `DatasetBuilder.as_dataset` when `file_format != "arrow"` (and fix the docstring)
Fix #5874
|
closed
|
https://github.com/huggingface/datasets/pull/5915
| 2023-05-30T14:27:55
| 2023-05-31T13:31:21
| 2023-05-31T13:23:54
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,731,483,996
| 5,914
|
array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size in Datasets
|
### Describe the bug
When using the `filter` or `map` function to preprocess a dataset, a ValueError is encountered with the error message "array is too big; arr.size * arr.dtype.itemsize is larger than the maximum possible size."
Detailed error message:
Traceback (most recent call last):
File "data_processing.py", line 26, in <module>
processed_dataset[split] = samromur_children[split].map(prepare_dataset, cache_file_name=cache_dict[split],writer_batch_size = 50)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2405, in map
desc=desc,
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 524, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/fingerprint.py", line 480, in wrapper
out = func(self, *args, **kwargs)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2756, in _map_single
example = apply_function_on_filtered_inputs(example, i, offset=offset)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2655, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2347, in decorated
result = f(decorated_item, *args, **kwargs)
File "data_processing.py", line 11, in prepare_dataset
audio = batch["audio"]
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 123, in __getitem__
value = decode_nested_example(self.features[key], value) if value is not None else None
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/features.py", line 1260, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/audio.py", line 156, in decode_example
array, sampling_rate = self._decode_non_mp3_path_like(path, token_per_repo_id=token_per_repo_id)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/datasets/features/audio.py", line 257, in _decode_non_mp3_path_like
array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/librosa/core/audio.py", line 176, in load
y, sr_native = __soundfile_load(path, offset, duration, dtype)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/librosa/core/audio.py", line 222, in __soundfile_load
y = sf_desc.read(frames=frame_duration, dtype=dtype, always_2d=False).T
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/soundfile.py", line 891, in read
out = self._create_empty_array(frames, always_2d, dtype)
File "/projects/zhwa3087/software/anaconda/envs/mycustomenv/lib/python3.7/site-packages/soundfile.py", line 1323, in _create_empty_array
return np.empty(shape, dtype, order='C')
ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
### Steps to reproduce the bug
```python
from datasets import load_dataset, DatasetDict
from transformers import WhisperFeatureExtractor
from transformers import WhisperTokenizer
samromur_children= load_dataset("language-and-voice-lab/samromur_children")
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="icelandic", task="transcribe")
def prepare_dataset(batch):
# load and resample audio data from 48 to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = feature_extractor(audio["array"], sampling_rate=16000).input_features[0]
# encode target text to label ids
batch["labels"] = tokenizer(batch["normalized_text"]).input_ids
return batch
cache_dict = {"train": "./cache/audio_train.cache", \
"validation": "./cache/audio_validation.cache", \
"test": "./cache/audio_test.cache"}
filter_cache_dict = {"train": "./cache/filter_train.arrow", \
"validation": "./cache/filter_validation.arrow", \
"test": "./cache/filter_test.arrow"}
print("before filtering")
print(samromur_children)
#filter the dataset to only include examples with more than 2 seconds of audio
samromur_children = samromur_children.filter(lambda example: example["audio"]["array"].shape[0] > 16000*2, cache_file_names=filter_cache_dict)
print("after filtering")
print(samromur_children)
processed_dataset = DatasetDict()
# processed_dataset = samromur_children.map(prepare_dataset, cache_file_names=cache_dict, num_proc=10,)
for split in ["train", "validation", "test"]:
processed_dataset[split] = samromur_children[split].map(prepare_dataset, cache_file_name=cache_dict[split])
```
### Expected behavior
The dataset is successfully processed and ready to train the model.
### Environment info
Python version: 3.7.13
datasets package version: 2.4.0
librosa package version: 0.10.0.post2
|
open
|
https://github.com/huggingface/datasets/issues/5914
| 2023-05-30T04:25:00
| 2024-10-27T04:09:18
| null |
{
"login": "ravenouse",
"id": 85110830,
"type": "User"
}
|
[] | false
|
[] |
1,731,427,484
| 5,913
|
I tried to load a custom dataset using the following statement: dataset = load_dataset('json', data_files=data_files). The dataset contains 50 million text-image pairs, but an error occurred.
|
### Describe the bug
File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
Downloading and preparing dataset json/default to /home/kas/diffusers/examples/dreambooth/cache_data/datasets/json/default-acf423d8c6ef99d0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data files: 100%|██████████| 1/1 [00:00<00:00, 84.35it/s]
Extracting data files: 0%| | 0/1 [00:00<?, ?it/s] for _, table in generator:
File "/home/kas/.conda/envs/diffusers/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 114, in _generate_tables
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
File "pyarrow/_json.pyx", line 258, in pyarrow._json.read_json
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 27.72it/s]
Generating train split: 0 examples [00:00, ? examples/s] File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 125, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2390448764
### Steps to reproduce the bug
1、data_files = ["1.json", "2.json", "3.json"]
2、dataset = load_dataset('json', data_files=data_files)
### Expected behavior
Read the dataset normally.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-4.15.0-29-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 1.3.5
|
closed
|
https://github.com/huggingface/datasets/issues/5913
| 2023-05-30T02:55:26
| 2023-07-24T12:00:38
| 2023-07-24T12:00:38
|
{
"login": "cjt222",
"id": 17508662,
"type": "User"
}
|
[] | false
|
[] |
1,730,299,852
| 5,912
|
Missing elements in `map` a batched dataset
|
### Describe the bug
As outlined [here](https://discuss.huggingface.co/t/length-error-using-map-with-datasets/40969/3?u=sachin), the following collate function drops 5 out of possible 6 elements in the batch (it is 6 because out of the eight, two are bad links in laion). A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
The weirdest part is when inspecting the sizes of the tensors as shown below, both `tokenized_captions["input_ids"]` and `image_features` show the correct shapes. Simply the output only has one element (with the batch dimension squeezed out).
```python
class CollateFn:
def get_image(self, url):
try:
response = requests.get(url)
return Image.open(io.BytesIO(response.content)).convert("RGB")
except PIL.UnidentifiedImageError:
logger.info(f"Reading error: Could not transform f{url}")
return None
except requests.exceptions.ConnectionError:
logger.info(f"Connection error: Could not transform f{url}")
return None
def __call__(self, batch):
images = [self.get_image(url) for url in batch["url"]]
captions = [caption for caption, image in zip(batch["caption"], images) if image is not None]
images = [image for image in images if image is not None]
tokenized_captions = tokenizer(
captions,
padding="max_length",
truncation=True,
max_length=tokenizer.model_max_length,
return_tensors="pt",
)
image_features = torch.stack([torch.Tensor(feature_extractor(image)["pixel_values"][0]) for image in images])
# import pdb; pdb.set_trace()
return {"input_ids": tokenized_captions["input_ids"], "images": image_features}
collate_fn = CollateFn()
laion_ds = datasets.load_dataset("laion/laion400m", split="train", streaming=True)
laion_ds_batched = laion_ds.map(collate_fn, batched=True, batch_size=8, remove_columns=next(iter(laion_ds)).keys())
```
### Steps to reproduce the bug
A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
### Expected behavior
Would expect `next(iter(laion_ds_batched))` to produce two tensors of shape `(batch_size, 77)` and `batch_size, image_shape`.
### Environment info
datasets==2.12.0
python==3.10
|
closed
|
https://github.com/huggingface/datasets/issues/5912
| 2023-05-29T08:09:19
| 2023-07-26T15:48:15
| 2023-07-26T15:48:15
|
{
"login": "sachinruk",
"id": 1410927,
"type": "User"
}
|
[] | false
|
[] |
1,728,909,790
| 5,910
|
Cannot use both set_format and set_transform
|
### Describe the bug
I need to process some data using the set_transform method but I also need the data to be formatted for pytorch before processing it.
I don't see anywhere in the documentation something that says that both methods cannot be used at the same time.
### Steps to reproduce the bug
```
from datasets import load_dataset
ds = load_dataset("mnist", split="train")
ds.set_format(type="torch")
def transform(entry):
return entry["image"].double()
ds.set_transform(transform)
print(ds[0])
```
### Expected behavior
It should print the pytorch tensor image as a double, but it errors because "entry" in the transform function doesn't receive a pytorch tensor to begin with, it receives a PIL Image -> entry.double() errors because entry isn't a pytorch tensor.
### Environment info
Latest versions.
### Note:
It would be at least handy to have access to a function that can do the dataset.set_format in the set_transform function.
Something like:
```
from datasets import load_dataset, do_format
ds = load_dataset("mnist", split="train")
def transform(entry):
entry = do_format(entry, type="torch")
return entry["image"].double()
ds.set_transform(transform)
print(ds[0])
```
|
closed
|
https://github.com/huggingface/datasets/issues/5910
| 2023-05-27T19:22:23
| 2023-07-09T21:40:54
| 2023-06-16T14:41:24
|
{
"login": "ybouane",
"id": 14046002,
"type": "User"
}
|
[] | false
|
[] |
1,728,900,068
| 5,909
|
Use more efficient and idiomatic way to construct list.
|
Using `*` is ~2X faster according to [benchmark](https://colab.research.google.com/gist/ttsugriy/c964a2604edf70c41911b10335729b6a/for-vs-mult.ipynb) with just 4 patterns. This doesn't matter much since this tiny difference is not going to be noticeable, but why not?
|
closed
|
https://github.com/huggingface/datasets/pull/5909
| 2023-05-27T18:54:47
| 2023-05-31T15:37:11
| 2023-05-31T13:28:29
|
{
"login": "ttsugriy",
"id": 172294,
"type": "User"
}
|
[] | true
|
[] |
1,728,653,935
| 5,908
|
Unbearably slow sorting on big mapped datasets
|
### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lines at all, with flattening takes about a minute).
### Steps to reproduce the bug
```Python
from datasets import load_dataset
import time
dataset = load_dataset("xnli", "en", split="train")
dataset = dataset.shard(10, 0)
print(len(dataset))
t = time.time()
# dataset = dataset.flatten_indices() # uncomment this line and it's fast
dataset = dataset.sort("label", reverse=True, load_from_cache_file=False)
print(f"finished in {time.time() - t:.4f} seconds")
```
### Expected behavior
Expect sorting to take the same or less time than flattening and then sorting.
### Environment info
- `datasets` version: 2.12.1.dev0 (same with 2.12.0 too)
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.10.10
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
|
open
|
https://github.com/huggingface/datasets/issues/5908
| 2023-05-27T11:08:32
| 2023-06-13T17:45:10
| null |
{
"login": "maximxlss",
"id": 29152154,
"type": "User"
}
|
[] | false
|
[] |
1,728,648,560
| 5,907
|
Add `flatten_indices` to `DatasetDict`
|
Add `flatten_indices` to `DatasetDict` for convinience
|
closed
|
https://github.com/huggingface/datasets/pull/5907
| 2023-05-27T10:55:44
| 2023-06-01T11:46:35
| 2023-06-01T11:39:36
|
{
"login": "maximxlss",
"id": 29152154,
"type": "User"
}
|
[] | true
|
[] |
1,728,171,113
| 5,906
|
Could you unpin responses version?
|
### Describe the bug
Could you unpin [this](https://github.com/huggingface/datasets/blob/main/setup.py#L139) or move it to test requirements? This is a testing library and we also use it for our tests as well. We do not want to use a very outdated version.
### Steps to reproduce the bug
could not install this library due to dependency conflict.
### Expected behavior
can install datasets
### Environment info
linux 64
|
closed
|
https://github.com/huggingface/datasets/issues/5906
| 2023-05-26T20:02:14
| 2023-05-30T17:53:31
| 2023-05-30T17:53:31
|
{
"login": "kenimou",
"id": 47789026,
"type": "User"
}
|
[] | false
|
[] |
1,727,541,392
| 5,905
|
Offer an alternative to Iterable Dataset that allows lazy loading and processing while skipping batches efficiently
|
### Feature request
I would like a way to resume training from a checkpoint without waiting for a very long time when using an iterable dataset.
### Motivation
I am training models on the speech-recognition task. I have very large datasets that I can't comfortably store on a disk and also quite computationally intensive audio processing to do. As a result I want to load data from my remote when it is needed and perform all processing on the fly.
I am currently using the iterable dataset feature of _datasets_. It does everything I need with one exception. My issue is that when resuming training at a step n, we have to download all the data and perform the processing of steps < n, just to get the iterable at the right step. In my case it takes almost as long as training for the same steps, which make resuming training from a checkpoint useless in practice.
I understand that the nature of iterators make it probably nearly impossible to quickly resume training.
I thought about a possible solution nonetheless :
I could in fact index my large dataset and make it a mapped dataset. Then I could use set_transform to perform the processing on the fly. Finally, if I'm not mistaken, the _accelerate_ package allows to [skip steps efficiently](https://github.com/huggingface/accelerate/blob/a73898027a211c3f6dc4460351b0ec246aa824aa/src/accelerate/data_loader.py#L827) for a mapped dataset.
Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there.
If not, I could do it using a plain _Pytorch_ dataset. Then I would need to convert it to a _datasets_' dataset to get all the features of _datasets_. Is it something possible ?
### Your contribution
I could provide a PR to allow lazy loading of mapped dataset or the conversion of a mapped _Pytorch_ dataset into a _Datasets_ dataset if you think it is an useful new feature.
|
open
|
https://github.com/huggingface/datasets/issues/5905
| 2023-05-26T12:33:02
| 2023-06-15T13:34:18
| null |
{
"login": "bruno-hays",
"id": 48770768,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,727,415,626
| 5,904
|
Validate name parameter in make_file_instructions
|
Validate `name` parameter in `make_file_instructions`.
This way users get more informative error messages, instead of:
```stacktrace
.../huggingface/datasets/src/datasets/arrow_reader.py in make_file_instructions(name, split_infos, instruction, filetype_suffix, prefix_path)
110 name2len = {info.name: info.num_examples for info in split_infos}
111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos}
--> 112 name2filenames = {
113 info.name: filenames_for_dataset_split(
114 path=prefix_path,
.../huggingface/datasets/src/datasets/arrow_reader.py in <dictcomp>(.0)
111 name2shard_lengths = {info.name: info.shard_lengths for info in split_infos}
112 name2filenames = {
--> 113 info.name: filenames_for_dataset_split(
114 path=prefix_path,
115 dataset_name=name,
.../huggingface/datasets/src/datasets/naming.py in filenames_for_dataset_split(path, dataset_name, split, filetype_suffix, shard_lengths)
68
69 def filenames_for_dataset_split(path, dataset_name, split, filetype_suffix=None, shard_lengths=None):
---> 70 prefix = filename_prefix_for_split(dataset_name, split)
71 prefix = os.path.join(path, prefix)
72
.../huggingface/datasets/src/datasets/naming.py in filename_prefix_for_split(name, split)
52
53 def filename_prefix_for_split(name, split):
---> 54 if os.path.basename(name) != name:
55 raise ValueError(f"Should be a dataset name, not a path: {name}")
56 if not re.match(_split_re, split):
.../lib/python3.9/posixpath.py in basename(p)
140 def basename(p):
141 """Returns the final component of a pathname"""
--> 142 p = os.fspath(p)
143 sep = _get_sep(p)
144 i = p.rfind(sep) + 1
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
Related to #5895.
|
closed
|
https://github.com/huggingface/datasets/pull/5904
| 2023-05-26T11:12:46
| 2023-05-31T07:43:32
| 2023-05-31T07:34:57
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,727,372,549
| 5,903
|
Relax `ci.yml` trigger for `pull_request` based on modified paths
|
## What's in this PR?
As of a previous PR at #5902, I've seen that the CI was automatically trigger on any file, in that case when modifying a Jupyter Notebook (.ipynb), which IMO could be skipped, as the modification on the Jupyter Notebook has no effect/impact on the `ci.yml` outcome. So this PR controls the paths that trigger the `ci.yml` to avoid wasting resources when not needed.
## What's pending in this PR?
I would like to confirm whether this should affect both `push` and `pull_request`, since just modifications in those files won't change the `ci.yml` outcome, so maybe it's worth skipping it too in the `push` trigger.
|
open
|
https://github.com/huggingface/datasets/pull/5903
| 2023-05-26T10:46:52
| 2023-09-07T15:52:36
| null |
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,727,342,194
| 5,902
|
Fix `Overview.ipynb` & detach Jupyter Notebooks from `datasets` repository
|
## What's in this PR?
This PR solves #5887 since there was a mismatch between the tokenizer and the model used, since the tokenizer was `bert-base-cased` while the model was `distilbert-base-case` both for the PyTorch and TensorFlow alternatives. Since DistilBERT doesn't use/need the `token_type_ids`, the `**batch` was failing, as the batch contained `input_ids`, `attention_mask`, `token_type_ids`, `start_positions` and `end_positions`, and `token_type_ids` was not required.
Besides that, at the end `seqeval` was being used to evaluate the model predictions, and just `evaluate` was being installed, so I've also included the `seqeval` installation.
Finally, I've re-run everything in Google Colab, and every cell was successfully executed!
## What was done on top of the original PR?
Based on the comments from @mariosasko and @stevhliu, I've updated the contents of this PR to also review the `quickstart.mdx` and update what was needed, besides that, we may eventually move the `Overview.ipynb` dataset to `huggingface/notebooks` following @stevhliu suggestions.
|
closed
|
https://github.com/huggingface/datasets/pull/5902
| 2023-05-26T10:25:01
| 2023-07-25T13:50:06
| 2023-07-25T13:38:33
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,727,179,016
| 5,901
|
Make prepare_split more robust if errors in metadata dataset_info splits
|
This PR uses `split_generator.split_info` as default value for `split_info` if any exception is raised while trying to get `split_generator.name` from `self.info.splits` (this may happen if there is any error in the metadata dataset_info splits).
Please note that `split_info` is only used by the logger.
Fix #5895 if passed `verification_mode="no_checks"`:
```python
ds = load_dataset(
"ArmelR/stack-exchange-instruction",
data_dir="data/finetune",
split="train",
verification_mode="no_checks",
revision="c609f1caade5cfbf3b9fe9cfa17d7cb000b457bd",
)
```
|
closed
|
https://github.com/huggingface/datasets/pull/5901
| 2023-05-26T08:48:22
| 2023-06-02T06:06:38
| 2023-06-01T13:39:40
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,727,129,617
| 5,900
|
Fix minor typo in docs loading.mdx
|
Minor fix.
|
closed
|
https://github.com/huggingface/datasets/pull/5900
| 2023-05-26T08:10:54
| 2023-05-26T09:34:15
| 2023-05-26T09:25:12
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,726,279,011
| 5,899
|
canonicalize data dir in config ID hash
|
fixes #5871
The second commit is optional but improves readability.
|
closed
|
https://github.com/huggingface/datasets/pull/5899
| 2023-05-25T18:17:10
| 2023-06-02T16:02:15
| 2023-06-02T15:52:04
|
{
"login": "kylrth",
"id": 5044802,
"type": "User"
}
|
[] | true
|
[] |
1,726,190,481
| 5,898
|
Loading The flores data set for specific language
|
### Describe the bug
I am trying to load the Flores data set
the code which is given is
```
from datasets import load_dataset
dataset = load_dataset("facebook/flores")
```
This gives the error of config name
""ValueError: Config name is missing"
Now if I add some config it gives me the some error
"HFValidationError: Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: 'facebook/flores, 'ace_Arab''.
"
How I can load the data of the specific language ?
Couldn't find any tutorial
any one can help me out?
### Steps to reproduce the bug
step one load the data set
`from datasets import load_dataset
dataset = load_dataset("facebook/flores")`
it gives the error of config
once config is given
it gives the error of
"HFValidationError: Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: 'facebook/flores, 'ace_Arab''.
"
### Expected behavior
Data set should be loaded but I am receiving error
### Environment info
Datasets , python ,
|
closed
|
https://github.com/huggingface/datasets/issues/5898
| 2023-05-25T17:08:55
| 2023-05-25T17:21:38
| 2023-05-25T17:21:37
|
{
"login": "106AbdulBasit",
"id": 36159918,
"type": "User"
}
|
[] | false
|
[] |
1,726,135,494
| 5,897
|
Fix `FixedSizeListArray` casting
|
Fix cast on sliced `FixedSizeListArray`s.
Fix #5866
|
closed
|
https://github.com/huggingface/datasets/pull/5897
| 2023-05-25T16:26:33
| 2023-05-26T12:22:04
| 2023-05-26T11:57:16
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,726,022,500
| 5,896
|
HuggingFace does not cache downloaded files aggressively/early enough
|
### Describe the bug
I wrote the following script:
```
import datasets
dataset = datasets.load.load_dataset("wikipedia", "20220301.en", split="train[:10000]")
```
I ran it and spent 90 minutes downloading a 20GB file. Then I saw:
```
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20.3G/20.3G [1:30:29<00:00, 3.73MB/s]
Traceback (most recent call last):
File "/home/jack/Code/Projects/Transformers/Codebase/main.py", line 5, in <module>
dataset = datasets.load.load_dataset("wikipedia", "20220301.en", split="train[:10000]")
File "/home/jack/.local/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset
builder_instance.download_and_prepare(
File "/home/jack/.local/lib/python3.10/site-packages/datasets/builder.py", line 883, in download_and_prepare
self._save_info()
File "/home/jack/.local/lib/python3.10/site-packages/datasets/builder.py", line 2037, in _save_info
import apache_beam as beam
ModuleNotFoundError: No module named 'apache_beam'
```
And the 20GB of data was seemingly instantly gone forever, because when I ran the script again, it had to do the download again.
### Steps to reproduce the bug
See above
### Expected behavior
See above
### Environment info
datasets 2.10.1
Python 3.10
|
closed
|
https://github.com/huggingface/datasets/issues/5896
| 2023-05-25T15:14:36
| 2024-03-15T15:36:07
| 2024-03-15T15:36:07
|
{
"login": "jack-jjm",
"id": 2124157,
"type": "User"
}
|
[] | false
|
[] |
1,725,467,252
| 5,895
|
The dir name and split strings are confused when loading ArmelR/stack-exchange-instruction dataset
|
### Describe the bug
When I load the ArmelR/stack-exchange-instruction dataset, I encounter a bug that may be raised by confusing the dir name string and the split string about the dataset.
When I use the script "datasets.load_dataset('ArmelR/stack-exchange-instruction', data_dir="data/finetune", split="train", use_auth_token=True)", it fails. But it succeeds when I add the "streaming = True" parameter.
The website of the dataset is https://huggingface.co/datasets/ArmelR/stack-exchange-instruction/ .
The traceback logs are as below:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/load.py", line 1797, in load_dataset
builder_instance.download_and_prepare(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 890, in download_and_prepare
self._download_and_prepare(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 985, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 1706, in _prepare_split
split_info = self.info.splits[split_generator.name]
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/splits.py", line 530, in __getitem__
instructions = make_file_instructions(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/arrow_reader.py", line 112, in make_file_instructions
name2filenames = {
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/arrow_reader.py", line 113, in <dictcomp>
info.name: filenames_for_dataset_split(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/naming.py", line 70, in filenames_for_dataset_split
prefix = filename_prefix_for_split(dataset_name, split)
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/naming.py", line 54, in filename_prefix_for_split
if os.path.basename(name) != name:
File "/home/xxx/miniconda3/envs/code/lib/python3.9/posixpath.py", line 142, in basename
p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
### Steps to reproduce the bug
1. import datasets library function: ```from datasets import load_dataset```
2. load dataset: ```ds=load_dataset('ArmelR/stack-exchange-instruction', data_dir="data/finetune", split="train", use_auth_token=True)```
### Expected behavior
The dataset can be loaded successfully without the streaming setting.
### Environment info
Linux,
python=3.9
datasets=2.12.0
|
closed
|
https://github.com/huggingface/datasets/issues/5895
| 2023-05-25T09:39:06
| 2023-05-29T02:32:12
| 2023-05-29T02:32:12
|
{
"login": "DongHande",
"id": 45357817,
"type": "User"
}
|
[] | false
|
[] |
1,724,774,910
| 5,894
|
Force overwrite existing filesystem protocol
|
Fix #5876
|
closed
|
https://github.com/huggingface/datasets/pull/5894
| 2023-05-24T21:41:53
| 2023-05-25T06:52:08
| 2023-05-25T06:42:33
|
{
"login": "baskrahmer",
"id": 24520725,
"type": "User"
}
|
[] | true
|
[] |
1,722,519,056
| 5,893
|
Load cached dataset as iterable
|
To be used to train models it allows to load an IterableDataset from the cached Arrow file.
See https://github.com/huggingface/datasets/issues/5481
|
closed
|
https://github.com/huggingface/datasets/pull/5893
| 2023-05-23T17:40:35
| 2023-06-01T11:58:24
| 2023-06-01T11:51:29
|
{
"login": "mariusz-jachimowicz-83",
"id": 10278877,
"type": "User"
}
|
[] | true
|
[] |
1,722,503,824
| 5,892
|
User access requests with manual review do not notify the dataset owner
|
### Describe the bug
When a user access requests are enabled, and new requests are set to Manual Review, the dataset owner should be notified of the pending requests. However, instead, currently nothing happens, and so the dataset request can go unanswered for quite some time until the owner happens to check that particular dataset's Settings pane.
### Steps to reproduce the bug
1. Enable a dataset's user access requests
2. Set to Manual Review
3. Ask another HF user to request access to the dataset
4. Dataset owner is not notified
### Expected behavior
The dataset owner should receive some kind of notification, perhaps in their HF site inbox, or by email, when a dataset access request is made and manual review is enabled.
### Environment info
n/a
|
closed
|
https://github.com/huggingface/datasets/issues/5892
| 2023-05-23T17:27:46
| 2023-07-21T13:55:37
| 2023-07-21T13:55:36
|
{
"login": "leondz",
"id": 121934,
"type": "User"
}
|
[] | false
|
[] |
1,722,384,135
| 5,891
|
Make split slicing consistent with list slicing
|
Fix #1774, fix #5875
|
closed
|
https://github.com/huggingface/datasets/pull/5891
| 2023-05-23T16:04:33
| 2024-01-31T16:00:26
| 2024-01-31T15:54:17
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,722,373,618
| 5,889
|
Token Alignment for input and output data over train and test batch/dataset.
|
`data`
> DatasetDict({
train: Dataset({
features: ['input', 'output'],
num_rows: 4500
})
test: Dataset({
features: ['input', 'output'],
num_rows: 500
})
})
**# input (in-correct sentence)**
`data['train'][0]['input']`
**>>** 'We are meet sunday 10am12pmET in Crown Heights Brooklyn New York'
**# output (correct sentence)**
`data['train'][0]['output']`
**>>** 'We meet Sundays 10am-12pmET in Crown Heights, Brooklyn, New York.'
**I Want to align the output tokens with input**
```
`# tokenize both inputs and targets
def tokenize_fn(batch):
# tokenize the input sequence first
# this populates input_ids, attention_mask, etc.
tokenized_inputs = tokenizer(
batch['input']
)
labels_batch = tokenizer.tokenize(batch['output']) # original targets
aligned_labels_batch = []
for i, labels in enumerate(labels_batch):
word_ids = tokenized_inputs[i].word_ids()
aligned_labels_batch.append(align_targets(labels, word_ids)) # align_targets is another user defined function which is been called here
# recall: the 'target' must be stored in key called 'labels'
tokenized_inputs['labels'] = aligned_labels_batch
return tokenized_inputs`
```
```
data.map(
tokenize_fn,
batched=True,
remove_columns=data['train'].column_names,
)
```
When this user defined function is mapped to every records of train and test batch am getting following error:
**1.** **raise DatasetTransformationNotAllowedError(
3457 "Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it."**
**2.** **TypeError: TextEncodeInput must be Union[TextInputSequence, Tuple[InputSequence, InputSequence]]**
|
open
|
https://github.com/huggingface/datasets/issues/5889
| 2023-05-23T15:58:55
| 2023-05-23T15:58:55
| null |
{
"login": "akesh1235",
"id": 125154243,
"type": "User"
}
|
[] | false
|
[] |
1,722,166,382
| 5,887
|
HuggingsFace dataset example give error
|
### Describe the bug
### Steps to reproduce the bug
Use link as reference document written https://colab.research.google.com/github/huggingface/datasets/blob/main/notebooks/Overview.ipynb#scrollTo=biqDH9vpvSVz
```python
# Now let's train our model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.train().to(device)
for i, batch in enumerate(dataloader):
batch.to(device)
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
model.zero_grad()
print(f'Step {i} - loss: {loss:.3}')
if i > 5:
break
```
Error
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-44-7040b885f382>](https://localhost:8080/#) in <cell line: 5>()
5 for i, batch in enumerate(dataloader):
6 batch.to(device)
----> 7 outputs = model(**batch)
8 loss = outputs.loss
9 loss.backward()
[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
TypeError: DistilBertForQuestionAnswering.forward() got an unexpected keyword argument 'token_type_ids'
```
https://github.com/huggingface/datasets/assets/1328316/5d8b1d61-9337-4d59-8423-4f37f834c156
### Expected behavior
Run success on Google Colab (free)
### Environment info
Windows 11 x64, Google Colab free (my Google Drive just empty about 200 MB, but I don't think it cause problem)
|
closed
|
https://github.com/huggingface/datasets/issues/5887
| 2023-05-23T14:09:05
| 2023-07-25T14:01:01
| 2023-07-25T14:01:00
|
{
"login": "donhuvy",
"id": 1328316,
"type": "User"
}
|
[] | false
|
[] |
1,721,070,225
| 5,886
|
Use work-stealing algorithm when parallel computing
|
### Feature request
when i used Dataset.map api to process data concurrently, i found that
it gets slower and slower as it gets closer to completion. Then i read the source code of arrow_dataset.py and found that it shard the dataset and use multiprocessing pool to execute each shard.It may cause the slowest task to drag out the entire program's execution time,especially when processing huge dataset.
### Motivation
using work-stealing algorithm instead of sharding and parallel computing to optimize performance.
### Your contribution
just an idea.
|
open
|
https://github.com/huggingface/datasets/issues/5886
| 2023-05-23T03:08:44
| 2023-05-24T15:30:09
| null |
{
"login": "1014661165",
"id": 46060451,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,720,954,440
| 5,885
|
Modify `is_remote_filesystem` to return True for FUSE-mounted paths
| null |
closed
|
https://github.com/huggingface/datasets/pull/5885
| 2023-05-23T01:04:54
| 2024-01-08T18:31:00
| 2024-01-08T18:31:00
|
{
"login": "maddiedawson",
"id": 106995444,
"type": "User"
}
|
[] | true
|
[] |
1,722,290,363
| 5,888
|
A way to upload and visualize .mp4 files (millions of them) as part of a dataset
|
**Is your feature request related to a problem? Please describe.**
I recently chose to use huggingface hub as the home for a large multi modal dataset I've been building. https://huggingface.co/datasets/Antreas/TALI
It combines images, text, audio and video. Now, I could very easily upload a dataset made via datasets.Dataset.from_generator, as long as it did not include video files. I found that including .mp4 files in the entries would not auto-upload those files.
Hence I tried to upload them myself. I quickly found out that uploading many small files is a very bad way to use git lfs, and that it would take ages, so, I resorted to using 7z to pack them all up. But then I had a new problem.
My dataset had a size of 1.9TB. Trying to upload such a large file with the default huggingface_hub API always resulted in time outs etc. So I decided to split the large files into chunks of 5GB each and reupload.
So, eventually it all worked out. But now the dataset can't be properly and natively used by the datasets API because of all the needed preprocessing -- and furthermore the hub is unable to visualize things.
**Describe the solution you'd like**
A native way to upload large datasets that include .mp4 or other video types.
**Describe alternatives you've considered**
Already explained earlier
**Additional context**
https://huggingface.co/datasets/Antreas/TALI
|
open
|
https://github.com/huggingface/datasets/issues/5888
| 2023-05-22T18:05:26
| 2023-06-23T03:37:16
| null |
{
"login": "AntreasAntoniou",
"id": 10792502,
"type": "User"
}
|
[] | false
|
[] |
1,719,548,172
| 5,884
|
`Dataset.to_tf_dataset` fails when strings cannot be encoded as `np.bytes_`
|
### Describe the bug
When loading any dataset that contains a column with strings that are not ASCII-compatible, looping over those records raises the following exception e.g. for `é` character `UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128)`.
### Steps to reproduce the bug
Running the following script will eventually fail, when reaching to the batch that contains non-ASCII compatible strings.
```python
from datasets import load_dataset
ds = load_dataset("imdb", split="train")
tfds = ds.to_tf_dataset(batch_size=16)
for batch in tfds:
print(batch)
>>> UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128)
```
### Expected behavior
The following script to run properly, making sure that the strings are either `numpy.unicode_` or `numpy.string` instead of `numpy.bytes_` since some characters are not ASCII compatible and that would lead to an issue when applying the `map`.
```python
from datasets import load_dataset
ds = load_dataset("imdb", split="train")
tfds = ds.to_tf_dataset(batch_size=16)
for batch in tfds:
print(batch)
```
### Environment info
- `datasets` version: 2.12.1.dev0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5884
| 2023-05-22T12:03:06
| 2023-06-09T16:04:56
| 2023-06-09T16:04:55
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | false
|
[] |
1,719,527,597
| 5,883
|
Fix string-encoding, make `batch_size` optional, and minor improvements in `Dataset.to_tf_dataset`
|
## What's in this PR?
This PR addresses some minor fixes and general improvements in the `to_tf_dataset` method of `datasets.Dataset`, to convert a 🤗HuggingFace Dataset as a TensorFlow Dataset.
The main bug solved in this PR comes with the string-encoding, since for safety purposes the internal conversion of `numpy.arrays` when `dtype` is unicode/string, is to convert it into `numpy.bytes`, more information in the docstring of https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#L210. That's triggered when using `tensorflow.numpy_function` as it's applying another type cast besides the one that `datasets` does, so the casting is applied at least twice per entry/batch. So this means that the definition of the `numpy.unicode_` dtype when the data in the batch is a string, is ignored, and replaced by `numpy.bytes_`.
Besides that, some other minor things have been fixed:
* Made `batch_size` an optional parameter in `to_tf_dataset`
* Map the `tensorflow` output dtypes just once, and not in every `tf.function` call during `map`
* Keep `numpy` formatting in the `datasets.Dataset` if already formatted like it, no need to format it again as `numpy`
* Docstring indentation in `dataset_to_tf` and `multiprocess_dataset_to_tf`
## What's missing in this PR?
I can include some integration tests if needed, to validate that `batch_size` is optional, and that the tensors in the TF-Dataset can be looped over with no issues as before.
|
closed
|
https://github.com/huggingface/datasets/pull/5883
| 2023-05-22T11:51:07
| 2023-06-08T11:09:03
| 2023-06-06T16:49:15
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,719,402,643
| 5,881
|
Split dataset by node: index error when sharding iterable dataset
|
### Describe the bug
Context: we're splitting an iterable dataset by node and then passing it to a torch data loader with multiple workers
When we iterate over it for 5 steps, we don't get an error
When we instead iterate over it for 8 steps, we get an `IndexError` when fetching the data if we have too many workers
### Steps to reproduce the bug
Here, we have 2 JAX processes (`jax.process_count() = 2`) which we split the dataset over. The dataset loading script can be found here: https://huggingface.co/datasets/distil-whisper/librispeech_asr/blob/c6a1e805cbfeed5057400ac5937327d7e30281b8/librispeech_asr.py#L310
<details>
<summary> Code to reproduce </summary>
```python
from datasets import load_dataset
import jax
from datasets.distributed import split_dataset_by_node
from torch.utils.data import DataLoader
from tqdm import tqdm
# load an example dataset (https://huggingface.co/datasets/distil-whisper/librispeech_asr)
dataset = load_dataset("distil-whisper/librispeech_asr", "all", split="train.clean.100", streaming=True)
# just keep the text column -> no need to define a collator
dataset_text = dataset.remove_columns(set(dataset.features.keys()) - {"text"})
# define some constants
batch_size = 256
num_examples = 5 # works for 5 examples, doesn't for 8
num_workers = dataset_text.n_shards
# try with multiple workers
dataloader = DataLoader(dataset_text, batch_size=batch_size, num_workers=num_workers, drop_last=True)
for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Multiple workers"):
if i == num_examples:
break
# try splitting by node (we can't do this with `dataset_text` since `split_dataset_by_node` expects the Audio column for an ASR dataset)
dataset = split_dataset_by_node(dataset, rank=jax.process_index(), world_size=jax.process_count())
# remove the text column again
dataset_text = dataset.remove_columns(set(dataset.features.keys()) - {"text"})
dataloader = DataLoader(dataset_text, batch_size=16, num_workers=num_workers // 2, drop_last=True)
for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Split by node"):
if i == num_examples:
break
# too many workers
dataloader = DataLoader(dataset_text, batch_size=256, num_workers=num_workers, drop_last=True)
for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Too many workers"):
if i == num_examples:
break
```
</details>
<details>
<summary> With 5 examples: </summary>
```
Multiple workers: 100%|███████████████████████████████████████████████████████████████████| 5/5 [00:16<00:00, 3.33s/it]
Assigning 7 shards (or data sources) of the dataset to each node.
Split by node: 100%|██████████████████████████████████████████████████████████████████████| 5/5 [00:13<00:00, 2.76s/it]
Assigning 7 shards (or data sources) of the dataset to each node.
Too many dataloader workers: 14 (max is dataset.n_shards=7). Stopping 7 dataloader workers.
To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary t
o have a number of workers greater than dataset.n_shards=7. To enable more parallelism, please split the dataset in more
files than 7.
Too many workers: 100%|███████████████████████████████████████████████████████████████████| 5/5 [00:15<00:00, 3.03s/it]
```
</details>
<details>
<summary> With 7 examples: </summary>
```
Multiple workers: 100%|███████████████████████████████████████████████████████████████████| 8/8 [00:13<00:00, 1.71s/it]
Assigning 7 shards (or data sources) of the dataset to each node.
Split by node: 100%|██████████████████████████████████████████████████████████████████████| 8/8 [00:11<00:00, 1.38s/it]
Assigning 7 shards (or data sources) of the dataset to each node.
Too many dataloader workers: 14 (max is dataset.n_shards=7). Stopping 7 dataloader workers.
To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=7. To enable more parallelism, please split the dataset in more files than 7.
Too many workers: 88%|██████████████████████████████████████████████████████████▋ | 7/8 [00:13<00:01, 1.89s/it]
Traceback (most recent call last):
File "distil-whisper/test_librispeech.py", line 36, in <module>
for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Too many workers"):
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/tqdm/std.py", line 1178, in __iter__
for obj in iterable:
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1325, in _next_data
return self._process_data(data)
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
data.reraise()
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/_utils.py", line 644, in reraise
raise exception
IndexError: Caught IndexError in DataLoader worker process 7.
Original Traceback (most recent call last):
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 986, in __iter__
yield from self._iter_pytorch(ex_iterable)
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 920, in _iter_pytorch
for key, example in ex_iterable.shard_data_sources(worker_info.id, worker_info.num_workers):
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 540, in shard_data_sources
self.ex_iterable.shard_data_sources(worker_id, num_workers),
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 796, in shard_data_sources
self.ex_iterable.shard_data_sources(worker_id, num_workers),
File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 126, in shard_data_sources
requested_gen_kwargs = _merge_gen_kwargs([gen_kwargs_list[i] for i in shard_indices])
File "/home/sanchitgandhi/datasets/src/datasets/utils/sharding.py", line 76, in _merge_gen_kwargs
for key in gen_kwargs_list[0]
IndexError: list index out of range
```
</details>
### Expected behavior
Should pass for both 5 and 7 examples
### Environment info
- `datasets` version: 2.12.1.dev0
- Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
|
open
|
https://github.com/huggingface/datasets/issues/5881
| 2023-05-22T10:36:13
| 2025-01-31T16:36:30
| null |
{
"login": "sanchit-gandhi",
"id": 93869735,
"type": "User"
}
|
[] | false
|
[] |
1,719,090,101
| 5,880
|
load_dataset from s3 file system through streaming can't not iterate data
|
### Describe the bug
I have a JSON file in my s3 file system(minio), I can use load_dataset to get the file link, but I can't iterate it
<img width="816" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/cc0778d3-36f3-45b5-ac68-4e7c664c2ed0">
<img width="1144" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/76872af3-8b3c-42ff-9f55-528c920a7af1">
we can change 4 lines to fix this bug, you can check whether it is ok for us.
<img width="941" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/5a22155a-ece7-496c-8506-047e5c235cd3">
### Steps to reproduce the bug
1. storage a file in you s3 file system
2. use load_dataset to read it through streaming
3. iterate it
### Expected behavior
can iterate it successfully
### Environment info
- `datasets` version: 2.12.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
|
open
|
https://github.com/huggingface/datasets/issues/5880
| 2023-05-22T07:40:27
| 2023-05-26T12:52:08
| null |
{
"login": "janineguo",
"id": 59083384,
"type": "User"
}
|
[] | false
|
[] |
1,718,203,843
| 5,878
|
Prefetching for IterableDataset
|
### Feature request
Add support for prefetching the next n batches through iterabledataset to reduce batch loading bottleneck in training loop.
### Motivation
The primary motivation behind this is to use hardware accelerators alongside a streaming dataset. This is required when you are in a low ram or low disk space setting as well as quick iteration where you're iterating though different accelerator environments (e.x changing ec2 instances quickly to figure out batch/sec for a particular architecture).
Currently, using the IterableDataset results in accelerators becoming basically useless due to the massive bottleneck induced by the dataset lazy loading/transform/mapping.
I've considered two alternatives:
PyTorch dataloader that handles this. However, I'm using jax, and I believe this is a piece of functionality that should live in the stream class.
Replicating the "num_workers" part of the PyTorch DataLoader to eagerly load batches and apply the transform so Arrow caching will automatically cache results and make them accessible.
### Your contribution
I may or may not have time to do this. Currently, I've written the basic multiprocessor approach to handle the eager DataLoader for my own use case with code that's not integrated to datasets. I'd definitely see this as being the default over the regular Dataset for most people given that they wouldn't have to wait on the datasets while also not worrying about performance.
|
open
|
https://github.com/huggingface/datasets/issues/5878
| 2023-05-20T15:25:40
| 2025-01-24T17:13:55
| null |
{
"login": "vyeevani",
"id": 30946190,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,717,983,961
| 5,877
|
Request for text deduplication feature
|
### Feature request
It would be great if there would be support for high performance, highly scalable text deduplication algorithms as part of the datasets library.
### Motivation
Motivated by this blog post https://huggingface.co/blog/dedup and this library https://github.com/google-research/deduplicate-text-datasets, but slightly frustrated by how its not very easy to work with these tools I am proposing this feature.
### Your contribution
I would be happy to contribute to the development effort of this feature. would love to collaborate with others in the development effort.
|
open
|
https://github.com/huggingface/datasets/issues/5877
| 2023-05-20T01:56:00
| 2024-01-25T14:40:09
| null |
{
"login": "SupreethRao99",
"id": 55043035,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,717,978,985
| 5,876
|
Incompatibility with DataLab
|
### Describe the bug
Hello,
I am currently working on a project where both [DataLab](https://github.com/ExpressAI/DataLab) and [datasets](https://github.com/huggingface/datasets) are subdependencies.
I noticed that I cannot import both libraries, as they both register FileSystems in `fsspec`, expecting the FileSystems not being registered before.
When running the code below, I get the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\__init__.py", line 28, in <module>
from datalabs.arrow_dataset import concatenate_datasets, Dataset
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\arrow_dataset.py", line 60, in <module>
from datalabs.arrow_writer import ArrowWriter, OptimizedTypedSequence
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\arrow_writer.py", line 28, in <module>
from datalabs.features import (
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\features\__init__.py", line 2, in <module>
from datalabs.features.audio import Audio
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\features\audio.py", line 21, in <module>
from datalabs.utils.streaming_download_manager import xopen
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\utils\streaming_download_manager.py", line 16, in <module>
from datalabs.filesystems import COMPRESSION_FILESYSTEMS
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\filesystems\__init__.py", line 37, in <module>
fsspec.register_implementation(fs_class.protocol, fs_class)
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\fsspec\registry.py", line 51, in register_implementation
raise ValueError(
ValueError: Name (bz2) already in the registry and clobber is False
```
I think as simple solution would be to just set `clobber=True` in https://github.com/huggingface/datasets/blob/main/src/datasets/filesystems/__init__.py#L28. This allows the register to discard previous registrations. This should work, as the datalabs FileSystems are copies of the datasets FileSystems. However, I don't know if it is guaranteed to be compatible with other libraries that might use the same protocols.
I am linking the symmetric issue on [DataLab](https://github.com/ExpressAI/DataLab/issues/425) as ideally the issue is solved in both libraries the same way. Otherwise, it could lead to different behaviors depending on which library gets imported first.
### Steps to reproduce the bug
1. Run `pip install datalabs==0.4.15 datasets==2.12.0`
2. Run the following python code:
```
import datalabs
import datasets
```
### Expected behavior
It should be possible to import both libraries without getting a Value Error
### Environment info
datalabs==0.4.15
datasets==2.12.0
|
closed
|
https://github.com/huggingface/datasets/issues/5876
| 2023-05-20T01:39:11
| 2023-05-25T06:42:34
| 2023-05-25T06:42:34
|
{
"login": "helpmefindaname",
"id": 26192135,
"type": "User"
}
|
[
{
"name": "good first issue",
"color": "7057ff"
}
] | false
|
[] |
1,716,770,394
| 5,875
|
Why split slicing doesn't behave like list slicing ?
|
### Describe the bug
If I want to get the first 10 samples of my dataset, I can do :
```
ds = datasets.load_dataset('mnist', split='train[:10]')
```
But if I exceed the number of samples in the dataset, an exception is raised :
```
ds = datasets.load_dataset('mnist', split='train[:999999999]')
```
> ValueError: Requested slice [:999999999] incompatible with 60000 examples.
### Steps to reproduce the bug
```
ds = datasets.load_dataset('mnist', split='train[:999999999]')
```
### Expected behavior
I would expect it to behave like python lists (no exception raised, the whole list is kept) :
```
d = list(range(1000))[:999999]
print(len(d)) # > 1000
```
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-12.6-arm64-arm-64bit
- Python version: 3.9.12
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5875
| 2023-05-19T07:21:10
| 2024-01-31T15:54:18
| 2024-01-31T15:54:18
|
{
"login": "astariul",
"id": 43774355,
"type": "User"
}
|
[
{
"name": "duplicate",
"color": "cfd3d7"
}
] | false
|
[] |
1,715,708,930
| 5,874
|
Using as_dataset on a "parquet" builder
|
### Describe the bug
I used a custom builder to ``download_and_prepare`` a dataset. The first (very minor) issue is that the doc seems to suggest ``download_and_prepare`` will return the dataset, while it does not ([builder.py](https://github.com/huggingface/datasets/blob/main/src/datasets/builder.py#L718-L738)).
```
>>> from datasets import load_dataset_builder
>>> builder = load_dataset_builder("rotten_tomatoes")
>>> ds = builder.download_and_prepare("./output_dir", file_format="parquet")
```
The main issue I am facing is loading the dataset from those parquet files. I used the `as_dataset` method suggested by the doc, however it returns:
`
FileNotFoundError: [Errno 2] Failed to open local file 'output_dir/__main__-train-00000-of-00245.arrow'. Detail:
[errno 2] No such file or directory.
`
### Steps to reproduce the bug
1. Create a custom builder of some sort: `builder = CustomBuilder()`.
2. Run `download_and_prepare` with the parquet format: `builder.download_and_prepare("./output_dir", file_format="parquet")`.
3. Run `dataset = builder.as_dataset()`.
### Expected behavior
I guess I'd expect `as_dataset` to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with `load_dataset` to no avail, probably due to misunderstandings on my part).
### Environment info
```
- `datasets` version: 2.12.0
- Platform: Linux-5.15.0-1027-gcp-x86_64-with-glibc2.31
- Python version: 3.10.0
- Huggingface_hub version: 0.14.1
- PyArrow version: 8.0.0
- Pandas version: 1.5.3
```
|
closed
|
https://github.com/huggingface/datasets/issues/5874
| 2023-05-18T14:09:03
| 2023-05-31T13:23:55
| 2023-05-31T13:23:55
|
{
"login": "rems75",
"id": 9039058,
"type": "User"
}
|
[] | false
|
[] |
1,713,269,724
| 5,873
|
Allow setting the environment variable for the lock file path
|
### Feature request
Add an environment variable to replace the default lock file path.
### Motivation
Usually, dataset path is a read-only path while the lock file needs to be modified each time. It would be convenient if the path can be reset individually.
### Your contribution
```/src/datasets/utils/filelock.py
class UnixFileLock(BaseFileLock):
def __init__(self, lock_file, timeout=-1, max_filename_length=None):
#-------------------
if os.getenv('DS_TMP_PATH'):
file_name = str(lock_file).split('/')[-1]
dataset_tmp_path = os.getenv('DS_TMP_PATH')
lock_file = os.path.join(dataset_tmp_path, file_name)
#-------------------
max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax
super().__init__(lock_file, timeout=timeout, max_filename_length=max_filename_length)
```
A simple demo is as upper. Thanks.
|
open
|
https://github.com/huggingface/datasets/issues/5873
| 2023-05-17T07:10:02
| 2023-05-17T07:11:05
| null |
{
"login": "xin3he",
"id": 83260933,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,713,174,662
| 5,872
|
Fix infer module for uppercase extensions
|
Fix the `infer_module_for_data_files` and `infer_module_for_data_files_in_archives` functions when passed a data file name with uppercase extension, e.g. `filename.TXT`.
Before, `None` module was returned.
|
closed
|
https://github.com/huggingface/datasets/pull/5872
| 2023-05-17T05:56:45
| 2023-05-17T14:26:59
| 2023-05-17T14:19:18
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,712,573,073
| 5,871
|
data configuration hash suffix depends on uncanonicalized data_dir
|
### Describe the bug
I am working with the `recipe_nlg` dataset, which requires manual download. Once it's downloaded, I've noticed that the hash in the custom data configuration is different if I add a trailing `/` to my `data_dir`. It took me a while to notice that the hashes were different, and to understand that that was the cause of my dataset being processed anew instead of the cached version being used.
### Steps to reproduce the bug
1. Follow the steps to manually download the `recipe_nlg` dataset to `/data/recipenlg`.
2. Load it using `load_dataset`, once without a trailing slash and once with one:
```python
>>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg")
Using custom data configuration default-082278caeea85765
Downloading and preparing dataset recipe_nlg/default to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74...
Dataset recipe_nlg downloaded and prepared to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.10s/it]
DatasetDict({
train: Dataset({
features: ['id', 'title', 'ingredients', 'directions', 'link', 'source', 'ner'],
num_rows: 2231142
})
})
>>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg/")
Using custom data configuration default-83e87680785d0493
Downloading and preparing dataset recipe_nlg/default to /home/user/.cache/huggingface/datasets/recipe_nlg/default-83e87680785d0493/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74...
Generating train split: 1%| | 12701/2231142 [00:04<13:15, 2790.25 examples/s
^C
```
3. Observe that the hash suffix in the custom data configuration changes due to the altered string.
### Expected behavior
I think I would expect the hash to remain constant if it actually points to the same location on disk. I would expect the use of `os.path.normpath` to canonicalize the paths.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
|
closed
|
https://github.com/huggingface/datasets/issues/5871
| 2023-05-16T18:56:04
| 2023-06-02T15:52:05
| 2023-06-02T15:52:05
|
{
"login": "kylrth",
"id": 5044802,
"type": "User"
}
|
[
{
"name": "good first issue",
"color": "7057ff"
}
] | false
|
[] |
1,712,156,282
| 5,870
|
Behaviour difference between datasets.map and IterableDatasets.map
|
### Describe the bug
All the examples in all the docs mentioned throughout huggingface datasets correspond to datasets object, and not IterableDatasets object. At one point of time, they might have been in sync, but the code for datasets version >=2.9.0 is very different as compared to the docs.
I basically need to .map() a transform on images in an iterable dataset, which was made using a custom databuilder config.
This works very good in map-styles datasets, but the .map() fails in IterableDatasets, show behvaiour as such:
"pixel_values" key not found, KeyError in examples object/dict passed into transform function for map, which works fine with map style, even as batch.
In iterable style, the object/dict passed into map() paramter callable function is completely different as what is mentioned in all examples.
Please look into this. Thank you
My databuilder class is inherited as such:
def _info(self):
print ("Config: ",self.config.__dict__.keys())
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"labels": datasets.Sequence(datasets.Value("uint16")),
# "labels_name": datasets.Value("string"),
# "pixel_values": datasets.Array3D(shape=(3, 1280, 960), dtype="float32"),
"pixel_values": datasets.Array3D(shape=(1280, 960, 3), dtype="uint8"),
"image_s3_path": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="none",
citation="",
)
def _split_generators(self, dl_manager):
records_train = list(db.mini_set.find({'split':'train'},{'image_s3_path':1, 'ocwen_template_name':1}))[:10000]
records_val = list(db.mini_set.find({'split':'val'},{'image_s3_path':1, 'ocwen_template_name':1}))[:1000]
# print (len(records),self.config.num_shards)
# shard_size_train = len(records_train)//self.config.num_shards
# sharded_records_train = [records_train[i:i+shard_size_train] for i in range(0,len(records_train),shard_size_train)]
# shard_size_val = len(records_val)//self.config.num_shards
# sharded_records_val = [records_val[i:i+shard_size_val] for i in range(0,len(records_val),shard_size_val)]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"records":records_train} # passing list of records, for sharding to take over
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"records":records_val} # passing list of records, for sharding to take over
),
]
def _generate_examples(self, records):
# print ("Generating examples for [{}] shards".format(len(shards)))
# initiate_db_connection()
# records = list(db.mini_set.find({'split':split},{'image_s3_path':1, 'ocwen_template_name':1}))[:10]
id_ = 0
# for records in shards:
for i,rec in enumerate(records):
img_local_path = fetch_file(rec['image_s3_path'],self.config.buffer_dir)
# t = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.squeeze()
# print (t.shape, type(t),type(t[0][0][0]))
# sys.exit()
pvs = np.array(Image.open(img_local_path).resize((1280,960))) # image object is wxh, so resize as per that, numpy array of it is hxwxc, transposing to cxwxh
# pvs = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.astype(np.float16).squeeze()
# print (type(pvs[0][0][0]))
lblids = self.config.processor.tokenizer('<s_class>'+rec['ocwen_template_name']+'</s_class>'+'</s>', add_special_tokens=False, padding=False, truncation=False, return_tensors="np")["input_ids"].squeeze(0) # take padding later, as per batch collating
# print (len(lblids),type(lblids[0]))
# print (type(pvs),pvs.shape,type(pvs[0][0][0]), type(lblids))
yield id_, {"labels":lblids,"pixel_values":pvs,"image_s3_path":rec['image_s3_path']}
id_+=1
os.remove(img_local_path)
and I load it inside my trainer script as such
`ds = load_dataset("/tmp/DonutDS/dataset/", split="train", streaming=True) # iterable dataset, where .map() falls`
or also as
`ds = load_from_disk('/tmp/DonutDS/dataset/') #map style dataset`
Thank you to the team for having such a great library, and for this bug fix in advance!
### Steps to reproduce the bug
Above config can allow one to reproduce the said bug
### Expected behavior
.map() should show some consistency b/w map-style and iterable-style datasets, or atleast the docs should address iterable-style datasets behaviour and examples. I honestly do not figure the use of such docs.
### Environment info
datasets==2.9.0
transformers==4.26.0
|
open
|
https://github.com/huggingface/datasets/issues/5870
| 2023-05-16T14:32:57
| 2023-05-16T14:36:05
| null |
{
"login": "llStringll",
"id": 30209072,
"type": "User"
}
|
[] | false
|
[] |
1,711,990,003
| 5,869
|
Image Encoding Issue when submitting a Parquet Dataset
|
### Describe the bug
Hello,
I'd like to report an issue related to pushing a dataset represented as a Parquet file to a dataset repository using Dask. Here are the details:
We attempted to load an example dataset in Parquet format from the Hugging Face (HF) filesystem using Dask with the following code snippet:
```
import dask.dataframe as dd
df = dd.read_parquet("hf://datasets/lambdalabs/pokemon-blip-captions",index=False)
```
In this dataset, the "image" column is represented as a dictionary/struct with the format:
```
df = df.compute()
df["image"].iloc[0].keys()
-> dict_keys(['bytes', 'path'])
```
I think this is the format encoded by the [`Image`](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Image) feature extractor from datasets to format suitable for Arrow.
The next step was to push the dataset to a repository that I created:
```
dd.to_parquet(dask_df, path = "hf://datasets/philippemo/dummy_dataset/data")
```
However, after pushing the dataset using Dask, the "image" column is now represented as the encoded dictionary `(['bytes', 'path'])`, and the images are not properly visualized. You can find the dataset here: [Link to the problematic dataset](https://huggingface.co/datasets/philippemo/dummy_dataset).
It's worth noting that both the original dataset and the one submitted with Dask have the same schema with minor alterations related to metadata:
**[ Schema of original dummy example.](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/blob/main/data/train-00000-of-00001-566cc9b19d7203f8.parquet)**
```
image: struct<bytes: binary, path: null>
child 0, bytes: binary
child 1, path: null
text: string
```
**[ Schema of pushed dataset with dask](https://huggingface.co/datasets/philippemo/dummy_dataset/blob/main/data/part.0.parquet)**
```
image: struct<bytes: binary, path: null>
child 0, bytes: binary
child 1, path: null
text: string
```
This issue seems to be related to an encoding type that occurs when pushing a model to the hub. Normally, models should be represented as an HF dataset before pushing, but we are working with an example where we need to push large datasets using Dask.
Could you please provide clarification on how to resolve this issue?
Thank you!
### Reproduction
To get the schema I downloaded the parquet files and used pyarrow.parquet to read the schema
```
import pyarrow.parquet
pyarrow.parquet.read_schema(<path_to_parquet>, memory_map=True)
```
### Logs
_No response_
### System info
```shell
- huggingface_hub version: 0.14.1
- Platform: Linux-5.19.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /home/philippe/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: philippemo
- Configured git credential helpers: cache
- FastAI: N/A
- Tensorflow: N/A
- Torch: N/A
- Jinja2: 3.1.2
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.4.0
- hf_transfer: N/A
- gradio: N/A
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /home/philippe/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /home/philippe/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/philippe/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
```
|
closed
|
https://github.com/huggingface/datasets/issues/5869
| 2023-05-16T09:42:58
| 2023-06-16T12:48:38
| 2023-06-16T09:30:48
|
{
"login": "PhilippeMoussalli",
"id": 47530815,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,711,173,098
| 5,868
|
Is it possible to change a cached file and 're-cache' it instead of re-generating?
|
### Feature request
Hi,
I have a huge cached file using `map`(over 500GB), and I want to change an attribution of each element, is there possible to do it using some method instead of re-generating, because `map` takes over 24 hours
### Motivation
For large datasets, I think it is very important because we always face the problem which is changing something in the original cache without re-generating it.
### Your contribution
For now, I can't help, sorry.
|
closed
|
https://github.com/huggingface/datasets/issues/5868
| 2023-05-16T03:45:42
| 2023-05-17T11:21:36
| 2023-05-17T11:21:36
|
{
"login": "zyh3826",
"id": 31238754,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,710,656,067
| 5,867
|
Add logic for hashing modules/functions optimized with `torch.compile`
|
Fix https://github.com/huggingface/datasets/issues/5839
PS: The `Pickler.save` method is becoming a bit messy, so I plan to refactor the pickler a bit at some point.
|
closed
|
https://github.com/huggingface/datasets/pull/5867
| 2023-05-15T19:03:35
| 2024-01-11T06:30:50
| 2023-11-27T20:03:31
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,710,496,993
| 5,866
|
Issue with Sequence features
|
### Describe the bug
Sequences features sometimes causes errors when the specified length is not -1
### Steps to reproduce the bug
```python
import numpy as np
from datasets import Features, ClassLabel, Sequence, Value, Dataset
feats = Features(**{'target': ClassLabel(names=[0, 1]),'x': Sequence(feature=Value(dtype='float64',id=None), length=2, id=None)})
Dataset.from_dict({"target": np.ones(2000).astype(int), "x": np.random.rand(2000,2)},features = feats).flatten_indices()
```
Throws:
```
TypeError: Couldn't cast array of type
fixed_size_list<item: double>[2]
to
Sequence(feature=Value(dtype='float64', id=None), length=2, id=None)
```
The same code works without any issues when `length = -1`
EDIT: The error seems to happen only when the length of the dataset is bigger than 1000 for some reason
### Expected behavior
No exception
### Environment info
- `datasets` version: 2.10.1
- Python version: 3.9.5
- PyArrow version: 11.0.0
- Pandas version: 1.4.1
|
closed
|
https://github.com/huggingface/datasets/issues/5866
| 2023-05-15T17:13:29
| 2023-05-26T11:57:17
| 2023-05-26T11:57:17
|
{
"login": "alialamiidrissi",
"id": 14365168,
"type": "User"
}
|
[] | false
|
[] |
1,710,455,738
| 5,865
|
Deprecate task api
|
The task API is not well adopted in the ecosystem, so this PR deprecates it. The `train_eval_index` is a newer, more flexible solution that should be used instead (I think?).
These are the projects that still use the task API :
* the image classification example in Transformers: [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/pytorch/image-classification/run_image_classification_no_trainer.py#L262) and [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/tensorflow/image-classification/run_image_classification.py#L277)
* autotrain: [here](https://github.com/huggingface/autotrain-backend/blob/455e274004b56f9377d64db4ab03671508fcc4cd/zeus/zeus/run/utils.py#L666)
* api-inference-community: [here](https://github.com/huggingface/api-inference-community/blob/fb8fb29d577a5bf01c82944db745489a6d6ed3d4/manage.py#L64) (but the rest of the code does not call the `resolve_dataset` function)
So we need to update these files after the merge.
cc @lewtun
|
closed
|
https://github.com/huggingface/datasets/pull/5865
| 2023-05-15T16:48:24
| 2023-07-10T12:33:59
| 2023-07-10T12:24:01
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,710,450,047
| 5,864
|
Slow iteration over Torch tensors
|
### Describe the bug
I have a problem related to this [issue](https://github.com/huggingface/datasets/issues/5841): I get a way slower iteration when using a Torch dataloader if I use vanilla Numpy tensors or if I first apply a ToTensor transform to the input. In particular, it takes 5 seconds to iterate over the vanilla input and ~30s after the transformation.
### Steps to reproduce the bug
Here is the minimum code to reproduce the problem
```python
import numpy as np
from datasets import Dataset, DatasetDict, load_dataset, Array3D, Image, Features
from torch.utils.data import DataLoader
from tqdm import tqdm
import torchvision
from torchvision.transforms import ToTensor, Normalize
#################################
# Without transform
#################################
train_dataset = load_dataset(
'cifar100',
split='train',
use_auth_token=True,
)
train_dataset.set_format(type="numpy", columns=["img", "fine_label"])
train_loader= DataLoader(
train_dataset,
batch_size=100,
pin_memory=False,
shuffle=True,
num_workers=8,
)
for batch in tqdm(train_loader, desc="Loading data, no transform"):
pass
#################################
# With transform
#################################
transform_func = torchvision.transforms.Compose([
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std= [0.229, 0.224, 0.225]),]
)
train_dataset = train_dataset.map(
desc=f"Preprocessing samples",
function=lambda x: {"img": transform_func(x["img"])},
)
train_dataset.set_format(type="numpy", columns=["img", "fine_label"])
train_loader= DataLoader(
train_dataset,
batch_size=100,
pin_memory=False,
shuffle=True,
num_workers=8,
)
for batch in tqdm(train_loader, desc="Loading data after transform"):
pass
```
I have also tried converting the Image column to an Array3D
```python
img_shape = train_dataset[0]["img"].shape
features = train_dataset.features.copy()
features["x"] = Array3D(shape=img_shape, dtype="float32")
train_dataset = train_dataset.map(
desc=f"Preprocessing samples",
function=lambda x: {"x": np.array(x["img"], dtype=np.uint8)},
features=features,
)
train_dataset.cast_column("x", Array3D(shape=img_shape, dtype="float32"))
train_dataset.set_format(type="numpy", columns=["x", "fine_label"])
```
but to no avail. Any clue?
### Expected behavior
The iteration should take approximately the same time with or without the transformation, as it doesn't change the shape of the input. What may be the issue here?
### Environment info
```
- `datasets` version: 2.12.0
- Platform: Linux-5.4.0-137-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
```
|
open
|
https://github.com/huggingface/datasets/issues/5864
| 2023-05-15T16:43:58
| 2024-10-08T10:21:48
| null |
{
"login": "crisostomi",
"id": 51738205,
"type": "User"
}
|
[] | false
|
[] |
1,710,335,905
| 5,863
|
Use a new low-memory approach for tf dataset index shuffling
|
This PR tries out a new approach to generating the index tensor in `to_tf_dataset`, which should reduce memory usage for very large datasets. I'll need to do some testing before merging it!
Fixes #5855
|
closed
|
https://github.com/huggingface/datasets/pull/5863
| 2023-05-15T15:28:34
| 2023-06-08T16:40:18
| 2023-06-08T16:32:51
|
{
"login": "Rocketknight1",
"id": 12866554,
"type": "User"
}
|
[] | true
|
[] |
1,710,140,646
| 5,862
|
IndexError: list index out of range with data hosted on Zenodo
|
The dataset viewer sometimes raises an `IndexError`:
```
IndexError: list index out of range
```
See:
- huggingface/datasets-server#1151
- https://huggingface.co/datasets/reddit/discussions/5
- huggingface/datasets-server#1118
- https://huggingface.co/datasets/krr-oxford/OntoLAMA/discussions/1
- https://huggingface.co/datasets/hyperpartisan_news_detection/discussions/3
- https://huggingface.co/datasets/um005/discussions/2
- https://huggingface.co/datasets/tapaco/discussions/2
- https://huggingface.co/datasets/common_language/discussions/3
- https://huggingface.co/datasets/pass/discussions/1
After investigation:
- This happens with data files hosted on Zenodo
- Indeed, there is an underlying 429 HTTP error: Too Many Requests
Note that some time ago, it also happened with data files hosted on Google Drive. See:
- #4581
- #4580
The reason then was that there was a 403 HTTP error: Forbidden
|
open
|
https://github.com/huggingface/datasets/issues/5862
| 2023-05-15T13:47:19
| 2023-09-25T12:09:51
| null |
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,709,807,340
| 5,861
|
Better error message when combining dataset dicts instead of datasets
|
close https://github.com/huggingface/datasets/issues/5851
|
closed
|
https://github.com/huggingface/datasets/pull/5861
| 2023-05-15T10:36:24
| 2023-05-23T10:40:13
| 2023-05-23T10:32:58
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,709,727,460
| 5,860
|
Minor tqdm optim
|
Don't create a tqdm progress bar when `disable_tqdm` is passed to `map_nested`.
On my side it sped up some iterable datasets by ~30% when `map_nested` is used extensively to recursively tensorize python dicts.
|
closed
|
https://github.com/huggingface/datasets/pull/5860
| 2023-05-15T09:49:37
| 2023-05-17T18:46:46
| 2023-05-17T18:39:35
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,709,554,829
| 5,859
|
Raise TypeError when indexing a dataset with bool
|
Fix #5858.
|
closed
|
https://github.com/huggingface/datasets/pull/5859
| 2023-05-15T08:08:42
| 2023-05-25T16:31:24
| 2023-05-25T16:23:17
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,709,332,632
| 5,858
|
Throw an error when dataset improperly indexed
|
### Describe the bug
Pandas-style subset indexing on dataset does not throw an error, when maybe it should. Instead returns the first instance of the dataset regardless of index condition.
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. `squad = datasets.load_dataset("squad_v2", split="validation")`
2. `item = squad[squad['question'] == "Who was the Norse leader?"]`
or `it = squad[squad['id'] == '56ddde6b9a695914005b962b']`
3. returns the first item in the dataset, which does not satisfy the above conditions:
`{'id': '56ddde6b9a695914005b9628', 'title': 'Normans', 'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse ("Norman" comes from "Norseman") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries.', 'question': 'In what country is Normandy located?', 'answers': {'text': ['France', 'France', 'France', 'France'], 'answer_start': [159, 159, 159, 159]}}`
### Expected behavior
Should either throw an error message, or return the dataset item that satisfies the condition.
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5858
| 2023-05-15T05:15:53
| 2023-05-25T16:23:19
| 2023-05-25T16:23:19
|
{
"login": "sarahwie",
"id": 8027676,
"type": "User"
}
|
[] | false
|
[] |
1,709,326,622
| 5,857
|
Adding chemistry dataset/models in huggingface
|
### Feature request
Huggingface is really amazing platform for open science.
In addition to computer vision, video and NLP, would it be of interest to add chemistry/materials science dataset/models in Huggingface? Or, if its already done, can you provide some pointers.
We have been working on a comprehensive benchmark on this topic: [JARVIS-Leaderboard](https://pages.nist.gov/jarvis_leaderboard/) and I am wondering if we could contribute/integrate this project as a part of huggingface.
### Motivation
Similar to the main stream AI field, there is need of large scale benchmarks/models/infrastructure for chemistry/materials data.
### Your contribution
We can start adding datasets as our [benchmarks](https://github.com/usnistgov/jarvis_leaderboard/tree/main/jarvis_leaderboard/benchmarks) should be easily convertible to the dataset format.
|
closed
|
https://github.com/huggingface/datasets/issues/5857
| 2023-05-15T05:09:49
| 2023-07-21T13:45:40
| 2023-07-21T13:45:40
|
{
"login": "knc6",
"id": 16902896,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,709,218,242
| 5,856
|
Error loading natural_questions
|
### Describe the bug
When try to load natural_questions through datasets == 2.12.0 with python == 3.8.9:
```python
import datasets
datasets.load_dataset('natural_questions',beam_runner='DirectRunner')
```
It failed with following info:
`pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs`
### Steps to reproduce the bug
In python console:
```python
import datasets
datasets.load_dataset('natural_questions',beam_runner='DirectRunner')
```
Then the trace is:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/load.py", line 1797, in load_dataset
builder_instance.download_and_prepare(
File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/builder.py", line 890, in download_and_prepare
self._download_and_prepare(
File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/builder.py", line 2019, in _download_and_prepare
num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter))
File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/arrow_writer.py", line 694, in finalize
shard_num_bytes, _ = parquet_to_arrow(source, destination)
File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/arrow_writer.py", line 737, in parquet_to_arrow
for record_batch in parquet_file.iter_batches():
File "pyarrow/_parquet.pyx", line 1323, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```
### Expected behavior
load natural_question questions
### Environment info
```
- `datasets` version: 2.12.0
- Platform: Linux-3.10.0-1160.42.2.el7.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.8.9
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.1
```
|
closed
|
https://github.com/huggingface/datasets/issues/5856
| 2023-05-15T02:46:04
| 2023-06-05T09:11:19
| 2023-06-05T09:11:18
|
{
"login": "Crownor",
"id": 19185508,
"type": "User"
}
|
[] | false
|
[] |
1,708,784,943
| 5,855
|
`to_tf_dataset` consumes too much memory
|
### Describe the bug
Hi, I'm using `to_tf_dataset` to convert a _large_ dataset to `tf.data.Dataset`. I observed that the data loading *before* training took a lot of time and memory, even with `batch_size=1`.
After some digging, i believe the reason lies in the shuffle behavior. The [source code](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/tf_utils.py#L185) uses `len(dataset)` as the `buffer_size`, which may load all the data into the memory, and the [tf.data doc](https://www.tensorflow.org/guide/data#randomly_shuffling_input_data) also states that "While large buffer_sizes shuffle more thoroughly, they can take a lot of memory, and significant time to fill".
### Steps to reproduce the bug
```python
from datasets import Dataset
def gen(): # some large data
for i in range(50000000):
yield {"data": i}
ds = Dataset.from_generator(gen, cache_dir="./huggingface")
tf_ds = ds.to_tf_dataset(
batch_size=64,
shuffle=False, # no shuffle
drop_remainder=False,
prefetch=True,
)
# fast and memory friendly 🤗
for batch in tf_ds:
...
tf_ds_shuffle = ds.to_tf_dataset(
batch_size=64,
shuffle=True,
drop_remainder=False,
prefetch=True,
)
# slow and memory hungry for simple iteration 😱
for batch in tf_ds_shuffle:
...
```
### Expected behavior
Shuffling should not load all the data into the memory. Would adding a `buffer_size` parameter in the `to_tf_dataset` API alleviate the problem?
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.17.1-051701-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 1.4.3
|
closed
|
https://github.com/huggingface/datasets/issues/5855
| 2023-05-14T01:22:29
| 2023-06-08T16:32:52
| 2023-06-08T16:32:52
|
{
"login": "massquantity",
"id": 28751760,
"type": "User"
}
|
[] | false
|
[] |
1,708,779,300
| 5,854
|
Can not load audiofolder dataset on kaggle
|
### Describe the bug
It's crash log:
FileNotFoundError: Couldn't find a dataset script at /kaggle/working/audiofolder/audiofolder.py or any data file in the same directory. Couldn't find 'audiofolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/audiofolder/audiofolder.py
### Steps to reproduce the bug
common_voice = load_dataset("audiofolder", data_dir="/kaggle/working/data")
### Expected behavior
load dataset without error. It works ok on colab, but on kaggle it happends.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.109+-x86_64-with-glibc2.31
- Python version: 3.10.10
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5854
| 2023-05-14T00:50:47
| 2023-08-16T13:35:36
| 2023-07-21T13:53:45
|
{
"login": "ILG2021",
"id": 93691919,
"type": "User"
}
|
[] | false
|
[] |
1,708,092,786
| 5,853
|
[docs] Redirects, migrated from nginx
| null |
closed
|
https://github.com/huggingface/datasets/pull/5853
| 2023-05-12T19:19:27
| 2023-05-15T10:37:19
| 2023-05-15T10:30:14
|
{
"login": "julien-c",
"id": 326577,
"type": "User"
}
|
[] | true
|
[] |
1,707,927,165
| 5,852
|
Iterable torch formatting
|
Used the TorchFormatter to get torch tensors in iterable dataset with format set to "torch".
It uses the data from Arrow if possible, otherwise applies recursive_tensorize.
When set back to format_type=None, cast_to_python_objects is used.
requires https://github.com/huggingface/datasets/pull/5821
close https://github.com/huggingface/datasets/issues/5793
|
closed
|
https://github.com/huggingface/datasets/pull/5852
| 2023-05-12T16:48:49
| 2023-06-13T16:04:05
| 2023-06-13T15:57:05
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,707,678,911
| 5,850
|
Make packaged builders skip non-supported file formats
|
This PR makes packaged builders skip non-supported file formats:
- Csv builder skips non-CSV files
- Analogously for the other builders
Fix #5849.
|
open
|
https://github.com/huggingface/datasets/pull/5850
| 2023-05-12T13:52:34
| 2023-06-07T12:26:38
| null |
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,707,551,511
| 5,849
|
CSV datasets should only read the CSV data files in the repo
|
When a no-script dataset has many CSV files and a JPG file, the library infers to use the Csv builder, but tries to read as CSV all files in the repo, also the JPG file.
I think the Csv builder should filter out non-CSV files when reading.
An analogue solution should be implemented for other packaged builders.
Related to:
- https://huggingface.co/datasets/abidlabs/img2text/discussions/1
- https://github.com/gradio-app/gradio/pull/3973#issuecomment-1545409061
CC: @abidlabs @severo
|
closed
|
https://github.com/huggingface/datasets/issues/5849
| 2023-05-12T12:29:53
| 2023-06-22T14:16:27
| 2023-06-22T14:16:27
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,707,506,734
| 5,848
|
Add `accelerate` as metric's test dependency to fix CI error
|
The `frugalscore` metric uses Transformers' Trainer, which requires `accelerate` (as of recently).
Fixes the following [CI error](https://github.com/huggingface/datasets/actions/runs/4950900048/jobs/8855148703?pr=5845).
|
closed
|
https://github.com/huggingface/datasets/pull/5848
| 2023-05-12T12:01:01
| 2023-05-12T13:48:47
| 2023-05-12T13:39:06
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,706,616,634
| 5,847
|
Streaming IterableDataset not working with translation pipeline
|
### Describe the bug
I'm trying to use a streaming dataset for translation inference to avoid downloading the training data.
I'm using a pipeline and a dataset, and following the guidance in the tutorial.
Instead I get an exception that IterableDataset has no len().
### Steps to reproduce the bug
CODE:
```
from transformers import pipeline
from transformers.pipelines.pt_utils import KeyDataset
from datasets import load_dataset
ds = load_dataset(path="wmt14", name="fr-en", split="test", streaming=True)
bs=1
mt = pipeline("translation_en_to_fr", model="t5-base", batch_size=bs)
#print(mt("hello")) THIS WORKS
ks = KeyDataset(ds, "translation")
print(f"{ks}")
xx= mt(ks)
for x in xx:
print(x)
```
RUN:
```
(watnlp) [jlquinn@bertdev01 hf]$ python ende.t5.pipe.py
2023-05-11 16:48:08.817572: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-05-11 16:48:08.821388: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2023-05-11 16:48:08.821407: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
<transformers.pipelines.pt_utils.KeyDataset object at 0x7f61ed5da9d0>
Traceback (most recent call last):
File "/home/jlquinn/models/hf/ende.t5.pipe.py", line 11, in <module>
for x in xx:
File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 111, in __next__
item = next(self.iterator)
File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 111, in __next__
item = next(self.iterator)
File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 681, in __next__
data = self._next_data()
File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 720, in _next_data
index = self._next_index() # may raise StopIteration
File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 671, in _next_index
return next(self._sampler_iter) # may raise StopIteration
File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 247, in __iter__
for idx in self.sampler:
File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 76, in __iter__
return iter(range(len(self.data_source)))
File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 13, in __len__
return len(self.dataset)
File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 289, in __len__
return len(self.dataset)
TypeError: object of type 'IterableDataset' has no len()
```
### Expected behavior
I'm expecting french translations of the english test set to be printed.
### Environment info
Run on CPU with no GPU.
RHEL 8.7 x86_64
python 3.9.0
transformers 4.17.0
datasets 2.0.0
tokenizers 0.12.1
```
(watnlp) [jlquinn@bertdev01 hf]$ datasets-cli env
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-372.19.1.el8_6.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.0
- PyArrow version: 8.0.0
- Pandas version: 1.4.4
```
|
open
|
https://github.com/huggingface/datasets/issues/5847
| 2023-05-11T21:52:38
| 2023-05-16T15:59:55
| null |
{
"login": "jlquinn",
"id": 826841,
"type": "User"
}
|
[] | false
|
[] |
1,707,907,048
| 5,851
|
Error message not clear in interleaving datasets
|
### System Info
standard env
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
I'm trying to interleave 'sciq', 'wiki' and the 'pile-enron' dataset. I think the error I made was that I loaded the train split of one, but for the other but the error is not too helpful-
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[/home/suryahari/Vornoi/save_model_ops.py](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/Vornoi/save_model_ops.py) in line 3
[41](file:///home/suryahari/Vornoi/save_model_ops.py?line=40) # %%
----> [43](file:///home/suryahari/Vornoi/save_model_ops.py?line=42) dataset = interleave_datasets(datasets, stopping_strategy="all_exhausted")
File [~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124), in interleave_datasets(datasets, probabilities, seed, info, split, stopping_strategy)
[122](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=121) for dataset in datasets[1:]:
[123](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=122) if (map_style and not isinstance(dataset, Dataset)) or (iterable and not isinstance(dataset, IterableDataset)):
--> [124](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=123) raise ValueError(
[125](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=124) f"Unable to interleave a {type(datasets[0])} with a {type(dataset)}. Expected a list of Dataset objects or a list of IterableDataset objects."
[126](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=125) )
[127](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=126) if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
[128](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=127) raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy.")
ValueError: Unable to interleave a with a . Expected a list of Dataset objects or a list of IterableDataset objects.
```
### Expected behavior
the error message should hopefully be more clear
|
closed
|
https://github.com/huggingface/datasets/issues/5851
| 2023-05-11T20:52:13
| 2023-05-23T10:32:59
| 2023-05-23T10:32:59
|
{
"login": "surya-narayanan",
"id": 17240858,
"type": "User"
}
|
[] | false
|
[] |
1,706,289,290
| 5,846
|
load_dataset('bigcode/the-stack-dedup', streaming=True) very slow!
|
### Describe the bug
Running
```
import datasets
ds = datasets.load_dataset('bigcode/the-stack-dedup', streaming=True)
```
takes about 2.5 minutes!
I would expect this to be near instantaneous. With other datasets, the runtime is one or two seconds.
### Environment info
- `datasets` version: 2.11.0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.10.10
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0
|
closed
|
https://github.com/huggingface/datasets/issues/5846
| 2023-05-11T17:58:57
| 2024-04-08T12:53:17
| 2024-04-05T12:28:58
|
{
"login": "tbenthompson",
"id": 4241811,
"type": "User"
}
|
[] | false
|
[] |
1,706,253,251
| 5,845
|
Add `date_format` param to the CSV reader
|
Adds the `date_format` param introduced in Pandas 2.0 to the CSV reader and improves its type hints.
|
closed
|
https://github.com/huggingface/datasets/pull/5845
| 2023-05-11T17:29:57
| 2023-05-15T07:39:13
| 2023-05-12T15:14:48
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,705,907,812
| 5,844
|
TypeError: Couldn't cast array of type struct<answer: struct<unanswerable: bool, answerType: string, free_form_answer: string, evidence: list<item: string>, evidenceAnnotate: list<item: string>, highlighted_evidence: list<item: string>>> to ...
|
### Describe the bug
TypeError: Couldn't cast array of type struct<answer: struct<unanswerable: bool, answerType: string, free_form_answer: string, evidence: list<item: string>, evidenceAnnotate: list<item: string>, highlighted_evidence: list<item: string>>> to {'answer': {'unanswerable': Value(dtype='bool', id=None), 'answerType': Value(dtype='string', id=None), 'free_form_answer': Value(dtype='string', id=None), 'evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'evidenceAnnotate': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'highlighted_evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'unanswerable': Value(dtype='bool', id=None), 'answerType': Value(dtype='string', id=None), 'free_form_answer': Value(dtype='string', id=None), 'evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'evidenceAnnotate': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'highlighted_evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
When I use _load_dataset()_ I get the error
`from datasets import load_dataset
datafiles = {'train': './data/train.json', 'validation': './data/validation.json', 'test': './data/test.json'}
raw_data = load_dataset("json", data_files=datafiles, cache_dir="./cache")
`
Detailed error information is as follows：
Traceback (most recent call last):
File "C:/Users/CHENJIALEI/Desktop/NLPCC2023/NLPCC23_SciMRC-main/test2.py", line 9, in <module>
raw_data = load_dataset("json", data_files=datafiles, cache_dir="./cache")
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\load.py", line 1747, in load_dataset
builder_instance.download_and_prepare(
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\builder.py", line 814, in download_and_prepare
self._download_and_prepare(
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\builder.py", line 905, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\builder.py", line 1521, in _prepare_split
writer.write_table(table)
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\arrow_writer.py", line 540, in write_table
pa_table = table_cast(pa_table, self._schema)
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 2069, in table_cast
return cast_table_to_schema(table, schema)
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 2031, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 2031, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1740, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1740, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1867, in cast_array_to_feature
casted_values = _c(array.values, feature[0])
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1742, in wrapper
return func(array, *args, **kwargs)
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1862, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1862, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1742, in wrapper
return func(array, *args, **kwargs)
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1867, in cast_array_to_feature
casted_values = _c(array.values, feature[0])
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1742, in wrapper
return func(array, *args, **kwargs)
File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1913, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
It is successful when I load the data separately
`raw_data = load_dataset("json", data_files="./data/train.json", cache_dir="./cache")`
### Steps to reproduce the bug
1.from datasets import load_dataset
2.datafiles = {'train': './data/train.json', 'validation': './data/validation.json', 'test': './data/test.json'}
3.raw_data = load_dataset("json", data_files=datafiles, cache_dir="./cache")
### Expected behavior
Successfully load dataset
### Environment info
datasets == 2.6.1
pyarrow == 8.0.0
python == 3.8
platform:windows11
|
open
|
https://github.com/huggingface/datasets/issues/5844
| 2023-05-11T14:15:01
| 2023-05-11T14:15:01
| null |
{
"login": "chen-coding",
"id": 54010030,
"type": "User"
}
|
[] | false
|
[] |
1,705,286,639
| 5,841
|
Abusurdly slow on iteration
|
### Describe the bug
I am attempting to iterate through an image dataset, but I am encountering a significant slowdown in the iteration speed. In order to investigate this issue, I conducted the following experiment:
```python
a=torch.randn(100,224)
a=torch.stack([a] * 10000)
a.shape
# %%
ds=Dataset.from_dict({"tensor":a})
for i in tqdm(ds.with_format("numpy")):
pass
for i in tqdm(ds.with_format("torch")):
pass
```
I noticed that the dataset in numpy format performs significantly faster than the one in torch format. My hypothesis is that the dataset undergoes a transformation process of torch->python->numpy(torch) in the background, which might be causing the slowdown. Is there any way to expedite the process by bypassing such transformations?
Furthermore, if I increase the size of a to an image shape, like:
```python
a=torch.randn(3,224,224)
```
the iteration speed becomes absurdly slow, around 100 iterations per second, whereas the speed with numpy format is approximately 250 iterations per second. This level of speed would be unacceptable for large image datasets, as it could take several hours just to iterate through a single epoch.
### Steps to reproduce the bug
```python
a=torch.randn(100,224)
a=torch.stack([a] * 10000)
a.shape
# %%
ds=Dataset.from_dict({"tensor":a})
for i in tqdm(ds.with_format("numpy")):
pass
for i in tqdm(ds.with_format("torch")):
pass
```
### Expected behavior
iteration faster
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.10
- Python version: 3.8.16
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0
|
closed
|
https://github.com/huggingface/datasets/issues/5841
| 2023-05-11T08:04:09
| 2023-05-15T15:38:13
| 2023-05-15T15:38:13
|
{
"login": "fecet",
"id": 41792945,
"type": "User"
}
|
[] | false
|
[] |
1,705,212,085
| 5,840
|
load model error.
|
### Describe the bug
I had trained one model use deepspeed, when I load the final load I get the follow error:
OSError: Can't load tokenizer for '/XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/home/fm001/hzl/Project/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor' is the correct path to a directory containing all relevant files for a BloomTokenizerFast tokenizer.
my load code is : python chat.py --path /XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor/
### Steps to reproduce the bug
。。。
### Expected behavior
。。。
### Environment info
。。。
|
closed
|
https://github.com/huggingface/datasets/issues/5840
| 2023-05-11T07:12:38
| 2023-05-12T13:44:07
| 2023-05-12T13:44:06
|
{
"login": "LanShanPi",
"id": 58167546,
"type": "User"
}
|
[] | false
|
[] |
1,705,510,602
| 5,842
|
Remove columns in interable dataset
|
### Feature request
Right now, remove_columns() produces a NotImplementedError for iterable style datasets
### Motivation
It would be great to have the same functionality irrespective of whether one is using an iterable or a map-style dataset
### Your contribution
hope and courage.
|
closed
|
https://github.com/huggingface/datasets/issues/5842
| 2023-05-11T03:48:46
| 2023-06-21T16:36:42
| 2023-06-21T16:36:41
|
{
"login": "surya-narayanan",
"id": 17240858,
"type": "User"
}
|
[] | false
|
[] |
1,705,514,551
| 5,843
|
Can't add iterable datasets to a Dataset Dict.
|
### System Info
standard env
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Get the following error:
TypeError: Values in `DatasetDict` should be of type `Dataset` but got type '<class 'datasets.iterable_dataset.IterableDataset'>'
### Expected behavior
should be able to add iterable datasets to a dataset dict
|
closed
|
https://github.com/huggingface/datasets/issues/5843
| 2023-05-11T02:09:29
| 2023-05-25T04:51:59
| 2023-05-25T04:51:59
|
{
"login": "surya-narayanan",
"id": 17240858,
"type": "User"
}
|
[] | false
|
[] |
1,704,554,718
| 5,839
|
Make models/functions optimized with `torch.compile` hashable
|
As reported in https://github.com/huggingface/datasets/issues/5819, hashing functions/transforms that reference a model, or a function, optimized with `torch.compile` currently fails due to them not being picklable (the concrete error can be found in the linked issue).
The solutions to consider:
1. hashing/pickling the original, uncompiled version of a compiled model/function (attributes `_orig_mod`/`_torchdynamo_orig_callable`) (less precise than the 2nd option as it ignores the other params of `torch.compute`)
2. wait for https://github.com/pytorch/pytorch/issues/101107 to be resolved
|
closed
|
https://github.com/huggingface/datasets/issues/5839
| 2023-05-10T20:02:08
| 2023-11-28T16:29:33
| 2023-11-28T16:29:33
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,703,210,848
| 5,838
|
Streaming support for `load_from_disk`
|
### Feature request
Support for streaming datasets stored in object stores in `load_from_disk`.
### Motivation
The `load_from_disk` function supports fetching datasets stored in object stores such as `s3`. In many cases, the datasets that are stored in object stores are very large and being able to stream the data from the buckets becomes essential.
### Your contribution
I'd be happy to contribute this feature if I could get the guidance on how to do so.
|
closed
|
https://github.com/huggingface/datasets/issues/5838
| 2023-05-10T06:25:22
| 2024-10-28T14:19:44
| 2023-05-12T09:37:45
|
{
"login": "Nilabhra",
"id": 5437792,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,703,019,816
| 5,837
|
Use DeepSpeed load myself " .csv " dataset.
|
### Describe the bug
When I use DeepSpeed train a model with my own " XXX.csv" dataset I got the follow question:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py", line 1767, in load_dataset
builder_instance = load_dataset_builder(
File "/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py", line 1498, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py", line 1217, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at /home/fm001/hzl/Data/qa.csv/qa.csv.py or any data file in the same directory.
### Steps to reproduce the bug
my code is :
from datasets import load_dataset
mydata = load_dataset("/home/fm001/hzl/Data/qa.csv")
### Expected behavior
。。。
### Environment info
。。。
|
open
|
https://github.com/huggingface/datasets/issues/5837
| 2023-05-10T02:39:28
| 2023-05-15T03:51:36
| null |
{
"login": "LanShanPi",
"id": 58167546,
"type": "User"
}
|
[] | false
|
[] |
1,702,773,316
| 5,836
|
[docs] Custom decoding transforms
|
Adds custom decoding transform solution to the docs to fix #5782.
|
closed
|
https://github.com/huggingface/datasets/pull/5836
| 2023-05-09T21:21:41
| 2023-05-15T07:36:12
| 2023-05-10T20:23:03
|
{
"login": "stevhliu",
"id": 59462357,
"type": "User"
}
|
[] | true
|
[] |
1,702,522,620
| 5,835
|
Always set nullable fields in the writer
|
This fixes loading of e.g. parquet data with non-nullable fields.
Indeed `datasets.Features` doesn't support non-nullable fields, which can lead to data not concatenable due to arrow schema mismatch.
|
closed
|
https://github.com/huggingface/datasets/pull/5835
| 2023-05-09T18:16:59
| 2023-05-23T16:10:29
| 2023-05-19T13:04:30
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,702,448,892
| 5,834
|
Is uint8 supported?
|
### Describe the bug
I expect the dataset to store the data in the `uint8` data type, but it's returning `int64` instead.
While I've found that `datasets` doesn't yet support float16 (https://github.com/huggingface/datasets/issues/4981), I'm wondering if this is the case for other data types as well.
Is there a way to store vector data as `uint8` and then upload it to the hub?
### Steps to reproduce the bug
```python
from datasets import Features, Dataset, Sequence, Value
import numpy as np
dataset = Dataset.from_dict(
{"vector": [np.array([0, 1, 2], dtype=np.uint8)]}, features=Features({"vector": Sequence(Value("uint8"))})
).with_format("numpy")
print(dataset[0]["vector"].dtype)
```
### Expected behavior
Expected: `uint8`
Actual: `int64`
### Environment info
- `datasets` version: 2.12.0
- Platform: macOS-12.1-x86_64-i386-64bit
- Python version: 3.8.12
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5834
| 2023-05-09T17:31:13
| 2023-05-13T05:04:21
| 2023-05-13T05:04:21
|
{
"login": "ryokan0123",
"id": 17979572,
"type": "User"
}
|
[] | false
|
[] |
1,702,280,682
| 5,833
|
Unable to push dataset - `create_pr` problem
|
### Describe the bug
I can't upload to the hub the dataset I manually created locally (Image dataset). I have a problem when using the method `.push_to_hub` which asks for a `create_pr` attribute which is not compatible.
### Steps to reproduce the bug
here what I have:
```python
dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts")
```
Output:
```python
Pushing split train to the Hub.
Pushing dataset shards to the dataset hub: 0%| | 0/2 [00:00<?, ?it/s]
Creating parquet from Arrow format: 0%| | 0/3 [00:00<?, ?ba/s]
Creating parquet from Arrow format: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 12.70ba/s]
Pushing dataset shards to the dataset hub: 0%| | 0/2 [00:01<?, ?it/s]
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py:259, in hf_raise_for_status(response, endpoint_name)
258 try:
--> 259 response.raise_for_status()
260 except HTTPError as e:
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/agomberto/FrenchCensus-handwritten-texts/commit/main
The above exception was the direct cause of the following exception:
HfHubHTTPError Traceback (most recent call last)
Cell In[7], line 1
----> 1 dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts")
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/dataset_dict.py:1583, in DatasetDict.push_to_hub(self, repo_id, private, token, branch, max_shard_size, num_shards, embed_external_files)
1581 logger.warning(f"Pushing split {split} to the Hub.")
1582 # The split=key needs to be removed before merging
-> 1583 repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(
1584 repo_id,
1585 split=split,
1586 private=private,
1587 token=token,
1588 branch=branch,
1589 max_shard_size=max_shard_size,
1590 num_shards=num_shards.get(split),
1591 embed_external_files=embed_external_files,
1592 )
1593 total_uploaded_size += uploaded_size
1594 total_dataset_nbytes += dataset_nbytes
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/arrow_dataset.py:5275, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, num_shards, embed_external_files)
5273 shard.to_parquet(buffer)
5274 uploaded_size += buffer.tell()
-> 5275 _retry(
5276 api.upload_file,
5277 func_kwargs={
5278 "path_or_fileobj": buffer.getvalue(),
5279 "path_in_repo": shard_path_in_repo,
5280 "repo_id": repo_id,
5281 "token": token,
5282 "repo_type": "dataset",
5283 "revision": branch,
5284 },
5285 exceptions=HTTPError,
5286 status_codes=[504],
5287 base_wait_time=2.0,
5288 max_retries=5,
5289 max_wait_time=20.0,
5290 )
5291 shards_path_in_repo.append(shard_path_in_repo)
5293 # Cleanup to remove unused files
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/utils/file_utils.py:285, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
283 except exceptions as err:
284 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
--> 285 raise err
286 else:
287 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/utils/file_utils.py:282, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
280 while True:
281 try:
--> 282 return func(*func_args, **func_kwargs)
283 except exceptions as err:
284 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:120, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
117 if check_use_auth_token:
118 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 120 return fn(*args, **kwargs)
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/hf_api.py:2998, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, commit_message, commit_description, create_pr, parent_commit)
2990 commit_message = (
2991 commit_message if commit_message is not None else f"Upload {path_in_repo} with huggingface_hub"
2992 )
2993 operation = CommitOperationAdd(
2994 path_or_fileobj=path_or_fileobj,
2995 path_in_repo=path_in_repo,
2996 )
-> 2998 commit_info = self.create_commit(
2999 repo_id=repo_id,
3000 repo_type=repo_type,
3001 operations=[operation],
3002 commit_message=commit_message,
3003 commit_description=commit_description,
3004 token=token,
3005 revision=revision,
3006 create_pr=create_pr,
3007 parent_commit=parent_commit,
3008 )
3010 if commit_info.pr_url is not None:
3011 revision = quote(_parse_revision_from_pr_url(commit_info.pr_url), safe="")
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:120, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
117 if check_use_auth_token:
118 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 120 return fn(*args, **kwargs)
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/hf_api.py:2548, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit)
2546 try:
2547 commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params)
-> 2548 hf_raise_for_status(commit_resp, endpoint_name="commit")
2549 except RepositoryNotFoundError as e:
2550 e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)
File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py:301, in hf_raise_for_status(response, endpoint_name)
297 raise BadRequestError(message, response=response) from e
299 # Convert `HTTPError` into a `HfHubHTTPError` to display request information
300 # as well (request id and/or server error message)
--> 301 raise HfHubHTTPError(str(e), response=response) from e
HfHubHTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/agomberto/FrenchCensus-handwritten-texts/commit/main (Request ID: Root=1-645a66bf-255ad91602a6404e6cb70fba)
Forbidden: pass `create_pr=1` as a query parameter to create a Pull Request
```
And then when I do
```python
dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts", create_pr=1)
```
I get
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[8], line 1
----> 1 dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts", create_pr=1)
TypeError: push_to_hub() got an unexpected keyword argument 'create_pr'
```
### Expected behavior
I would like to have the dataset updloaded [here](https://huggingface.co/datasets/agomberto/FrenchCensus-handwritten-texts).
### Environment info
```bash
- `datasets` version: 2.12.0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.8.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 1.5.3
```
|
closed
|
https://github.com/huggingface/datasets/issues/5833
| 2023-05-09T15:32:55
| 2023-10-24T18:22:29
| 2023-10-24T18:22:29
|
{
"login": "agombert",
"id": 17645711,
"type": "User"
}
|
[] | false
|
[] |
1,702,135,336
| 5,832
|
404 Client Error: Not Found for url: https://huggingface.co/api/models/bert-large-cased
|
### Describe the bug
Running [Bert-Large-Cased](https://huggingface.co/bert-large-cased) model causes `HTTPError`, with the following traceback-
```
HTTPError Traceback (most recent call last)
<ipython-input-6-5c580443a1ad> in <module>
----> 1 tokenizer = BertTokenizer.from_pretrained('bert-large-cased')
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs)
1646 # At this point pretrained_model_name_or_path is either a directory or a model identifier name
1647 fast_tokenizer_file = get_fast_tokenizer_file(
-> 1648 pretrained_model_name_or_path, revision=revision, use_auth_token=use_auth_token
1649 )
1650 additional_files_names = {
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in get_fast_tokenizer_file(path_or_repo, revision, use_auth_token)
3406 """
3407 # Inspect all files from the repo/folder.
-> 3408 all_files = get_list_of_files(path_or_repo, revision=revision, use_auth_token=use_auth_token)
3409 tokenizer_files_map = {}
3410 for file_name in all_files:
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/file_utils.py in get_list_of_files(path_or_repo, revision, use_auth_token)
1685 token = None
1686 model_info = HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).model_info(
-> 1687 path_or_repo, revision=revision, token=token
1688 )
1689 return [f.rfilename for f in model_info.siblings]
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/huggingface_hub/hf_api.py in model_info(self, repo_id, revision, token)
246 )
247 r = requests.get(path, headers=headers)
--> 248 r.raise_for_status()
249 d = r.json()
250 return ModelInfo(**d)
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/requests/models.py in raise_for_status(self)
951
952 if http_error_msg:
--> 953 raise HTTPError(http_error_msg, response=self)
954
955 def close(self):
HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/models/bert-large-cased
```
I have also tried running in offline mode, as [discussed here](https://huggingface.co/docs/transformers/installation#offline-mode)
```
HF_DATASETS_OFFLINE=1
TRANSFORMERS_OFFLINE=1
```
### Steps to reproduce the bug
1. `from transformers import BertTokenizer, BertModel`
2. `tokenizer = BertTokenizer.from_pretrained('bert-large-cased')`
### Expected behavior
Run without the HTTP error.
### Environment info
| # Name | Version | Build | Channel | |
|--------------------|------------|-----------------------------|---------|---|
| _libgcc_mutex | 0.1 | main | | |
| _openmp_mutex | 4.5 | 1_gnu | | |
| _pytorch_select | 0.1 | cpu_0 | | |
| appdirs | 1.4.4 | pypi_0 | pypi | |
| backcall | 0.2.0 | pypi_0 | pypi | |
| blas | 1.0 | mkl | | |
| bzip2 | 1.0.8 | h7b6447c_0 | | |
| ca-certificates | 2021.7.5 | h06a4308_1 | | |
| certifi | 2021.5.30 | py37h06a4308_0 | | |
| cffi | 1.14.6 | py37h400218f_0 | | |
| charset-normalizer | 2.0.3 | pypi_0 | pypi | |
| click | 8.0.1 | pypi_0 | pypi | |
| colorama | 0.4.4 | pypi_0 | pypi | |
| cudatoolkit | 11.1.74 | h6bb024c_0 | nvidia | |
| cycler | 0.11.0 | pypi_0 | pypi | |
| decorator | 5.0.9 | pypi_0 | pypi | |
| docker-pycreds | 0.4.0 | pypi_0 | pypi | |
| docopt | 0.6.2 | pypi_0 | pypi | |
| dominate | 2.6.0 | pypi_0 | pypi | |
| ffmpeg | 4.3 | hf484d3e_0 | pytorch | |
| filelock | 3.0.12 | pypi_0 | pypi | |
| fonttools | 4.38.0 | pypi_0 | pypi | |
| freetype | 2.10.4 | h5ab3b9f_0 | | |
| gitdb | 4.0.7 | pypi_0 | pypi | |
| gitpython | 3.1.18 | pypi_0 | pypi | |
| gmp | 6.2.1 | h2531618_2 | | |
| gnutls | 3.6.15 | he1e5248_0 | | |
| huggingface-hub | 0.0.12 | pypi_0 | pypi | |
| humanize | 3.10.0 | pypi_0 | pypi | |
| idna | 3.2 | pypi_0 | pypi | |
| importlib-metadata | 4.6.1 | pypi_0 | pypi | |
| intel-openmp | 2019.4 | 243 | | |
| ipdb | 0.13.9 | pypi_0 | pypi | |
| ipython | 7.25.0 | pypi_0 | pypi | |
| ipython-genutils | 0.2.0 | pypi_0 | pypi | |
| jedi | 0.18.0 | pypi_0 | pypi | |
| joblib | 1.0.1 | pypi_0 | pypi | |
| jpeg | 9b | h024ee3a_2 | | |
| jsonpickle | 1.5.2 | pypi_0 | pypi | |
| kiwisolver | 1.4.4 | pypi_0 | pypi | |
| lame | 3.100 | h7b6447c_0 | | |
| lcms2 | 2.12 | h3be6417_0 | | |
| ld_impl_linux-64 | 2.35.1 | h7274673_9 | | |
| libffi | 3.3 | he6710b0_2 | | |
| libgcc-ng | 9.3.0 | h5101ec6_17 | | |
| libgomp | 9.3.0 | h5101ec6_17 | | |
| libiconv | 1.15 | h63c8f33_5 | | |
| libidn2 | 2.3.2 | h7f8727e_0 | | |
| libmklml | 2019.0.5 | 0 | | |
| libpng | 1.6.37 | hbc83047_0 | | |
| libstdcxx-ng | 9.3.0 | hd4cf53a_17 | | |
| libtasn1 | 4.16.0 | h27cfd23_0 | | |
| libtiff | 4.2.0 | h85742a9_0 | | |
| libunistring | 0.9.10 | h27cfd23_0 | | |
| libuv | 1.40.0 | h7b6447c_0 | | |
| libwebp-base | 1.2.0 | h27cfd23_0 | | |
| lz4-c | 1.9.3 | h2531618_0 | | |
| matplotlib | 3.5.3 | pypi_0 | pypi | |
| matplotlib-inline | 0.1.2 | pypi_0 | pypi | |
| mergedeep | 1.3.4 | pypi_0 | pypi | |
| mkl | 2020.2 | 256 | | |
| mkl-service | 2.3.0 | py37he8ac12f_0 | | |
| mkl_fft | 1.3.0 | py37h54f3939_0 | | |
| mkl_random | 1.1.1 | py37h0573a6f_0 | | |
| msgpack | 1.0.2 | pypi_0 | pypi | |
| munch | 2.5.0 | pypi_0 | pypi | |
| ncurses | 6.2 | he6710b0_1 | | |
| nettle | 3.7.3 | hbbd107a_1 | | |
| ninja | 1.10.2 | hff7bd54_1 | | |
| nltk | 3.8.1 | pypi_0 | pypi | |
| numpy | 1.19.2 | py37h54aff64_0 | | |
| numpy-base | 1.19.2 | py37hfa32c7d_0 | | |
| olefile | 0.46 | py37_0 | | |
| openh264 | 2.1.0 | hd408876_0 | | |
| openjpeg | 2.3.0 | h05c96fa_1 | | |
| openssl | 1.1.1k | h27cfd23_0 | | |
| packaging | 21.0 | pypi_0 | pypi | |
| pandas | 1.3.1 | pypi_0 | pypi | |
| parso | 0.8.2 | pypi_0 | pypi | |
| pathtools | 0.1.2 | pypi_0 | pypi | |
| pexpect | 4.8.0 | pypi_0 | pypi | |
| pickleshare | 0.7.5 | pypi_0 | pypi | |
| pillow | 8.3.1 | py37h2c7a002_0 | | |
| pip | 21.1.3 | py37h06a4308_0 | | |
| prompt-toolkit | 3.0.19 | pypi_0 | pypi | |
| protobuf | 4.21.12 | pypi_0 | pypi | |
| psutil | 5.8.0 | pypi_0 | pypi | |
| ptyprocess | 0.7.0 | pypi_0 | pypi | |
| py-cpuinfo | 8.0.0 | pypi_0 | pypi | |
| pycparser | 2.20 | py_2 | | |
| pygments | 2.9.0 | pypi_0 | pypi | |
| pyparsing | 2.4.7 | pypi_0 | pypi | |
| python | 3.7.10 | h12debd9_4 | | |
| python-dateutil | 2.8.2 | pypi_0 | pypi | |
| pytorch | 1.9.0 | py3.7_cuda11.1_cudnn8.0.5_0 | pytorch | |
| pytz | 2021.1 | pypi_0 | pypi | |
| pyyaml | 5.4.1 | pypi_0 | pypi | |
| readline | 8.1 | h27cfd23_0 | | |
| regex | 2022.10.31 | pypi_0 | pypi | |
| requests | 2.26.0 | pypi_0 | pypi | |
| sacred | 0.8.2 | pypi_0 | pypi | |
| sacremoses | 0.0.45 | pypi_0 | pypi | |
| scikit-learn | 0.24.2 | pypi_0 | pypi | |
| scipy | 1.7.0 | pypi_0 | pypi | |
| sentry-sdk | 1.15.0 | pypi_0 | pypi | |
| setproctitle | 1.3.2 | pypi_0 | pypi | |
| setuptools | 52.0.0 | py37h06a4308_0 | | |
| six | 1.16.0 | pyhd3eb1b0_0 | | |
| smmap | 4.0.0 | pypi_0 | pypi | |
| sqlite | 3.36.0 | hc218d9a_0 | | |
| threadpoolctl | 2.2.0 | pypi_0 | pypi | |
| tk | 8.6.10 | hbc83047_0 | | |
| tokenizers | 0.10.3 | pypi_0 | pypi | |
| toml | 0.10.2 | pypi_0 | pypi | |
| torchaudio | 0.9.0 | py37 | pytorch | |
| torchvision | 0.10.0 | py37_cu111 | pytorch | |
| tqdm | 4.61.2 | pypi_0 | pypi | |
| traitlets | 5.0.5 | pypi_0 | pypi | |
| transformers | 4.9.1 | pypi_0 | pypi | |
| typing-extensions | 3.10.0.0 | hd3eb1b0_0 | | |
| typing_extensions | 3.10.0.0 | pyh06a4308_0 | | |
| urllib3 | 1.26.14 | pypi_0 | pypi | |
| wandb | 0.13.10 | pypi_0 | pypi | |
| wcwidth | 0.2.5 | pypi_0 | pypi | |
| wheel | 0.36.2 | pyhd3eb1b0_0 | | |
| wrapt | 1.12.1 | pypi_0 | pypi | |
| xz | 5.2.5 | h7b6447c_0 | | |
| zipp | 3.5.0 | pypi_0 | pypi | |
| zlib | 1.2.11 | h7b6447c_3 | | |
| zstd | 1.4.9 | haebb681_0 | | |
|
closed
|
https://github.com/huggingface/datasets/issues/5832
| 2023-05-09T14:14:59
| 2023-05-09T14:25:59
| 2023-05-09T14:25:59
|
{
"login": "varungupta31",
"id": 51288316,
"type": "User"
}
|
[] | false
|
[] |
1,701,813,835
| 5,831
|
[Bug]504 Server Error when loading dataset which was already cached
|
### Describe the bug
I have already cached the dataset using:
```
dataset = load_dataset("databricks/databricks-dolly-15k",
cache_dir="/mnt/data/llm/datasets/databricks-dolly-15k")
```
After that, I tried to load it again using the same machine, I got this error:
```
Traceback (most recent call last):
File "/mnt/home/llm/pythia/train.py", line 16, in <module>
dataset = load_dataset("databricks/databricks-dolly-15k",
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1773, in load_dataset
builder_instance = load_dataset_builder(
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1502, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1219, in dataset_module_factory
raise e1 from None
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1186, in dataset_module_factory
raise e
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1160, in dataset_module_factory
dataset_info = hf_api.dataset_info(
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 120, in _inner_fn
return fn(*args, **kwargs)
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 1667, in dataset_info
hf_raise_for_status(r)
File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py", line 301, in hf_raise_for_status
raise HfHubHTTPError(str(e), response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/databricks/databricks-dolly-15k
```
### Steps to reproduce the bug
1. cache the databrick-dolly-15k dataset using load_dataset, setting a cache_dir
2. use load_dataset again, setting the same cache_dir
### Expected behavior
Dataset loaded succuessfully.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-4.18.0-372.16.1.el8_6.x86_64-x86_64-with-glibc2.27
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
open
|
https://github.com/huggingface/datasets/issues/5831
| 2023-05-09T10:31:07
| 2023-05-10T01:48:20
| null |
{
"login": "SingL3",
"id": 20473466,
"type": "User"
}
|
[] | false
|
[] |
1,701,451,399
| 5,830
|
Debug windows #2
| null |
closed
|
https://github.com/huggingface/datasets/pull/5830
| 2023-05-09T06:40:34
| 2023-05-09T06:40:47
| 2023-05-09T06:40:47
|
{
"login": "HyukjinKwon",
"id": 6477701,
"type": "User"
}
|
[] | true
|
[] |
1,699,958,189
| 5,829
|
(mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64'))
|
### Describe the bug
M2 MBP can't run
```python
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
### Steps to reproduce the bug
1. Use M2 MBP
2. Python 3.10.10 from pyenv
3. Run
```
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
### Expected behavior
Be able to run normally
### Environment info
```
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
OSX: 13.2
CPU: M2
|
closed
|
https://github.com/huggingface/datasets/issues/5829
| 2023-05-08T10:07:14
| 2023-06-30T11:39:14
| 2023-05-09T00:46:42
|
{
"login": "elcolie",
"id": 18206728,
"type": "User"
}
|
[] | false
|
[] |
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