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https://api.github.com/repos/huggingface/datasets/issues/6718 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6718/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6718/comments | https://api.github.com/repos/huggingface/datasets/issues/6718/events | https://github.com/huggingface/datasets/pull/6718 | 2,169,468,488 | PR_kwDODunzps5ouwwE | 6,718 | Fix concurrent script loading with force_redownload | {
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} | [] | closed | false | null | [] | null | 2 | 2024-03-05T15:04:20 | 2024-03-07T14:05:53 | 2024-03-07T13:58:04 | MEMBER | null | I added `lock_importable_file` in `get_dataset_builder_class` and `extend_dataset_builder_for_streaming` to fix the issue, and I also added a test
cc @clefourrier | {
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https://api.github.com/repos/huggingface/datasets/issues/6717 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6717/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6717/comments | https://api.github.com/repos/huggingface/datasets/issues/6717/events | https://github.com/huggingface/datasets/issues/6717 | 2,168,726,432 | I_kwDODunzps6BRCOg | 6,717 | `remove_columns` method used with a streaming enable dataset mode produces a LibsndfileError on multichannel audio | {
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} | [] | open | false | null | [] | null | 2 | 2024-03-05T09:33:26 | 2024-08-14T17:54:20 | null | NONE | null | ### Describe the bug
When loading a HF dataset in streaming mode and removing some columns, it is impossible to load a sample if the audio contains more than one channel. I have the impression that the time axis and channels are swapped or concatenated.
### Steps to reproduce the bug
Minimal error code:
```python
from datasets import load_dataset
dataset_name = "zinc75/Vibravox_dummy"
config_name = "BWE_Larynx_microphone"
# if we use "ASR_Larynx_microphone" subset which is a monochannel audio, no error is thrown.
dataset = load_dataset(
path=dataset_name, name=config_name, split="train", streaming=True
)
dataset = dataset.remove_columns(["sensor_id"])
# dataset = dataset.map(lambda x:x, remove_columns=["sensor_id"])
# The commented version does not produce an error, but loses the dataset features.
sample = next(iter(dataset))
```
Error:
```
Traceback (most recent call last):
File "/home/julien/Bureau/github/vibravox/tmp.py", line 15, in <module>
sample = next(iter(dataset))
^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1392, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1080, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1889, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in <dictcomp>
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1300, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/audio.py", line 98, in encode_example
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 343, in write
with SoundFile(file, 'w', samplerate, channels,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 658, in __init__
self._file = self._open(file, mode_int, closefd)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 1216, in _open
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7fd795d24680>: Format not recognised.
Process finished with exit code 1
```
### Expected behavior
I would expect this code to run without error.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-21-generic-x86_64-with-glibc2.35
- Python version: 3.11.0
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6716 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6716/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6716/comments | https://api.github.com/repos/huggingface/datasets/issues/6716/events | https://github.com/huggingface/datasets/issues/6716 | 2,168,706,558 | I_kwDODunzps6BQ9X- | 6,716 | Non-deterministic `Dataset.builder_name` value | {
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} | [] | closed | false | null | [] | null | 6 | 2024-03-05T09:23:21 | 2024-03-19T07:58:14 | 2024-03-19T07:58:14 | NONE | null | ### Describe the bug
I'm not sure if this is a bug, but `print(ds.builder_name)` in the following code sometimes prints out `rotten_tomatoes` instead of `parquet`:
```python
import datasets
for _ in range(100):
ds = datasets.load_dataset("rotten_tomatoes", split="train")
print(ds.builder_name) # prints out "rotten_tomatoes" sometimes instead of "parquet"
```
Output:
```
...
parquet
parquet
parquet
rotten_tomatoes
parquet
parquet
parquet
...
```
Here's a reproduction using GitHub Actions:
https://github.com/mlflow/mlflow/actions/runs/8153247984/job/22284263613?pr=11329#step:12:241
One of our tests is flaky because `builder_name` is not deterministic.
### Steps to reproduce the bug
1. Run the code above.
### Expected behavior
Always prints out `parquet`?
### Environment info
```
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-1015-azure-x86_64-with-glibc2.34
- Python version: 3.8.18
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2024.2.0
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https://api.github.com/repos/huggingface/datasets/issues/6715 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6715/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6715/comments | https://api.github.com/repos/huggingface/datasets/issues/6715/events | https://github.com/huggingface/datasets/pull/6715 | 2,167,747,095 | PR_kwDODunzps5oo36i | 6,715 | Fix sliced ConcatenationTable pickling with mixed schemas vertically | {
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} | [] | closed | false | null | [] | null | 2 | 2024-03-04T21:02:07 | 2024-03-05T11:23:05 | 2024-03-05T11:17:04 | MEMBER | null | A sliced + pickled ConcatenationTable could end up with a different schema than the original schema, if the slice only contains blocks with only a subset of the columns.
This can lead to issues when saving datasets from a concatenation of datasets with mixed schemas
Reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595 | {
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https://api.github.com/repos/huggingface/datasets/issues/6714 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6714/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6714/comments | https://api.github.com/repos/huggingface/datasets/issues/6714/events | https://github.com/huggingface/datasets/pull/6714 | 2,167,569,080 | PR_kwDODunzps5ooQd2 | 6,714 | Expand no-code dataset info with datasets-server info | {
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} | [] | closed | false | null | [] | null | 2 | 2024-03-04T19:18:10 | 2024-03-04T20:28:30 | 2024-03-04T20:22:15 | COLLABORATOR | null | E.g., to have info about a dataset's number of examples for more informative TQDM bars. | {
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https://api.github.com/repos/huggingface/datasets/issues/6713 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6713/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6713/comments | https://api.github.com/repos/huggingface/datasets/issues/6713/events | https://github.com/huggingface/datasets/pull/6713 | 2,166,797,560 | PR_kwDODunzps5olmqh | 6,713 | Bump huggingface-hub lower version to 0.21.2 | {
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} | [] | closed | false | null | [] | null | 4 | 2024-03-04T13:00:52 | 2024-03-04T18:14:03 | 2024-03-04T18:06:05 | MEMBER | null | This should fix the version compatibility issue when using `huggingface_hub` < 0.21.2 and latest fsspec (>=2023.12.0).
See my comment: https://github.com/huggingface/datasets/pull/6687#issuecomment-1976493336
>> EDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`
>
>Please note that people using `huggingface_hub` < 0.21.2 and latest `fsspec` will have issues when using `datasets`:
>- https://github.com/huggingface/lighteval/actions/runs/8139147047/job/22241658122?pr=86
>- https://github.com/huggingface/lighteval/pull/84
CC: @clefourrier
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https://api.github.com/repos/huggingface/datasets/issues/6712 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6712/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6712/comments | https://api.github.com/repos/huggingface/datasets/issues/6712/events | https://github.com/huggingface/datasets/pull/6712 | 2,166,588,373 | PR_kwDODunzps5ok4VF | 6,712 | fix CastError pickling | {
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} | [] | closed | false | null | [] | null | 2 | 2024-03-04T11:14:18 | 2024-03-04T20:23:47 | 2024-03-04T20:17:17 | MEMBER | null | reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595 | {
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https://api.github.com/repos/huggingface/datasets/issues/6711 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6711/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6711/comments | https://api.github.com/repos/huggingface/datasets/issues/6711/events | https://github.com/huggingface/datasets/pull/6711 | 2,165,507,817 | PR_kwDODunzps5ohM1a | 6,711 | 3x Faster Text Preprocessing | {
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} | [] | open | false | null | [] | null | 3 | 2024-03-03T19:03:04 | 2024-06-26T06:28:14 | null | NONE | null | I was preparing some datasets for AI training and noticed that `datasets` by HuggingFace uses the conventional `open` mechanism to read the file and split it into chunks. I thought it can be significantly accelerated, and [started with a benchmark](https://gist.github.com/ashvardanian/55c2052e9f78b05b8d614aa90cb12347):
```sh
$ pip install --upgrade --force-reinstall datasets
$ python benchmark_huggingface_datasets.py xlsum.csv
Generating train split: 1004598 examples [00:47, 21116.16 examples/s]
Time taken to load the dataset: 48.66838526725769 seconds
Time taken to chunk the dataset into parts of size 10000: 0.11466407775878906 seconds
Total time taken: 48.78304934501648 seconds
```
For benchmarks I've used a [large CSV file with mixed UTF-8 content](https://github.com/ashvardanian/StringZilla/blob/main/CONTRIBUTING.md#benchmarking-datasets), most common in modern large-scale pre-training pipelines. I've later patched the `datasets` library to use `stringzilla`, which resulted in significantly lower memory consumption and in 2.9x throughput improvement on the AWS `r7iz` instances. That's using slow SSDs mounted over the network. Performance on local SSDs on something like a DGX-H100 should be even higher:
```sh
$ pip install -e .
$ python benchmark_huggingface_datasets.py xlsum.csv
Generating train split: 1004598 examples [00:15, 64529.90 examples/s]
Time taken to load the dataset: 16.45028805732727 seconds
Time taken to chunk the dataset into parts of size 10000: 0.1291060447692871 seconds
Total time taken: 16.579394102096558 seconds
```
I've already [pushed the patches to my fork](https://github.com/ashvardanian/datasets/tree/faster-text-parsers), and would love to contribute them to the upstream repository.
---
All the tests pass, but they leave a couple of important questions open. The default Python `open(..., newline=None)` uses universal newlines, where `\n`, `\r`, and `\r\n` are all converted to `\n` on the fly. I am not sure if its a good idea for a general purpose dataset preparation pipeline?
I can simulate the same behavior (which I don't yet do) for `"line"` splitter. Adjusting it for `"paragraph"`-splitter would be harder. Should we stick exactly to the old Pythonic behavior or stay closer to how C and other programming languages do that? | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6710 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6710/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6710/comments | https://api.github.com/repos/huggingface/datasets/issues/6710/events | https://github.com/huggingface/datasets/pull/6710 | 2,164,781,564 | PR_kwDODunzps5oe4ov | 6,710 | Persist IterableDataset epoch in workers | {
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} | [] | closed | false | null | [] | null | 2 | 2024-03-02T12:08:50 | 2024-07-01T17:51:25 | 2024-07-01T17:45:30 | MEMBER | null | Use shared memory for the IterableDataset epoch.
This way calling `ds.set_epoch()` in the main process will update the epoch in the DataLoader workers as well.
This is useful especially because the epoch is used to compute the `effective_seed` used for shuffling.
I used torch's shared memory in case users want to send dataset copies without shared memory using pickle. I also find it easier to use than using `multiprocessing.shared_memory` than requires unlinking only in the main process, or `mp.Value` that is not picklable.
close https://github.com/huggingface/datasets/issues/6673
cc @rwightman | {
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https://api.github.com/repos/huggingface/datasets/issues/6705 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6705/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6705/comments | https://api.github.com/repos/huggingface/datasets/issues/6705/events | https://github.com/huggingface/datasets/pull/6705 | 2,163,768,640 | PR_kwDODunzps5obdbY | 6,705 | Fix data_files when passing data_dir | {
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} | [] | closed | false | null | [] | null | 2 | 2024-03-01T16:38:53 | 2024-03-01T18:59:06 | 2024-03-01T18:52:49 | MEMBER | null | This code should not return empty data files
```python
from datasets import load_dataset_builder
revision = "3d406e70bc21c3ca92a9a229b4c6fc3ed88279fd"
b = load_dataset_builder("bigcode/the-stack-v2-dedup", data_dir="data/Dockerfile", revision=revision)
print(b.config.data_files)
```
Previously it would return no data files because it would apply the YAML `data_files: data/**/train-*` pattern to this directory
cc @anton-l | {
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https://api.github.com/repos/huggingface/datasets/issues/6704 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6704/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6704/comments | https://api.github.com/repos/huggingface/datasets/issues/6704/events | https://github.com/huggingface/datasets/pull/6704 | 2,163,752,391 | PR_kwDODunzps5obZyj | 6,704 | Improve default patterns resolution | {
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} | [] | closed | false | null | [] | null | 11 | 2024-03-01T16:31:25 | 2024-04-23T09:43:09 | 2024-03-15T15:22:03 | COLLABORATOR | null | Separate the default patterns that match directories from the ones matching files and ensure directories are checked first (reverts the change from https://github.com/huggingface/datasets/pull/6244, which merged these patterns). Also, ensure that the glob patterns do not overlap to avoid duplicates in the result.
Additionally, replace `get_fs_token_paths` with `url_to_fs` to avoid [unnecessary glob calls](https://github.com/fsspec/filesystem_spec/blob/14dce8ca78f7aa509a20edb263bff83a7760c24d/fsspec/core.py#L655-L656).
fix https://github.com/huggingface/datasets/issues/6259
fix https://github.com/huggingface/datasets/issues/6272 | {
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https://api.github.com/repos/huggingface/datasets/issues/6703 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6703/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6703/comments | https://api.github.com/repos/huggingface/datasets/issues/6703/events | https://github.com/huggingface/datasets/issues/6703 | 2,163,250,590 | I_kwDODunzps6A8JWe | 6,703 | Unable to load dataset that was saved with `save_to_disk` | {
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} | [] | closed | false | null | [] | null | 8 | 2024-03-01T11:59:56 | 2024-03-04T13:46:20 | 2024-03-04T13:46:20 | NONE | null | ### Describe the bug
I get the following error message: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.
### Steps to reproduce the bug
1. Save a dataset with `save_to_disk`
2. Try to load it with `load_datasets`
### Expected behavior
I am able to load the dataset again with `load_datasets` which most packages uses over `load_from_disk`. I want to have a workaround that allows me to create the same indexing that `push_to_hub` creates for you before using `save_to_disk` - how can that be achieved?
### Environment info
datasets 2.17.1, python 3.10 | {
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https://api.github.com/repos/huggingface/datasets/issues/6702 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6702/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6702/comments | https://api.github.com/repos/huggingface/datasets/issues/6702/events | https://github.com/huggingface/datasets/issues/6702 | 2,161,938,484 | I_kwDODunzps6A3JA0 | 6,702 | Push samples to dataset on hub without having the dataset locally | {
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] | closed | false | null | [] | null | 2 | 2024-02-29T19:17:12 | 2024-03-08T21:08:38 | 2024-03-08T21:08:38 | NONE | null | ### Feature request
Say I have the following code:
```
from datasets import Dataset
import pandas as pd
new_data = {
"column_1": ["value1", "value2"],
"column_2": ["value3", "value4"],
}
df_new = pd.DataFrame(new_data)
dataset_new = Dataset.from_pandas(df_new)
# add these samples to a remote dataset
```
It would be great to have a way to push dataset_new to a remote dataset that respects the same schema. This way one would not have to do the following:
```
from datasets import load_dataset
dataset = load_dataset('username/dataset_name', use_auth_token='your_hf_token_here')
updated_dataset = dataset['train'].concatenate(dataset_new)
updated_dataset.push_to_hub('username/dataset_name', use_auth_token='your_hf_token_here')
```
### Motivation
No need to download the dataset.
### Your contribution
Maybe this feature already exists, didnt see it though. I do not have the expertise to do this. | {
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https://api.github.com/repos/huggingface/datasets/issues/6701 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6701/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6701/comments | https://api.github.com/repos/huggingface/datasets/issues/6701/events | https://github.com/huggingface/datasets/pull/6701 | 2,161,448,017 | PR_kwDODunzps5oTfO_ | 6,701 | Base parquet batch_size on parquet row group size | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-29T14:53:01 | 2024-02-29T15:15:18 | 2024-02-29T15:08:55 | MEMBER | null | This allows to stream datasets like [Major-TOM/Core-S2L2A](https://huggingface.co/datasets/Major-TOM/Core-S2L2A) which have row groups with few rows (one row is ~10MB). Previously the cold start would take a lot of time and OOM because it would download many row groups before yielding the first example.
I tried on OpenOrca and imagenet-hard and it does't affect overall throughput.
Even if the overall throughput doesn't change for datasets like imagenet-hard with big rows, note that it does create shorter and more frequent pauses to download the next row group. Though I find it fine because previously the pauses were less frequent but very long (downloading multiple row groups at a time) | {
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https://api.github.com/repos/huggingface/datasets/issues/6700 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6700/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6700/comments | https://api.github.com/repos/huggingface/datasets/issues/6700/events | https://github.com/huggingface/datasets/issues/6700 | 2,158,871,038 | I_kwDODunzps6ArcH- | 6,700 | remove_columns is not in-place but the doc shows it is in-place | {
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} | [] | closed | false | null | [] | null | 3 | 2024-02-28T12:36:22 | 2024-04-02T17:15:28 | 2024-04-02T17:15:28 | NONE | null | ### Describe the bug
The doc of `datasets` v2.17.0/v2.17.1 shows that `remove_columns` is in-place. [link](https://huggingface.co/docs/datasets/v2.17.1/en/package_reference/main_classes#datasets.DatasetDict.remove_columns)
In the text classification example of transformers v4.38.1, the columns are not removed.
https://github.com/huggingface/transformers/blob/a0857740c0e6127485c11476650314df3accc2b6/examples/pytorch/text-classification/run_classification.py#L421
### Steps to reproduce the bug
https://github.com/huggingface/transformers/blob/a0857740c0e6127485c11476650314df3accc2b6/examples/pytorch/text-classification/run_classification.py#L421
### Expected behavior
Actually remove the columns.
### Environment info
1. datasets v2.17.0
2. transformers v4.38.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6699/comments | https://api.github.com/repos/huggingface/datasets/issues/6699/events | https://github.com/huggingface/datasets/issues/6699 | 2,158,152,341 | I_kwDODunzps6AosqV | 6,699 | `Dataset` unexpected changed dict data and may cause error | {
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} | [] | open | false | null | [] | null | 2 | 2024-02-28T05:30:10 | 2024-02-28T19:14:36 | null | NONE | null | ### Describe the bug
Will unexpected get keys with `None` value in the parsed json dict.
### Steps to reproduce the bug
```jsonl test.jsonl
{"id": 0, "indexs": {"-1": [0, 10]}}
{"id": 1, "indexs": {"-1": [0, 10]}}
```
```python
dataset = Dataset.from_json('.test.jsonl')
print(dataset[0])
```
Result:
```
{'id': 0, 'indexs': {'-1': [...], '-2': None, '-3': None, '-4': None, '-5': None, '-6': None, '-7': None, '-8': None, '-9': None, ...}}
```
Those keys with `None` value will unexpected appear in the dict.
### Expected behavior
Result should be
```
{'id': 0, 'indexs': {'-1': [0, 10]}}
```
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-6.5.0-14-generic-x86_64-with-glibc2.35
- Python version: 3.11.6
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0
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} | [] | closed | false | null | [] | null | 3 | 2024-02-27T22:55:08 | 2024-02-27T23:44:49 | 2024-02-27T23:38:14 | COLLABORATOR | null | Pass `detail=False` to the `fsspec` `listdir` to avoid unnecessarily fetching expensive metadata about the paths. | {
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https://api.github.com/repos/huggingface/datasets/issues/6697 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6697/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6697/comments | https://api.github.com/repos/huggingface/datasets/issues/6697/events | https://github.com/huggingface/datasets/issues/6697 | 2,157,322,224 | I_kwDODunzps6Alh_w | 6,697 | Unable to Load Dataset in Kaggle | {
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} | [] | closed | false | null | [] | null | 4 | 2024-02-27T18:19:34 | 2024-02-29T17:32:42 | 2024-02-29T17:32:41 | NONE | null | ### Describe the bug
Having installed the latest versions of transformers==4.38.1 and datasets==2.17.1 Unable to load the dataset in a kaggle notebook.
Get this Error:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[8], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("llm-blender/mix-instruct")
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1661 ignore_verifications = ignore_verifications or save_infos
1663 # Create a dataset builder
-> 1664 builder_instance = load_dataset_builder(
1665 path=path,
1666 name=name,
1667 data_dir=data_dir,
1668 data_files=data_files,
1669 cache_dir=cache_dir,
1670 features=features,
1671 download_config=download_config,
1672 download_mode=download_mode,
1673 revision=revision,
1674 use_auth_token=use_auth_token,
1675 **config_kwargs,
1676 )
1678 # Return iterable dataset in case of streaming
1679 if streaming:
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1488 download_config = download_config.copy() if download_config else DownloadConfig()
1489 download_config.use_auth_token = use_auth_token
-> 1490 dataset_module = dataset_module_factory(
1491 path,
1492 revision=revision,
1493 download_config=download_config,
1494 download_mode=download_mode,
1495 data_dir=data_dir,
1496 data_files=data_files,
1497 )
1499 # Get dataset builder class from the processing script
1500 builder_cls = import_main_class(dataset_module.module_path)
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1242, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1237 if isinstance(e1, FileNotFoundError):
1238 raise FileNotFoundError(
1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1241 ) from None
-> 1242 raise e1 from None
1243 else:
1244 raise FileNotFoundError(
1245 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory."
1246 )
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1230, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1215 return HubDatasetModuleFactoryWithScript(
1216 path,
1217 revision=revision,
(...)
1220 dynamic_modules_path=dynamic_modules_path,
1221 ).get_module()
1222 else:
1223 return HubDatasetModuleFactoryWithoutScript(
1224 path,
1225 revision=revision,
1226 data_dir=data_dir,
1227 data_files=data_files,
1228 download_config=download_config,
1229 download_mode=download_mode,
-> 1230 ).get_module()
1231 except Exception as e1: # noqa: all the attempts failed, before raising the error we should check if the module is already cached.
1232 try:
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:846, in HubDatasetModuleFactoryWithoutScript.get_module(self)
836 token = self.download_config.use_auth_token
837 hfh_dataset_info = HfApi(config.HF_ENDPOINT).dataset_info(
838 self.name,
839 revision=self.revision,
840 token=token,
841 timeout=100.0,
842 )
843 patterns = (
844 sanitize_patterns(self.data_files)
845 if self.data_files is not None
--> 846 else get_patterns_in_dataset_repository(hfh_dataset_info)
847 )
848 data_files = DataFilesDict.from_hf_repo(
849 patterns,
850 dataset_info=hfh_dataset_info,
851 allowed_extensions=ALL_ALLOWED_EXTENSIONS,
852 )
853 infered_module_names = {
854 key: infer_module_for_data_files(data_files_list, use_auth_token=self.download_config.use_auth_token)
855 for key, data_files_list in data_files.items()
856 }
File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:471, in get_patterns_in_dataset_repository(dataset_info)
469 resolver = partial(_resolve_single_pattern_in_dataset_repository, dataset_info)
470 try:
--> 471 return _get_data_files_patterns(resolver)
472 except FileNotFoundError:
473 raise FileNotFoundError(
474 f"The dataset repository at '{dataset_info.id}' doesn't contain any data file."
475 ) from None
File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:99, in _get_data_files_patterns(pattern_resolver)
97 try:
98 for pattern in patterns:
---> 99 data_files = pattern_resolver(pattern)
100 if len(data_files) > 0:
101 non_empty_splits.append(split)
File /opt/conda/lib/python3.10/site-packages/datasets/data_files.py:303, in _resolve_single_pattern_in_dataset_repository(dataset_info, pattern, allowed_extensions)
301 data_files_ignore = FILES_TO_IGNORE
302 fs = HfFileSystem(repo_info=dataset_info)
--> 303 glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)]
304 matched_paths = [
305 filepath
306 for filepath in glob_iter
307 if filepath.name not in data_files_ignore and not filepath.name.startswith(".")
308 ]
309 if allowed_extensions is not None:
File /opt/conda/lib/python3.10/site-packages/fsspec/spec.py:606, in AbstractFileSystem.glob(self, path, maxdepth, **kwargs)
602 depth = None
604 allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
--> 606 pattern = glob_translate(path + ("/" if ends_with_sep else ""))
607 pattern = re.compile(pattern)
609 out = {
610 p: info
611 for p, info in sorted(allpaths.items())
(...)
618 )
619 }
File /opt/conda/lib/python3.10/site-packages/fsspec/utils.py:734, in glob_translate(pat)
732 continue
733 elif "**" in part:
--> 734 raise ValueError(
735 "Invalid pattern: '**' can only be an entire path component"
736 )
737 if part:
738 results.extend(_translate(part, f"{not_sep}*", not_sep))
ValueError: Invalid pattern: '**' can only be an entire path component
```
```
After loading this dataset
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("llm-blender/mix-instruct")
```
### Expected behavior
The dataset should load with desired split.
### Environment info
- `datasets` version: 2.17.1
- Platform: Linux-5.15.133+-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
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} | [] | closed | false | null | [] | null | 2 | 2024-02-26T13:18:31 | 2024-02-28T06:45:23 | 2024-02-28T06:39:12 | MEMBER | null | Support JSON file with an array of strings.
Fix #6695. | {
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] | null | 1 | 2024-02-26T12:35:11 | 2024-03-08T14:16:25 | 2024-02-28T06:39:13 | MEMBER | null | Support loading a dataset from a JSON file with an array of strings.
See: https://huggingface.co/datasets/CausalLM/Refined-Anime-Text/discussions/1 | {
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} | [] | open | false | null | [] | null | 1 | 2024-02-26T01:46:55 | 2024-02-29T16:52:58 | null | NONE | null | It's too cumbersome to write this command every time we perform a dataset merging operation. ```pythonfrom datasets import concatenate_datasets``` We have added a simple `__add__` magic method to each class using `concatenate_datasets.`
```python
from datasets import load_dataset
bookcorpus = load_dataset("bookcorpus", split="train")
wiki = load_dataset("wikimedia/wikipedia", "20231101.ab", split="train")
wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"]) # only keep the 'text' column
bookcorpus + wiki
#Dataset({
# features: ['text'],
# num_rows: 74004228
#})
#Dataset({
# features: ['text'],
# num_rows: 6152
#})
#Dataset({
# features: ['text'],
# num_rows: 74010380
#})
``` | null | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-25T18:37:07 | 2024-02-25T19:57:12 | 2024-02-25T19:51:02 | CONTRIBUTOR | null | Update documentation to align with `Dataset.__repr__` change after #423 | {
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] | null | 2 | 2024-02-24T05:56:04 | 2024-02-26T07:15:07 | 2024-02-26T07:09:35 | NONE | null | ### Feature request
the load_dataset() for local functions support file types like csv, json etc but not of type tsv (tab separated values).
### Motivation
cant easily load files of type tsv, have to convert them to another type like csv then load
### Your contribution
Can try by raising a PR with a little help, currently went through the code but didn't fully understand | {
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Regardless of what method I use, datasets defaults to zstandard for unpacking my datasets.
This is poor behavior, because not only is zstandard not a dependency in the huggingface package (and therefore, your dataset loading will be interrupted while it asks you to install the package), but it happens on datasets that are uploaded in json format too, meaning the dataset loader will attempt to convert the data to a zstandard compatible format, and THEN try to unpackage it.
My 4tb drive runs out of room when using zstandard on slimpajama. It loads fine on 1.5tb when using json, however I lack the understanding of the "magic numbers" system used to select the unpackaging algorithm, so I can't push a change myself.
Commenting out this line, in "/datasets/utils/extract.py" fixes the issue, and causes SlimPajama to properly extract using rational amounts of storage, however it completely disables zstandard, which is probably undesirable behavior. Someone with an understanding of the "magic numbers" system should probably take a pass over this issue.
```
class Extractor:
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
extractors: Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
#"zstd": ZstdExtractor, # This line needs to go, in order for datasets to work w/o non-dependent packages
"rar": RarExtractor,
"bz2": Bzip2Extractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": Lz4Extractor, # <Added version="2.4.0"/>
}
```
### Steps to reproduce the bug
'''
from datasaets import load_dataset
load_dataset(path="/cerebras/SlimPajama-627B")
'''
This alone should trigger the error on any system that does not have zstandard pip installed.
### Expected behavior
This repository (which is encoded in json format, not zstandard) should check whether zstandard is installed before defaulting to it. Additionally, using zstandard should not use more than 3x the required space that other extraction mechanisms use.
### Environment info
- `datasets` version: 2.17.1
- Platform: Linux-6.5.0-18-generic-x86_64-with-glibc2.35
- Python version: 3.12.0
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6688 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6688/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6688/comments | https://api.github.com/repos/huggingface/datasets/issues/6688/events | https://github.com/huggingface/datasets/issues/6688 | 2,148,609,859 | I_kwDODunzps6AES9D | 6,688 | Tensor type (e.g. from `return_tensors`) ignored in map | {
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} | [] | open | false | null | [] | null | 3 | 2024-02-22T09:27:57 | 2024-02-22T15:56:21 | null | NONE | null | ### Describe the bug
I don't know if it is a bug or an expected behavior, but the tensor type seems to be ignored after applying map. For example, mapping over to tokenize text with a transformers' tokenizer always returns lists and it ignore the `return_tensors` argument.
If this is an expected behaviour (e.g., for caching/Arrow compatibility/etc.) it should be clearly documented. For example, current documentation (see [here](https://huggingface.co/docs/datasets/v2.17.1/en/nlp_process#map)) clearly state to "set `return_tensors="np"` when you tokenize your text" to have Numpy arrays.
### Steps to reproduce the bug
```py
# %%%
import datasets
import numpy as np
import tensorflow as tf
import torch
from transformers import AutoTokenizer
# %%
ds = datasets.load_dataset("cnn_dailymail", "1.0.0", split="train[:1%]")
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
#%%
for return_tensors in [None, "np", "pt", "tf", "jax"]:
print(f"********** no map, return_tensors={return_tensors} **********")
_ds = tokenizer(ds["article"], return_tensors=return_tensors, truncation=True, padding=True)
print('Type <input_ids>:', type(_ds["input_ids"]))
# %%
for return_tensors in [None, "np", "pt", "tf", "jax"]:
print(f"********** map, return_tensors={return_tensors} **********")
_ds = ds.map(
lambda examples: tokenizer(examples["article"], return_tensors=return_tensors, truncation=True, padding=True),
batched=True,
remove_columns=["article"],
)
print('Type <input_ids>:', type(_ds[0]["input_ids"]))
```
### Expected behavior
The output from the script above. I would expect the second half to be the same.
```
********** no map, return_tensors=None **********
Type <input_ids>: <class 'list'>
********** no map, return_tensors=np **********
Type <input_ids>: <class 'numpy.ndarray'>
********** no map, return_tensors=pt **********
Type <input_ids>: <class 'torch.Tensor'>
********** no map, return_tensors=tf **********
Type <input_ids>: <class 'tensorflow.python.framework.ops.EagerTensor'>
********** no map, return_tensors=jax **********
Type <input_ids>: <class 'jaxlib.xla_extension.ArrayImpl'>
********** map, return_tensors=None **********
Type <input_ids>: <class 'list'>
********** map, return_tensors=np **********
Type <input_ids>: <class 'list'>
********** map, return_tensors=pt **********
Type <input_ids>: <class 'list'>
********** map, return_tensors=tf **********
Type <input_ids>: <class 'list'>
********** map, return_tensors=jax **********
Type <input_ids>: <class 'list'>
```
### Environment info
- `datasets` version: 2.17.1
- Platform: Redacted (linux)
- Python version: 3.10.12
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.1.3
- `fsspec` version: 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6687 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6687/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6687/comments | https://api.github.com/repos/huggingface/datasets/issues/6687/events | https://github.com/huggingface/datasets/pull/6687 | 2,148,554,178 | PR_kwDODunzps5nnqBB | 6,687 | fsspec: support fsspec>=2023.12.0 glob changes | {
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} | [] | closed | false | null | [] | null | 7 | 2024-02-22T08:59:32 | 2024-03-04T12:59:42 | 2024-02-29T15:12:17 | CONTRIBUTOR | null | - adds support for the `fs.glob` changes introduced in `fsspec==2023.12.0` and unpins the current upper bound
Should close #6644
Should close #6645
The `test_data_files` glob/pattern tests pass for me in:
- `fsspec==2023.10.0` (the pinned max version in datasets `main`)
- `fsspec==2023.12.0` (#6644)
- `fsspec==2024.2.0` (#6645) | {
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https://api.github.com/repos/huggingface/datasets/issues/6686 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6686/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6686/comments | https://api.github.com/repos/huggingface/datasets/issues/6686/events | https://github.com/huggingface/datasets/issues/6686 | 2,147,795,103 | I_kwDODunzps6ABMCf | 6,686 | Question: Is there any way for uploading a large image dataset? | {
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} | [] | open | false | null | [] | null | 1 | 2024-02-21T22:07:21 | 2024-05-02T03:44:59 | null | NONE | null | I am uploading an image dataset like this:
```
dataset = load_dataset(
"json",
data_files={"train": "data/custom_dataset/train.json", "validation": "data/custom_dataset/val.json"},
)
dataset = dataset.cast_column("images", Sequence(Image()))
dataset.push_to_hub("StanfordAIMI/custom_dataset", max_shard_size="1GB")
```
where it takes a long time in the `Map` process. Do you think I can use multi-processing to map all the image data to the memory first? For the `Map()` function, I can set `num_proc`. But for `push_to_hub` and `cast_column`, I can not find it.
Thanks in advance!
Best, | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6685 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6685/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6685/comments | https://api.github.com/repos/huggingface/datasets/issues/6685/events | https://github.com/huggingface/datasets/pull/6685 | 2,145,570,006 | PR_kwDODunzps5ndZQa | 6,685 | Updated Quickstart Notebook link | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-21T01:04:18 | 2024-03-12T21:31:04 | 2024-02-25T18:48:08 | CONTRIBUTOR | null | Fixed Quickstart Notebook Link in the [Overview notebook](https://github.com/huggingface/datasets/blob/main/notebooks/Overview.ipynb) | {
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https://api.github.com/repos/huggingface/datasets/issues/6684 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6684/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6684/comments | https://api.github.com/repos/huggingface/datasets/issues/6684/events | https://github.com/huggingface/datasets/pull/6684 | 2,144,092,388 | PR_kwDODunzps5nYUIf | 6,684 | Improve error message for gated datasets on load | {
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} | [] | closed | false | null | [] | null | 7 | 2024-02-20T10:51:27 | 2024-02-20T15:40:52 | 2024-02-20T15:33:56 | MEMBER | null | Internal Slack discussion: https://huggingface.slack.com/archives/C02V51Q3800/p1708424971135029 | {
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https://api.github.com/repos/huggingface/datasets/issues/6683 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6683/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6683/comments | https://api.github.com/repos/huggingface/datasets/issues/6683/events | https://github.com/huggingface/datasets/pull/6683 | 2,142,751,955 | PR_kwDODunzps5nTxGu | 6,683 | Fix imagefolder dataset url | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-19T16:26:51 | 2024-02-19T17:24:25 | 2024-02-19T17:18:10 | COLLABORATOR | null | null | {
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> Node.js 16 actions are deprecated. Please update the following actions to use Node.js 20: actions/checkout@v3, actions/setup-python@v4. For more information see: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/.
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https://api.github.com/repos/huggingface/datasets/issues/6677 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6677/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6677/comments | https://api.github.com/repos/huggingface/datasets/issues/6677/events | https://github.com/huggingface/datasets/pull/6677 | 2,141,244,167 | PR_kwDODunzps5nOmo_ | 6,677 | Pass through information about location of cache directory. | {
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Pass download config in as an arg too. | {
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https://api.github.com/repos/huggingface/datasets/issues/6676 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6676/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6676/comments | https://api.github.com/repos/huggingface/datasets/issues/6676/events | https://github.com/huggingface/datasets/issues/6676 | 2,140,648,619 | I_kwDODunzps5_l7Sr | 6,676 | Can't Read List of JSON Files Properly | {
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} | [] | open | false | null | [] | null | 3 | 2024-02-17T22:58:15 | 2024-03-02T20:47:22 | null | NONE | null | ### Describe the bug
Trying to read a bunch of JSON files into Dataset class but default approach doesn't work. I don't get why it works when I read it one by one but not when I pass as a list :man_shrugging:
The code fails with
```
ArrowInvalid: JSON parse error: Invalid value. in row 0
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
This doesn't work
```
from datasets import Dataset
# dir contains 100 json files.
Dataset.from_json("/PUT SOME PATH HERE/*")
```
This works:
```
from datasets import concatenate_datasets
ls_ds = []
for file in list_of_json_files:
ls_ds.append(Dataset.from_json(file))
ds = concatenate_datasets(ls_ds)
```
### Expected behavior
I expect this to read json files properly as error is not clear
### Environment info
- `datasets` version: 2.17.0
- Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.2
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/6675 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6675/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6675/comments | https://api.github.com/repos/huggingface/datasets/issues/6675/events | https://github.com/huggingface/datasets/issues/6675 | 2,139,640,381 | I_kwDODunzps5_iFI9 | 6,675 | Allow image model (color conversion) to be specified as part of datasets Image() decode | {
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] | closed | false | null | [] | null | 1 | 2024-02-16T23:43:20 | 2024-03-18T15:41:34 | 2024-03-18T15:41:34 | NONE | null | ### Feature request
Typical torchvision / torch Datasets in image applications apply color conversion in the Dataset portion of the code as part of image decode, separately from the image transform stack. This is true for PIL.Image where convert is usually called in dataset, for native torchvision https://pytorch.org/vision/main/generated/torchvision.io.decode_jpeg.html, and similarly in tensorflow.data pipelines decode_jpeg or https://www.tensorflow.org/api_docs/python/tf/io/decode_and_crop_jpeg have a channels arg that allows controlling the image mode in the decode step.
datasets currently requires this pattern (from [examples](https://huggingface.co/docs/datasets/main/en/image_process)):
```
from torchvision.transforms import Compose, ColorJitter, ToTensor
jitter = Compose(
[
ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.7),
ToTensor(),
]
)
def transforms(examples):
examples["pixel_values"] = [jitter(image.convert("RGB")) for image in examples["image"]]
return examples
```
### Motivation
It would be nice to be able to handle `image.convert("RGB")` (or other modes) in the decode step, before applying torchvision transforms, this would reduce differences in code when handling pipelines that can handle torchvision, webdatset, or hf datasets with fewer code differences and without needing to handle image mode argument passing in two different stages of the pipelines...
### Your contribution
Can do a PR with guidance on how mode should be passed / set on the dataset. | {
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} | [] | closed | false | null | [] | null | 1 | 2024-02-16T22:51:35 | 2024-02-25T18:48:09 | 2024-02-25T18:48:09 | CONTRIBUTOR | null | ### Describe the bug
For the dreprecated notebook found [here](https://github.com/huggingface/datasets/blob/main/notebooks/Overview.ipynb). The link to the new notebook is broken.
### Steps to reproduce the bug
Click the [Quickstart notebook](https://github.com/huggingface/notebooks/blob/main/datasets_doc/quickstart.ipynb) link in the notebook.
### Expected behavior
I believe is it suposed to link [here](https://github.com/huggingface/notebooks/blob/main/datasets_doc/en/quickstart.ipynb). That is mentioned in the readme.
### Environment info
Colab | {
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] | closed | false | null | [] | null | 0 | 2024-02-16T21:38:12 | 2024-07-01T17:45:31 | 2024-07-01T17:45:31 | NONE | null | ### Describe the bug
When persistent workers are enabled, the epoch that's set via the IterableDataset instance held by the training process is ignored by the workers as they are disconnected across processes.
PyTorch samplers for non-iterable datasets have a mechanism to sync this, datasets.IterableDataset does not.
In my own use of IterableDatasets I usually track the epoch count which crosses process boundaries in a multiprocessing.Value
### Steps to reproduce the bug
Use a streaming dataset (Iterable) w/ the recommended pattern below and `persistent_workers=True` in the torch DataLoader.
```
for epoch in range(epochs):
shuffled_dataset.set_epoch(epoch)
for example in shuffled_dataset:
...
```
### Expected behavior
When the canonical bit of code above is used with `num_workers > 0` and `persistent_workers=True`, the epoch set via `set_epoch()` is propagated to the IterableDataset instances in the worker processes
### Environment info
N/A | {
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Note that the `verbose` parameter is deprecated since pandas 2.2.0. See:
- https://github.com/pandas-dev/pandas/pull/56556
- https://github.com/pandas-dev/pandas/pull/57450
Fix #6671. | {
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https://api.github.com/repos/huggingface/datasets/issues/6671 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6671/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6671/comments | https://api.github.com/repos/huggingface/datasets/issues/6671/events | https://github.com/huggingface/datasets/issues/6671 | 2,138,727,870 | I_kwDODunzps5_emW- | 6,671 | CSV builder raises deprecation warning on verbose parameter | {
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] | null | 0 | 2024-02-16T14:23:46 | 2024-02-19T09:20:23 | 2024-02-19T09:20:23 | MEMBER | null | CSV builder raises a deprecation warning on `verbose` parameter:
```
FutureWarning: The 'verbose' keyword in pd.read_csv is deprecated and will be removed in a future version.
```
See:
- https://github.com/pandas-dev/pandas/pull/56556
- https://github.com/pandas-dev/pandas/pull/57450 | {
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https://api.github.com/repos/huggingface/datasets/issues/6670 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6670/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6670/comments | https://api.github.com/repos/huggingface/datasets/issues/6670/events | https://github.com/huggingface/datasets/issues/6670 | 2,138,372,958 | I_kwDODunzps5_dPte | 6,670 | ValueError | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-16T11:05:17 | 2024-02-17T04:26:34 | 2024-02-16T14:43:53 | NONE | null | ### Describe the bug
ValueError Traceback (most recent call last)
[<ipython-input-11-9b99bc80ec23>](https://localhost:8080/#) in <cell line: 11>()
9 import numpy as np
10 import matplotlib.pyplot as plt
---> 11 from datasets import DatasetDict, Dataset
12 from transformers import AutoTokenizer, AutoModelForSequenceClassification
13 from transformers import Trainer, TrainingArguments
5 frames
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
16 __version__ = "2.17.0"
17
---> 18 from .arrow_dataset import Dataset
19 from .arrow_reader import ReadInstruction
20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
65
66 from . import config
---> 67 from .arrow_reader import ArrowReader
68 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
69 from .data_files import sanitize_patterns
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module>
27
28 import pyarrow as pa
---> 29 import pyarrow.parquet as pq
30 from tqdm.contrib.concurrent import thread_map
31
[/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/__init__.py](https://localhost:8080/#) in <module>
18 # flake8: noqa
19
---> 20 from .core import *
[/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py](https://localhost:8080/#) in <module>
34 import pyarrow as pa
35 import pyarrow.lib as lib
---> 36 import pyarrow._parquet as _parquet
37
38 from pyarrow._parquet import (ParquetReader, Statistics, # noqa
/usr/local/lib/python3.10/dist-packages/pyarrow/_parquet.pyx in init pyarrow._parquet()
ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject
### Steps to reproduce the bug
ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject
### Expected behavior
Resolve the binary incompatibility
### Environment info
Google Colab Note book | {
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https://api.github.com/repos/huggingface/datasets/issues/6669 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6669/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6669/comments | https://api.github.com/repos/huggingface/datasets/issues/6669/events | https://github.com/huggingface/datasets/issues/6669 | 2,138,322,662 | I_kwDODunzps5_dDbm | 6,669 | attribute error when writing trainer.train() | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-16T10:40:49 | 2024-03-01T10:58:00 | 2024-02-29T17:25:17 | NONE | null | ### Describe the bug
AttributeError Traceback (most recent call last)
Cell In[39], line 2
1 # Start the training process
----> 2 trainer.train()
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
1537 hf_hub_utils.enable_progress_bars()
1538 else:
-> 1539 return inner_training_loop(
1540 args=args,
1541 resume_from_checkpoint=resume_from_checkpoint,
1542 trial=trial,
1543 ignore_keys_for_eval=ignore_keys_for_eval,
1544 )
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
1833 rng_to_sync = True
1835 step = -1
-> 1836 for step, inputs in enumerate(epoch_iterator):
1837 total_batched_samples += 1
1839 if self.args.include_num_input_tokens_seen:
File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self)
449 # We iterate one batch ahead to check when we are at the end
450 try:
--> 451 current_batch = next(dataloader_iter)
452 except StopIteration:
453 yield
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self)
627 if self._sampler_iter is None:
628 # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E)
629 self._reset() # type: ignore[call-arg]
--> 630 data = self._next_data()
631 self._num_yielded += 1
632 if self._dataset_kind == _DatasetKind.Iterable and \
633 self._IterableDataset_len_called is not None and \
634 self._num_yielded > self._IterableDataset_len_called:
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self)
672 def _next_data(self):
673 index = self._next_index() # may raise StopIteration
--> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
675 if self._pin_memory:
676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index)
49 data = self.dataset.__getitems__(possibly_batched_index)
50 else:
---> 51 data = [self.dataset[idx] for idx in possibly_batched_index]
52 else:
53 data = self.dataset[possibly_batched_index]
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0)
49 data = self.dataset.__getitems__(possibly_batched_index)
50 else:
---> 51 data = [self.dataset[idx] for idx in possibly_batched_index]
52 else:
53 data = self.dataset[possibly_batched_index]
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key)
1762 def __getitem__(self, key): # noqa: F811
1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 1764 return self._getitem(
1765 key,
1766 )
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs)
1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 1749 formatted_output = format_table(
1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
1751 )
1752 return formatted_output
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns)
538 else:
539 pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns)
--> 540 formatted_output = formatter(pa_table_to_format, query_type=query_type)
541 if output_all_columns:
542 if isinstance(formatted_output, MutableMapping):
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table)
56 def format_row(self, pa_table: pa.Table) -> dict:
---> 57 row = self.numpy_arrow_extractor().extract_row(pa_table)
58 return self.recursive_tensorize(row)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table)
153 def extract_row(self, pa_table: pa.Table) -> dict:
--> 154 return _unnest(self.extract_batch(pa_table))
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table)
159 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0)
159 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array)
194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist()
195 if len(array) > 0:
--> 196 if any(
197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
198 or (isinstance(x, float) and np.isnan(x))
199 for x in array
200 ):
201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
202 return np.array(array, copy=False, **self.np_array_kwargs)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0)
194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist()
195 if len(array) > 0:
196 if any(
--> 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
198 or (isinstance(x, float) and np.isnan(x))
199 for x in array
200 ):
201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
202 return np.array(array, copy=False, **self.np_array_kwargs)
File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr)
319 warnings.warn(
320 f"In the future `np.{attr}` will be defined as the "
321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
323 if attr in __former_attrs__:
--> 324 raise AttributeError(__former_attrs__[attr])
326 if attr == 'testing':
327 import numpy.testing as testing
AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecationsAttributeError Traceback (most recent call last)
Cell In[39], line 2
1 # Start the training process
----> 2 trainer.train()
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
1537 hf_hub_utils.enable_progress_bars()
1538 else:
-> 1539 return inner_training_loop(
1540 args=args,
1541 resume_from_checkpoint=resume_from_checkpoint,
1542 trial=trial,
1543 ignore_keys_for_eval=ignore_keys_for_eval,
1544 )
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
1833 rng_to_sync = True
1835 step = -1
-> 1836 for step, inputs in enumerate(epoch_iterator):
1837 total_batched_samples += 1
1839 if self.args.include_num_input_tokens_seen:
File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self)
449 # We iterate one batch ahead to check when we are at the end
450 try:
--> 451 current_batch = next(dataloader_iter)
452 except StopIteration:
453 yield
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self)
627 if self._sampler_iter is None:
628 # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E)
629 self._reset() # type: ignore[call-arg]
--> 630 data = self._next_data()
631 self._num_yielded += 1
632 if self._dataset_kind == _DatasetKind.Iterable and \
633 self._IterableDataset_len_called is not None and \
634 self._num_yielded > self._IterableDataset_len_called:
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self)
672 def _next_data(self):
673 index = self._next_index() # may raise StopIteration
--> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
675 if self._pin_memory:
676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index)
49 data = self.dataset.__getitems__(possibly_batched_index)
50 else:
---> 51 data = [self.dataset[idx] for idx in possibly_batched_index]
52 else:
53 data = self.dataset[possibly_batched_index]
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0)
49 data = self.dataset.__getitems__(possibly_batched_index)
50 else:
---> 51 data = [self.dataset[idx] for idx in possibly_batched_index]
52 else:
53 data = self.dataset[possibly_batched_index]
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key)
1762 def __getitem__(self, key): # noqa: F811
1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 1764 return self._getitem(
1765 key,
1766 )
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs)
1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 1749 formatted_output = format_table(
1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
1751 )
1752 return formatted_output
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns)
538 else:
539 pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns)
--> 540 formatted_output = formatter(pa_table_to_format, query_type=query_type)
541 if output_all_columns:
542 if isinstance(formatted_output, MutableMapping):
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table)
56 def format_row(self, pa_table: pa.Table) -> dict:
---> 57 row = self.numpy_arrow_extractor().extract_row(pa_table)
58 return self.recursive_tensorize(row)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table)
153 def extract_row(self, pa_table: pa.Table) -> dict:
--> 154 return _unnest(self.extract_batch(pa_table))
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table)
159 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0)
159 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array)
194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist()
195 if len(array) > 0:
--> 196 if any(
197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
198 or (isinstance(x, float) and np.isnan(x))
199 for x in array
200 ):
201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
202 return np.array(array, copy=False, **self.np_array_kwargs)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0)
194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist()
195 if len(array) > 0:
196 if any(
--> 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
198 or (isinstance(x, float) and np.isnan(x))
199 for x in array
200 ):
201 return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
202 return np.array(array, copy=False, **self.np_array_kwargs)
File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr)
319 warnings.warn(
320 f"In the future `np.{attr}` will be defined as the "
321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
323 if attr in __former_attrs__:
--> 324 raise AttributeError(__former_attrs__[attr])
326 if attr == 'testing':
327 import numpy.testing as testing
AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
Please help me to resolve the above error
### Steps to reproduce the bug
Please resolve the issue of deprecated function np.object to object in the numpy
### Expected behavior
np.object should be written as object only
### Environment info
kaggle notebook | {
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https://api.github.com/repos/huggingface/datasets/issues/6668 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6668/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6668/comments | https://api.github.com/repos/huggingface/datasets/issues/6668/events | https://github.com/huggingface/datasets/issues/6668 | 2,137,859,935 | I_kwDODunzps5_bSdf | 6,668 | Chapter 6 - Issue Loading `cnn_dailymail` dataset | {
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} | [] | open | false | null | [] | null | 0 | 2024-02-16T04:40:56 | 2024-02-16T04:40:56 | null | NONE | null | ### Describe the bug
So I am getting this bug when I try to run cell 4 of the Chapter 6 notebook code:
`dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0")`
Error Message:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[4], line 4
1 #hide_output
2 from datasets import load_dataset
----> 4 dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0")
7 # dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0", trust_remote_code=True)
8 print(f"Features: {dataset['train'].column_names}")
File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\load.py:2587, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2583 # Build dataset for splits
2584 keep_in_memory = (
2585 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2586 )
-> 2587 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
2588 # Rename and cast features to match task schema
2589 if task is not None:
2590 # To avoid issuing the same warning twice
File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1244, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1241 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
1243 # Create a dataset for each of the given splits
-> 1244 datasets = map_nested(
1245 partial(
1246 self._build_single_dataset,
1247 run_post_process=run_post_process,
1248 verification_mode=verification_mode,
1249 in_memory=in_memory,
1250 ),
1251 split,
1252 map_tuple=True,
1253 disable_tqdm=True,
1254 )
1255 if isinstance(datasets, dict):
1256 datasets = DatasetDict(datasets)
File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:477, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)
466 mapped = [
467 map_nested(
468 function=function,
(...)
474 for obj in iterable
475 ]
476 elif num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:
--> 477 mapped = [
478 _single_map_nested((function, obj, types, None, True, None))
479 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc)
480 ]
481 else:
482 with warnings.catch_warnings():
File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:478, in <listcomp>(.0)
466 mapped = [
467 map_nested(
468 function=function,
(...)
474 for obj in iterable
475 ]
476 elif num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:
477 mapped = [
--> 478 _single_map_nested((function, obj, types, None, True, None))
479 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc)
480 ]
481 else:
482 with warnings.catch_warnings():
File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:370, in _single_map_nested(args)
368 # Singleton first to spare some computation
369 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 370 return function(data_struct)
372 # Reduce logging to keep things readable in multiprocessing with tqdm
373 if rank is not None and logging.get_verbosity() < logging.WARNING:
File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1274, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory)
1271 split = Split(split)
1273 # Build base dataset
-> 1274 ds = self._as_dataset(
1275 split=split,
1276 in_memory=in_memory,
1277 )
1278 if run_post_process:
1279 for resource_file_name in self._post_processing_resources(split).values():
File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1348, in DatasetBuilder._as_dataset(self, split, in_memory)
1346 if self._check_legacy_cache():
1347 dataset_name = self.name
-> 1348 dataset_kwargs = ArrowReader(cache_dir, self.info).read(
1349 name=dataset_name,
1350 instructions=split,
1351 split_infos=self.info.splits.values(),
1352 in_memory=in_memory,
1353 )
1354 fingerprint = self._get_dataset_fingerprint(split)
1355 return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\arrow_reader.py:254, in BaseReader.read(self, name, instructions, split_infos, in_memory)
252 if not files:
253 msg = f'Instruction "{instructions}" corresponds to no data!'
--> 254 raise ValueError(msg)
255 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
**ValueError: Instruction "validation" corresponds to no data!**
````
Looks like the data is not being loaded. Any advice would be appreciated. Thanks!
### Steps to reproduce the bug
Run all cells of Chapter 6 notebook.
### Expected behavior
Data should load correctly without any errors.
### Environment info
- `datasets` version: 2.17.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.18
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6667 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6667/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6667/comments | https://api.github.com/repos/huggingface/datasets/issues/6667/events | https://github.com/huggingface/datasets/issues/6667 | 2,137,769,552 | I_kwDODunzps5_a8ZQ | 6,667 | Default config for squad is incorrect | {
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} | [] | open | false | null | [] | null | 1 | 2024-02-16T02:36:55 | 2024-02-23T09:10:00 | null | NONE | null | ### Describe the bug
If you download Squad, it will download the plain_text version, but the config still specifies "default", so if you set the offline mode the cache will try to look it up according to the config_id which is "default" and this will say;
ValueError: Couldn't find cache for squad for config 'default'
Available configs in the cache: ['plain_text']
### Steps to reproduce the bug
1. export HF_DATASETS_OFFLINE=0
2. load_dataset("squad")
3. export HF_DATASETS_OFFLINE=1
4. load_dataset("squad")
### Expected behavior
We should change the config_name I guess?
### Environment info
linux, latest version of datasets | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6665 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6665/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6665/comments | https://api.github.com/repos/huggingface/datasets/issues/6665/events | https://github.com/huggingface/datasets/pull/6665 | 2,136,136,425 | PR_kwDODunzps5m9JgW | 6,665 | Allow SplitDict setitem to replace existing SplitInfo | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-15T10:17:08 | 2024-03-01T16:02:46 | 2024-03-01T15:56:38 | MEMBER | null | Fix this code provided by @clefourrier
```python
import datasets
import os
token = os.getenv("TOKEN")
results = datasets.load_dataset("gaia-benchmark/results_public", "2023", token=token, download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)
results["test"] = datasets.Dataset.from_list([row for row in results["test"] if row["model"] != "StateFlow"])
results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test")
```
```
ValueError Traceback (most recent call last)
Cell In[43], line 1
----> 1 results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test")
File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/arrow_dataset.py:5498, in Dataset.push_to_hub(self, repo_id, config_name, split, private, token, branch, max_shard_size, num_shards, embed_external_files)
5496 repo_info.dataset_size = (repo_info.dataset_size or 0) + dataset_nbytes
5497 repo_info.size_in_bytes = repo_info.download_size + repo_info.dataset_size
-> 5498 repo_info.splits[split] = SplitInfo(
5499 split, num_bytes=dataset_nbytes, num_examples=len(self), dataset_name=dataset_name
5500 )
5501 info_to_dump = repo_info
5502 # create the metadata configs if it was uploaded with push_to_hub before metadata configs existed
File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/splits.py:541, in SplitDict.__setitem__(self, key, value)
539 raise ValueError(f"Cannot add elem. (key mismatch: '{key}' != '{value.name}')")
540 if key in self:
--> 541 raise ValueError(f"Split {key} already present")
542 super().__setitem__(key, value)
ValueError: Split test already present
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6664 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6664/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6664/comments | https://api.github.com/repos/huggingface/datasets/issues/6664/events | https://github.com/huggingface/datasets/pull/6664 | 2,135,483,978 | PR_kwDODunzps5m67g0 | 6,664 | Revert the changes in `arrow_writer.py` from #6636 | {
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} | [] | closed | false | null | [] | null | 5 | 2024-02-15T01:47:33 | 2024-02-16T14:02:39 | 2024-02-16T02:31:11 | CONTRIBUTOR | null | #6636 broke `write_examples_on_file` and `write_batch` from the class `ArrowWriter`. I'm undoing these changes. See #6663.
Note the current implementation doesn't keep the order of the columns and the schema, thus setting a wrong schema for each column. | {
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https://api.github.com/repos/huggingface/datasets/issues/6663 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6663/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6663/comments | https://api.github.com/repos/huggingface/datasets/issues/6663/events | https://github.com/huggingface/datasets/issues/6663 | 2,135,480,811 | I_kwDODunzps5_SNnr | 6,663 | `write_examples_on_file` and `write_batch` are broken in `ArrowWriter` | {
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} | [] | closed | false | null | [] | null | 3 | 2024-02-15T01:43:27 | 2024-02-16T09:25:00 | 2024-02-16T09:25:00 | CONTRIBUTOR | null | ### Describe the bug
`write_examples_on_file` and `write_batch` are broken in `ArrowWriter` since #6636. The order between the columns and the schema is not preserved anymore. So these functions don't work anymore unless the order happens to align well.
### Steps to reproduce the bug
Try to do `write_batch` with anything that has many columns, and it's likely to break.
### Expected behavior
I expect these functions to work, instead of it trying to cast a column to its incorrect type.
### Environment info
- `datasets` version: 2.17.0
- Platform: Linux-5.15.0-1040-aws-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.19.4
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6662 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6662/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6662/comments | https://api.github.com/repos/huggingface/datasets/issues/6662/events | https://github.com/huggingface/datasets/pull/6662 | 2,132,425,812 | PR_kwDODunzps5mwgKP | 6,662 | fix: show correct package name to install biopython | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-13T14:15:04 | 2024-03-01T17:49:48 | 2024-03-01T17:43:39 | CONTRIBUTOR | null | When you try to download a dataset that uses [biopython](https://github.com/biopython/biopython), like `load_dataset("InstaDeepAI/multi_species_genomes")`, you get the error:
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("InstaDeepAI/multi_species_genomes")
/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py:1454: FutureWarning: The repository for InstaDeepAI/multi_species_genomes contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/InstaDeepAI/multi_species_genomes
You can avoid this message in future by passing the argument `trust_remote_code=True`.
Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
warnings.warn(
Downloading builder script: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.51k/7.51k [00:00<00:00, 7.67MB/s]
Downloading readme: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17.2k/17.2k [00:00<00:00, 11.0MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2548, in load_dataset
builder_instance = load_dataset_builder(
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2220, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1871, in dataset_module_factory
raise e1 from None
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1844, in dataset_module_factory
).get_module()
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1466, in get_module
local_imports = _download_additional_modules(
File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 346, in _download_additional_modules
raise ImportError(
ImportError: To be able to use InstaDeepAI/multi_species_genomes, you need to install the following dependency: Bio.
Please install it using 'pip install Bio' for instance.
>>>
```
`Bio` comes from the `biopython` package that can be installed with `pip install biopython`, not with `pip install Bio` as suggested.
This PR adds special logic to show the correct package name in the error message of ` _download_additional_modules`, similarly as is done for `sklearn` / `scikit-learn` already.
There are more packages where importable module name differs from the PyPI package name, so this could be made more generic, like:
```
# Mapping of importable module names to their PyPI package names
package_map = {
"sklearn": "scikit-learn",
"Bio": "biopython",
"PIL": "Pillow",
"bs4": "beautifulsoup4"
}
for module_name, pypi_name in package_map.items():
if module_name in needs_to_be_installed.keys():
needs_to_be_installed[module_name] = pypi_name
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6661 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6661/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6661/comments | https://api.github.com/repos/huggingface/datasets/issues/6661/events | https://github.com/huggingface/datasets/issues/6661 | 2,132,296,267 | I_kwDODunzps5_GEJL | 6,661 | Import error on Google Colab | {
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} | [] | closed | false | null | [] | null | 4 | 2024-02-13T13:12:40 | 2024-02-25T16:37:54 | 2024-02-14T08:04:47 | NONE | null | ### Describe the bug
Cannot be imported on Google Colab, the import throws the following error:
ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject
### Steps to reproduce the bug
1. `! pip install -U datasets`
2. `import datasets`
### Expected behavior
Should be possible to use the library
### Environment info
- `datasets` version: 2.17.0
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6660 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6660/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6660/comments | https://api.github.com/repos/huggingface/datasets/issues/6660/events | https://github.com/huggingface/datasets/pull/6660 | 2,131,977,011 | PR_kwDODunzps5mu9wU | 6,660 | Automatic Conversion for uint16/uint32 to Compatible PyTorch Dtypes | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-13T10:24:33 | 2024-03-01T19:01:57 | 2024-03-01T18:52:37 | CONTRIBUTOR | null | This PR addresses an issue encountered when utilizing uint16 or uint32 datatypes with datasets, followed by attempting to convert these datasets into PyTorch-compatible formats. Currently, doing so results in a TypeError due to incompatible datatype conversion, as illustrated by the following example:
```python
from datasets import Dataset, Sequence, Value, Features
def gen():
for i in range(100):
yield {'seq': list(range(i, i + 20))}
ds = Dataset.from_generator(gen, features=Features({'seq': Sequence(feature=Value(dtype='uint16'), length=-1)}))
ds.set_format('torch')
print(ds[0])
```
This code snippet triggers the following error due to the inability to convert numpy.uint16 arrays to a PyTorch-supported format:
```
TypeError: can't convert np.ndarray of type numpy.uint16. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.
```
This PR introduces an automatic mechanism to convert np.uint16 and np.uint32 datatypes to np.int64 for seamless compatibility with PyTorch formats, simplifying workflows and improving developer experience by eliminating the need for manual conversion handling. | {
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https://api.github.com/repos/huggingface/datasets/issues/6659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6659/comments | https://api.github.com/repos/huggingface/datasets/issues/6659/events | https://github.com/huggingface/datasets/pull/6659 | 2,129,229,810 | PR_kwDODunzps5mlmmo | 6,659 | Change default compression argument for JsonDatasetWriter | {
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} | [] | closed | false | null | [] | null | 3 | 2024-02-11T23:49:07 | 2024-03-01T17:51:50 | 2024-03-01T17:44:55 | CONTRIBUTOR | null | Change default compression type from `None` to "infer", to align with pandas' defaults.
Documentation asks the user to supply `to_json_kwargs` with arguments suitable for pandas' `to_json` method. At the same time, while pandas' by default uses ["infer"](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) for compression, datasets enforce `None` as default. This, likely, confuses user, as they expect the same behaviour, i.e they expect that if they name their output file as "dataset.jsonl.zst" then the compression would be inferred as "zstd" and file will be compressed before writing.
Moreover, while it is probably outside of the scope of this pull request, `compression` argument needs to be capable of taking `dict` as input (along with `str`), as it does in pandas, in order to allow user to specify compression parameters. Current implementation will likely fail with `NotImplementedError`, as it expects either `None` or `str` specifying compression algo. | {
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https://api.github.com/repos/huggingface/datasets/issues/6658 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6658/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6658/comments | https://api.github.com/repos/huggingface/datasets/issues/6658/events | https://github.com/huggingface/datasets/pull/6658 | 2,129,158,371 | PR_kwDODunzps5mlZyb | 6,658 | [Resumable IterableDataset] Add IterableDataset state_dict | {
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} | [] | closed | false | null | [] | null | 20 | 2024-02-11T20:35:52 | 2024-10-01T10:19:38 | 2024-06-03T19:15:39 | MEMBER | null | A simple implementation of a mechanism to resume an IterableDataset.
It works by restarting at the latest shard and skip samples. It provides fast resuming (though not instantaneous).
Example:
```python
from datasets import Dataset, concatenate_datasets
ds = Dataset.from_dict({"a": range(5)}).to_iterable_dataset(num_shards=3)
ds = concatenate_datasets([ds] * 2)
print(f"{ds.state_dict()=}")
for i, example in enumerate(ds):
print(example)
if i == 6:
state_dict = ds.state_dict()
print("checkpoint")
ds.load_state_dict(state_dict)
print(f"resuming from checkpoint {ds.state_dict()=}")
for example in ds:
print(example)
```
returns
```
ds.state_dict()={'ex_iterable_idx': 0, 'ex_iterables': [{'shard_idx': 0, 'shard_example_idx': 0}, {'shard_idx': 0, 'shard_example_idx': 0}]}
{'a': 0}
{'a': 1}
{'a': 2}
{'a': 3}
{'a': 4}
{'a': 0}
{'a': 1}
checkpoint
{'a': 2}
{'a': 3}
{'a': 4}
resuming from checkpoint ds.state_dict()={'ex_iterable_idx': 1, 'ex_iterables': [{'shard_idx': 3, 'shard_example_idx': 0}, {'shard_idx': 0, 'shard_example_idx': 2}]}
{'a': 2}
{'a': 3}
{'a': 4}
```
using torchdata:
```python
from datasets import load_dataset
from torchdata.stateful_dataloader import StatefulDataLoader
my_iterable_dataset = load_dataset("deepmind/code_contests", streaming=True, split="train")
dataloader = StatefulDataLoader(my_iterable_dataset, batch_size=32, num_workers=4)
# save in the middle of training
state_dict = dataloader.state_dict()
# and resume later
dataloader.load_state_dict(state_dict)
```
docs: https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume | {
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https://api.github.com/repos/huggingface/datasets/issues/6657 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6657/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6657/comments | https://api.github.com/repos/huggingface/datasets/issues/6657/events | https://github.com/huggingface/datasets/issues/6657 | 2,129,147,085 | I_kwDODunzps5-6DTN | 6,657 | Release not pushed to conda channel | {
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] | null | 5 | 2024-02-11T20:05:17 | 2024-03-06T15:06:22 | 2024-03-06T15:06:22 | NONE | null | ### Describe the bug
The github actions step to publish the release 2.17.0 to conda channel has failed due to expired token. Can some one please update the anaconda token rerun the failed action? @albertvillanova ?
### Steps to reproduce the bug
Please see this actions [link](https://github.com/huggingface/datasets/actions/runs/7842473662)
### Expected behavior
The action runs successfully and the latest release is pushed to HuggingFace conda channel
### Environment info
Not applicable. | {
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https://api.github.com/repos/huggingface/datasets/issues/6656 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6656/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6656/comments | https://api.github.com/repos/huggingface/datasets/issues/6656/events | https://github.com/huggingface/datasets/issues/6656 | 2,127,338,377 | I_kwDODunzps5-zJuJ | 6,656 | Error when loading a big local json file | {
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} | [] | open | false | null | [] | null | 2 | 2024-02-09T15:14:21 | 2024-11-29T10:06:57 | null | NONE | null | ### Describe the bug
When trying to load big json files from a local directory, `load_dataset` throws the following error
```
Traceback (most recent call last):
File "/miniconda3/envs/conda-env/lib/python3.10/site-packages/datasets/builder.py", line 1989, in _prepare_split_single
writer.write_table(table)
File "miniconda3/envs/conda-env/lib/python3.10/site-packages/datasets/arrow_writer.py", line 573, in write_table
pa_table = pa_table.combine_chunks()
File "pyarrow/table.pxi", line 3638, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
```
### Steps to reproduce the bug
1. Download a big file, e.g. `https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz`
2. Load it like `data = load_dataset("json", data_files=["nq-train.json"], split="train")`
```python
from datasets import load_dataset
data = load_dataset("json", data_files=["nq-train.json"], split="train")
```
A similarly formatted but smaller file, e.g. e.g. `https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz` is loaded without issues
```python
from datasets import load_dataset
data = load_dataset("json", data_files=["nq-dev.json"], split="train")
```
### Expected behavior
It should load normally
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0 | null | {
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] | null | 4 | 2024-02-09T12:15:39 | 2024-02-12T09:35:55 | null | NONE | null | ### Describe the bug
When I run the following code I get an exception;
`go_emotions = load_dataset("go_emotions")`
> AttributeError Traceback (most recent call last)
Cell In[6], [line 1](vscode-notebook-cell:?execution_count=6&line=1)
----> [1](vscode-notebook-cell:?execution_count=6&line=1) go_emotions = load_dataset("go_emotions")
[2](vscode-notebook-cell:?execution_count=6&line=2) data = go_emotions.data
File [c:\Users\hijik\anaconda3\Lib\site-packages\datasets\load.py:2523](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2523), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
[2518](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2518) verification_mode = VerificationMode(
[2519](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2519) (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
[2520](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2520) )
[2522](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2522) # Create a dataset builder
-> [2523](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2523) builder_instance = load_dataset_builder(
[2524](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2524) path=path,
[2525](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2525) name=name,
[2526](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2526) data_dir=data_dir,
[2527](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2527) data_files=data_files,
[2528](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2528) cache_dir=cache_dir,
[2529](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2529) features=features,
[2530](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2530) download_config=download_config,
[2531](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2531) download_mode=download_mode,
[2532](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2532) revision=revision,
[2533](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2533) token=token,
[2534](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2534) storage_options=storage_options,
[2535](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2535) trust_remote_code=trust_remote_code,
[2536](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2536) _require_default_config_name=name is None,
...
---> [63](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:63) if issubclass(obj_type, transformers.PreTrainedTokenizerBase):
[64](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:64) pklregister(obj_type)(_save_transformersPreTrainedTokenizerBase)
[66](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:66) # Unwrap `torch.compile`-ed functions
AttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?10bc0728-6947-456e-9a3e-f056872b04c6) or open in a [text editor](command:workbench.action.openLargeOutput?10bc0728-6947-456e-9a3e-f056872b04c6). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Steps to reproduce the bug
```
from datasets import load_dataset
go_emotions = load_dataset("go_emotions")
```
### Expected behavior
Should simply load the variable with the data from the file
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.16.1
- Platform: Windows-10-10.0.22631-SP0
- Python version: 3.11.4
- `huggingface_hub` version: 0.20.3
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.10.0 | null | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-09T11:23:19 | 2024-02-12T08:26:53 | 2024-02-12T08:26:53 | NONE | null | ### Describe the bug
I encountered a TypeError when batch processing a dataset with Sequence features in datasets package version 2.16.1. The error arises from a mismatch in handling fixed-size list arrays during the map function execution. Debugging pinpoints the issue to an if-statement in datasets/table.py, line 2093, failing to correctly process sequence lengths.
### Steps to reproduce the bug
Create virtual environment and activate
```
virtualenv venv
source venv/bin/activate
```
Then install the datasets package (I'm using the latest version)
```
pip install datasets==2.16.1
```
Then run
```python
# bug.py
from datasets import Dataset
from datasets.features import Features, Sequence, Value
data = {
"num": [[1, 2], [3, 4]],
}
features = Features({'num': Sequence(feature=Value(dtype='int32'), length=2)})
dataset = Dataset.from_dict(data, features=features)
dataset.map(lambda x: x, batched=True, batch_size=1)
```
### Expected behavior
I get the following stack trace
```
Map: 50%|█████ | 1/2 [00:00<00:00, 423.92 examples/s]
Traceback (most recent call last):
File "/PATH/TO/BUG_PORT/bug.py", line 9, in <module>
dataset.map(lambda x: x, batched=True, batch_size=1)
File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 592, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3093, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3489, in _map_single
writer.write_batch(batch)
File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 551, in write_batch
array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values
File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
fixed_size_list<item: int32>[2]
to
Sequence(feature=Value(dtype='int32', id=None), length=2, id=None)
```
After some debugging, I found that the if-statement that is actually failing is line 2093 in `datasets/table.py`
```python
# datasets/table.py
...
2093 if feature.length * len(array) == len(array_values):
2094 return pa.FixedSizeListArray.from_arrays(_c(array_values, feature.feature), feature.length)
...
```
### Environment info
Platform: MacOS
Datasets version: datasets==2.16.1
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] | open | false | null | [] | null | 0 | 2024-02-09T08:00:21 | 2024-06-14T14:42:46 | null | NONE | null | ### Feature request
Support for slice splits in `datasets.load_from_disk`, similar to how it's already supported for `datasets.load_dataset`.
### Motivation
Slice splits are convienient in a numer of cases - adding support to `datasets.load_from_disk` would make working with local datasets easier and homogenize the APIs of load_from_disk and load_dataset.
### Your contribution
Sure, if the devs think the feature request is sensible. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6650 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6650/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6650/comments | https://api.github.com/repos/huggingface/datasets/issues/6650/events | https://github.com/huggingface/datasets/issues/6650 | 2,125,680,991 | I_kwDODunzps5-s1Ff | 6,650 | AttributeError: 'InMemoryTable' object has no attribute '_batches' | {
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```
Traceback (most recent call last):
File "finetune.py", line 103, in <module>
main(args)
File "finetune.py", line 45, in main
data_tokenized = data.map(partial(funcs.tokenize_function, tokenizer,
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/dataset_dict.py", line 868, in map
{
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/dataset_dict.py", line 869, in <dictcomp>
k: dataset.map(
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 592, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3093, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3432, in _map_single
arrow_formatted_shard = shard.with_format("arrow")
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2667, in with_format
dataset = copy.deepcopy(self)
File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 270, in _reconstruct
state = deepcopy(state, memo)
File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 230, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 153, in deepcopy
y = copier(memo)
File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/table.py", line 176, in __deepcopy__
memo[id(self._batches)] = list(self._batches)
AttributeError: 'InMemoryTable' object has no attribute '_batches'
```
### Steps to reproduce the bug
I'm running an MLOps flow using AzureML.
The error appears when I run the following function in my training script:
```python
data_tokenized = data.map(partial(funcs.tokenize_function, tokenizer,
seq_length),
batched=True,
batch_size=batch_size,
remove_columns=['col1', 'col2'])
```
```python
def tokenize_function(tok, seq_length, example)
# Pad so that each batch has the same sequence length
inp = tok(example['col1'], padding=True, truncation=True)
outp = tok(example['col2'], padding="max_length", max_length=seq_length)
res = {
'input_ids': inp['input_ids'],
'attention_mask': inp['attention_mask'],
'decoder_input_ids': outp['input_ids'],
'labels': outp['input_ids'],
'decoder_attention_mask': outp['attention_mask']
}
return res
```
### Expected behavior
Processing proceeds without errors. I ran this same workflow 2 weeks ago without a problem. I recreated the environment since then but it doesn't appear that datasets versions have changed since Dec. '23.
### Environment info
datasets 2.16.1
transformers 4.35.2
pyarrow 15.0.0
pyarrow-hotfix 0.6
torch 2.0.1
I'm not using the latest transformers version because there was an error due to a conflict with Azure mlflow when I tried the last time. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6649 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6649/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6649/comments | https://api.github.com/repos/huggingface/datasets/issues/6649/events | https://github.com/huggingface/datasets/pull/6649 | 2,124,940,213 | PR_kwDODunzps5mXRo8 | 6,649 | Minor multi gpu doc improvement | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-08T11:17:24 | 2024-02-08T11:23:35 | 2024-02-08T11:17:35 | MEMBER | null | just added torch.no_grad and eval() | {
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https://api.github.com/repos/huggingface/datasets/issues/6648 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6648/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6648/comments | https://api.github.com/repos/huggingface/datasets/issues/6648/events | https://github.com/huggingface/datasets/pull/6648 | 2,124,813,589 | PR_kwDODunzps5mW1MA | 6,648 | Document usage of hfh cli instead of git | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-08T10:24:56 | 2024-02-08T13:57:41 | 2024-02-08T13:51:39 | MEMBER | null | (basically the same content as the hfh upload docs, but adapted for datasets) | {
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} | [] | open | false | null | [] | null | 2 | 2024-02-07T16:18:08 | 2024-02-08T15:34:17 | null | NONE | null | * A small update to the documentation, noting the ability to load jsonl files. | null | {
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} | [] | closed | false | null | [] | null | 3 | 2024-02-07T14:15:01 | 2024-02-09T17:43:32 | 2024-02-07T14:59:11 | MEMBER | null | Use Qwen1.5-0.5B-Chat as an easy example for multi-GPU
the previous example was using a model for translation and the way it was setup was not really the right way to use the model. | {
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First, we should address:
- #6644 | {
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https://api.github.com/repos/huggingface/datasets/issues/6643 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6643/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6643/comments | https://api.github.com/repos/huggingface/datasets/issues/6643/events | https://github.com/huggingface/datasets/issues/6643 | 2,121,239,039 | I_kwDODunzps5-b4n_ | 6,643 | Faiss GPU index cannot be serialised when passed to trainer | {
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} | [] | open | false | null | [] | null | 3 | 2024-02-06T16:41:00 | 2024-02-15T10:29:32 | null | NONE | null | ### Describe the bug
I am working on a retrieval project and encountering I have encountered two issues in the hugging face faiss integration:
1. I am trying to pass in a dataset with a faiss index to the Huggingface trainer. The code works for a cpu faiss index, but doesn't for a gpu one, getting error:
```
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1543, in train
return inner_training_loop(
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1555, in _inner_training_loop
train_dataloader = self.get_train_dataloader()
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 831, in get_train_dataloader
train_dataset = self._remove_unused_columns(train_dataset, description="training")
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 725, in _remove_unused_columns
return dataset.remove_columns(ignored_columns)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 592, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/fingerprint.py", line 481, in wrapper
out = func(dataset, *args, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2146, in remove_columns
dataset = copy.deepcopy(self)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 161, in deepcopy
rv = reductor(4)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 556, in index_getstate
return {"this": serialize_index(self).tobytes()}
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 1607, in serialize_index
write_index(index, writer)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/swigfaiss.py", line 9843, in write_index
return _swigfaiss.write_index(*args)
RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /project/faiss/faiss/impl/index_write.cpp:590: don't know how to serialize this type of index
```
The index was created with the add_faiss_index method
```
train_dataset.add_faiss_index(
column='embeddings',
index_name='embeddings',
string_factory=faiss_index_string,
train_size=config.faiss_train_size,
device=0, # Use -1 for CPU, or specify GPU device ID
faiss_verbose=True
)
```
2. Athough faiss is written to be compatible on the gpu for searching [https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU) I am getting error when trying to use the hugggingface code to do the search on gpu. This seems to be caused by this line https://github.com/huggingface/datasets/blob/f9975f636542df7f95c27065ea93147440d690b7/src/datasets/search.py#L376 producing error
```
total_scores, total_examples = self.dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 773, in get_nearest_examples_batch
total_scores, total_indices = self.search_batch(index_name, queries, k, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 727, in search_batch
return self._indexes[index_name].search_batch(queries, k, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 376, in search_batch
if not queries.flags.c_contiguous:
AttributeError: 'Tensor' object has no attribute 'flags'
```
### Steps to reproduce the bug
```
train_dataset.add_faiss_index(
column='embeddings',
index_name='embeddings',
string_factory=faiss_index_string,
train_size=config.faiss_train_size,
device=0, # Use -1 for CPU, or specify GPU device ID
faiss_verbose=True
)
Trainer(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator,
tokenizer=tokenizer
)
train_dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k)
```
### Expected behavior
I would expect the faiss database code to be gpu compatible
### Environment info
huggingface Version: 2.16.1 | null | {
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} | [] | closed | false | null | [] | null | 2 | 2024-02-05T17:28:57 | 2024-02-06T09:50:19 | 2024-02-06T09:50:19 | NONE | null | ### Describe the bug
Differently sized object is saved than it is loaded.
### Steps to reproduce the bug
Hi, I save dataset in a following way:
```
dataset = load_dataset("json",
data_files={
"train": os.path.join(input_folder, f"{task_meta_type}_{task_type}_train.jsonl"),
"test": os.path.join(input_folder, f"{task_meta_type}_{task_type}_test.jsonl")})
print(os.path.join(output_folder, f"{task_meta_type}_{task_type}"))
print(f"Length of train dataset: {len(dataset['train'])}")
print(f"Length of test dataset: {len(dataset['test'])}")
dataset.save_to_disk(os.path.join(output_folder, f"{task_meta_type}_{task_type}"))
```
this yields output
```
.data/hf_dataset/propaganda_zanr
Length of train dataset: 7642
Length of test dataset: 1000
```
Everything looks fine.
Then I load the dataset
```python
from datasets import load_dataset
dataset_path = ".data/hf_dataset/propaganda_zanr"
dataset = load_dataset(dataset_path)
print(f"Length of train dataset: {len(dataset['train'])}")
print(f"Length of test dataset: {len(dataset['test'])}")
```
this prints
```
Generating train split: 1 examples [00:00, 72.10 examples/s]
Generating test split: 1 examples [00:00, 100.69 examples/s]
Length of train dataset: 1
Length of test dataset: 1
```
I dont' understand :(
### Expected behavior
same object is loaded
### Environment info
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] | null | 1 | 2024-02-04T08:49:31 | 2024-02-06T09:26:07 | 2024-02-06T09:11:45 | NONE | null | ### Describe the bug
unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte
### Steps to reproduce the bug
```
import sys
sys.getdefaultencoding()
'utf-8'
from datasets import load_dataset
print(f"Train dataset size: {len(dataset['train'])}")
print(f"Test dataset size: {len(dataset['test'])}")
Resolving data files: 100%
159/159 [00:00<00:00, 9909.28it/s]
Using custom data configuration samsum-0b1209637541c9e6
Downloading and preparing dataset json/samsum to C:/Users/Administrator/.cache/huggingface/datasets/json/samsum-0b1209637541c9e6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%
3/3 [00:00<00:00, 119.99it/s]
Extracting data files: 100%
3/3 [00:00<00:00, 9.54it/s]
Generating train split:
88392/0 [00:15<00:00, 86848.17 examples/s]
Generating test split:
0/0 [00:00<?, ? examples/s]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:132, in Json._generate_tables(self, files)
131 try:
--> 132 pa_table = paj.read_json(
133 io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
134 )
135 break
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\_json.pyx:290, in pyarrow._json.read_json()
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:100, in pyarrow.lib.check_status()
ArrowInvalid: JSON parse error: Invalid value. in row 0
During handling of the above exception, another exception occurred:
UnicodeDecodeError Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1819, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1818 _time = time.time()
-> 1819 for _, table in generator:
1820 if max_shard_size is not None and writer._num_bytes > max_shard_size:
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:153, in Json._generate_tables(self, files)
152 with open(file, encoding="utf-8") as f:
--> 153 dataset = json.load(f)
154 except json.JSONDecodeError:
File ~\AppData\Local\Programs\Python\Python310\lib\json\__init__.py:293, in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
276 """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing
277 a JSON document) to a Python object.
278
(...)
291 kwarg; otherwise ``JSONDecoder`` is used.
292 """
--> 293 return loads(fp.read(),
294 cls=cls, object_hook=object_hook,
295 parse_float=parse_float, parse_int=parse_int,
296 parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)
File ~\AppData\Local\Programs\Python\Python310\lib\codecs.py:322, in BufferedIncrementalDecoder.decode(self, input, final)
321 data = self.buffer + input
--> 322 (result, consumed) = self._buffer_decode(data, self.errors, final)
323 # keep undecoded input until the next call
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[81], line 5
1 from datasets import load_dataset
3 # Load dataset from the hub
4 #dataset = load_dataset("json",data_files="C:/Users/Administrator/Desktop/samsum/samsum/data/corpus/train.json",field="data")
----> 5 dataset = load_dataset('json',"samsum")
6 #dataset = load_dataset("samsum")
7 print(f"Train dataset size: {len(dataset['train'])}")
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py:1758, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1755 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1757 # Download and prepare data
-> 1758 builder_instance.download_and_prepare(
1759 download_config=download_config,
1760 download_mode=download_mode,
1761 ignore_verifications=ignore_verifications,
1762 try_from_hf_gcs=try_from_hf_gcs,
1763 num_proc=num_proc,
1764 )
1766 # Build dataset for splits
1767 keep_in_memory = (
1768 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1769 )
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:860, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
858 if num_proc is not None:
859 prepare_split_kwargs["num_proc"] = num_proc
--> 860 self._download_and_prepare(
861 dl_manager=dl_manager,
862 verify_infos=verify_infos,
863 **prepare_split_kwargs,
864 **download_and_prepare_kwargs,
865 )
866 # Sync info
867 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:953, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
949 split_dict.add(split_generator.split_info)
951 try:
952 # Prepare split will record examples associated to the split
--> 953 self._prepare_split(split_generator, **prepare_split_kwargs)
954 except OSError as e:
955 raise OSError(
956 "Cannot find data file. "
957 + (self.manual_download_instructions or "")
958 + "\nOriginal error:\n"
959 + str(e)
960 ) from None
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1708, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1706 gen_kwargs = split_generator.gen_kwargs
1707 job_id = 0
-> 1708 for job_id, done, content in self._prepare_split_single(
1709 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1710 ):
1711 if done:
1712 result = content
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1851, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1849 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1850 e = e.__context__
-> 1851 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1853 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
can't load dataset
### Environment info
dataset:samsum
system :win10
gpu:m40 24G | {
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https://api.github.com/repos/huggingface/datasets/issues/6640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6640/comments | https://api.github.com/repos/huggingface/datasets/issues/6640/events | https://github.com/huggingface/datasets/issues/6640 | 2,115,864,531 | I_kwDODunzps5-HYfT | 6,640 | Sign Language Support | {
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] | open | false | null | [] | null | 0 | 2024-02-02T21:54:51 | 2024-02-02T21:54:51 | null | NONE | null | ### Feature request
Currently, there are only several Sign Language labels, I would like to propose adding all the Signed Languages as new labels which are described in this ISO standard: https://www.evertype.com/standards/iso639/sign-language.html
### Motivation
Datasets currently only have labels for several signed languages. There are more signed languages in the world. Furthermore, some signed languages that have a lot of online data cannot be found because of this reason (for instance, German Sign Language, and there is no German Sign Language label on huggingface datasets even though there are a lot of readily available sign language datasets exist for German Sign Language, which are used very frequently in Sign Language Processing papers, and models.)
### Your contribution
I can submit a PR for this as well, adding the ISO codes and languages to the labels in datasets. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6639 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6639/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6639/comments | https://api.github.com/repos/huggingface/datasets/issues/6639/events | https://github.com/huggingface/datasets/pull/6639 | 2,114,620,200 | PR_kwDODunzps5l0KPG | 6,639 | Run download_and_prepare if missing splits | {
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} | [] | open | false | null | [] | null | 1 | 2024-02-02T10:36:49 | 2024-02-06T16:54:22 | null | MEMBER | null | A first step towards https://github.com/huggingface/datasets/issues/6529 | null | {
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} | [] | closed | false | null | [] | null | 1 | 2024-02-01T19:41:42 | 2024-02-01T20:07:29 | 2024-02-01T20:07:29 | NONE | null | ### Describe the bug
As of this morning (PST) 2/1/2024, seeing the wmt16 dataset is missing from opus , could you suggest an alternative?
```
Downloading data files: 0%| | 0/4 [00:00<?, ?it/s]Traceback (most recent call last):
File "test.py", line 2, in <module>
raw_datasets = load_dataset("wmt16","ro-en",split="train")
File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 2153, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 954, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1717, in _download_and_prepare
super()._download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1027, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/746749a11d25c02058042da7502d973ff410e73457f3d305fc1177dc0e8c4227/wmt_utils.py", line 754, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 565, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 428, in download
downloaded_path_or_paths = map_nested(
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 464, in map_nested
mapped = [
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 465, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 367, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 454, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 182, in cached_path
output_path = get_from_cache(
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 596, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz
```
### Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("wmt16","ro-en",split="train")
```
### Expected behavior
Expect the dataset to be downloaded/ at least a clean exit with error explaining dataset is missing and a suggestion for next steps
### Environment info
- `datasets` version: 2.14.7
- Platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.17.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.1
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https://api.github.com/repos/huggingface/datasets/issues/6637 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6637/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6637/comments | https://api.github.com/repos/huggingface/datasets/issues/6637/events | https://github.com/huggingface/datasets/issues/6637 | 2,113,025,975 | I_kwDODunzps598je3 | 6,637 | 'with_format' is extremely slow when used together with 'interleave_datasets' or 'shuffle' on IterableDatasets | {
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} | [] | open | false | null | [] | null | 1 | 2024-02-01T17:16:54 | 2024-02-05T10:43:47 | null | NONE | null | ### Describe the bug
If you:
1. Interleave two iterable datasets together with the interleave_datasets function, or shuffle an iterable dataset
2. Set the output format to torch tensors with .with_format('torch')
Then iterating through the dataset becomes over 100x slower than it is if you don't apply the torch formatting.
### Steps to reproduce the bug
```python
import datasets
import torch
from tqdm import tqdm
rand_a = torch.randn(3,224,224)
rand_b = torch.randn(3,224,224)
a = torch.stack([rand_a] * 1000)
b = torch.stack([rand_b] * 1000)
features = datasets.Features({"tensor": datasets.Array3D(shape=(3,224,224), dtype="float32")})
ds_a = datasets.Dataset.from_dict({"tensor": a}, features=features).to_iterable_dataset()
ds_b = datasets.Dataset.from_dict({"tensor": b}, features=features).to_iterable_dataset()
# Iterating through either dataset with torch formatting is really fast (2000it/s on my machine)
for example in tqdm(ds_a.with_format('torch')):
pass
# Iterating through either dataset shuffled is also pretty fast (100it/s on my machine)
for example in tqdm(ds_a.shuffle()):
pass
# Iterating through this interleaved dataset is pretty fast (200it/s on my machine)
ds_fast = datasets.interleave_datasets([ds_a, ds_b])
for example in tqdm(ds_fast):
pass
# Iterating through either dataset with torch formatting *after shuffling* is really slow... (<2it/s on my machine)
for example in tqdm(ds_a.shuffle().with_format('torch')):
pass
# Iterating through this torch formatted interleaved dataset is also really slow (<2it/s on my machine)...
ds_slow = datasets.interleave_datasets([ds_a, ds_b]).with_format('torch')
for example in tqdm(ds_slow):
pass
# Even doing this is way faster!! (70it/s on my machine)
for example in tqdm(ds_fast):
test = torch.tensor(example['tensor'])
```
### Expected behavior
Applying torch formatting to the interleaved dataset shouldn't increase the time taken to iterate through the dataset by very much, since even explicitly converting every example is over 70x faster than calling .with_format('torch').
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.38
- Python version: 3.11.6
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
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https://api.github.com/repos/huggingface/datasets/issues/6636 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6636/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6636/comments | https://api.github.com/repos/huggingface/datasets/issues/6636/events | https://github.com/huggingface/datasets/pull/6636 | 2,110,781,097 | PR_kwDODunzps5lm4zI | 6,636 | Faster column validation and reordering | {
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} | [] | closed | false | null | [] | null | 3 | 2024-01-31T19:08:28 | 2024-02-07T19:39:00 | 2024-02-06T23:03:38 | CONTRIBUTOR | null | I work with bioinformatics data and often these tables have thousands and even tens of thousands of features. These tables are also accompanied by metadata that I do not want to pass in the model. When I perform `set_format('pt', columns=large_column_list)` , it can take several minutes before it finishes. The culprit is when the following check is performed: `any(col not in self._data.column_names for col in columns)`. Replacing this by `set(columns) - (self._data.column_names)` is more efficient. | {
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https://api.github.com/repos/huggingface/datasets/issues/6635 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6635/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6635/comments | https://api.github.com/repos/huggingface/datasets/issues/6635/events | https://github.com/huggingface/datasets/pull/6635 | 2,110,659,519 | PR_kwDODunzps5lmeNO | 6,635 | Fix missing info when loading some datasets from Parquet export | {
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} | [] | closed | false | null | [] | null | 2 | 2024-01-31T17:55:21 | 2024-02-07T16:48:55 | 2024-02-07T16:41:04 | MEMBER | null | Fix getting the info for script-based datasets with Parquet export with a single config not named "default".
E.g.
```python
from datasets import load_dataset_builder
b = load_dataset_builder("bookcorpus")
print(b.info.features)
# should print {'text': Value(dtype='string', id=None)}
```
I fixed this by setting the default config name when there is only one config. | {
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https://api.github.com/repos/huggingface/datasets/issues/6634 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6634/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6634/comments | https://api.github.com/repos/huggingface/datasets/issues/6634/events | https://github.com/huggingface/datasets/pull/6634 | 2,110,242,376 | PR_kwDODunzps5llB9a | 6,634 | Support data_dir parameter in push_to_hub | {
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} | [] | closed | false | null | [] | null | 3 | 2024-01-31T14:37:36 | 2024-02-05T10:32:49 | 2024-02-05T10:26:40 | MEMBER | null | Support `data_dir` parameter in `push_to_hub`.
This allows users to organize the data files according to their specific needs. For example, "wikimedia/wikipedia" files could be organized by year and/or date, e.g. "2024/20240101/20240101.en". | {
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} | [] | closed | false | null | [] | null | 2 | 2024-01-31T13:41:54 | 2024-01-31T14:05:04 | 2024-01-31T13:59:01 | COLLABORATOR | null | null | {
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} | [] | closed | false | null | [] | null | 2 | 2024-01-30T18:52:23 | 2024-02-06T17:27:35 | 2024-02-06T17:21:24 | MEMBER | null | The cache used to only check for the latest cache directory with a given config_name, but it was wrong (e.g. `default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` instead of `default-data_dir=data%2Ffortran`)
I fixed this by not passing the `config_kwargs` to the parent Builder `__init__`, and passing the config_id forged from the `config_kwargs` directly
close https://github.com/huggingface/datasets/issues/6609 | {
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} | [] | closed | false | null | [] | null | 2 | 2024-01-30T12:56:01 | 2024-01-30T15:34:49 | 2024-01-30T15:28:37 | MEMBER | null | reported in https://github.com/huggingface/evaluate/issues/542
cc @stas00 @williamberrios
close https://github.com/huggingface/datasets/issues/6589 | {
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https://api.github.com/repos/huggingface/datasets/issues/6630 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6630/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6630/comments | https://api.github.com/repos/huggingface/datasets/issues/6630/events | https://github.com/huggingface/datasets/pull/6630 | 2,106,478,275 | PR_kwDODunzps5lYPi3 | 6,630 | Bump max range of dill to 0.3.8 | {
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} | [] | closed | false | null | [] | null | 4 | 2024-01-29T21:35:55 | 2024-01-30T16:19:45 | 2024-01-30T15:12:25 | CONTRIBUTOR | null | Release on Jan 27, 2024: https://pypi.org/project/dill/0.3.8/#history
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} | [] | closed | false | null | [] | null | 3 | 2024-01-29T15:36:52 | 2024-02-05T12:35:43 | 2024-02-05T12:29:36 | MEMBER | null | This behavior is aligned with:
- the behavior of `datasets` before merging #6519
- the behavior described in the corresponding docstring
- the behavior of `huggingface_hub.create_repo`
Revert "Support push_to_hub canonical datasets (#6519)"
- This reverts commit a887ee78835573f5d80f9e414e8443b4caff3541.
Fix #6597. | {
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} | [] | closed | false | null | [] | null | 3 | 2024-01-29T15:30:09 | 2024-02-05T10:29:20 | 2024-02-05T10:23:13 | MEMBER | null | Support passing `--num_proc` to CLI test.
This was really useful recently to run the command on `pubmed`: https://huggingface.co/datasets/pubmed/discussions/11 | {
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https://api.github.com/repos/huggingface/datasets/issues/6627 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6627/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6627/comments | https://api.github.com/repos/huggingface/datasets/issues/6627/events | https://github.com/huggingface/datasets/pull/6627 | 2,105,735,816 | PR_kwDODunzps5lVr-t | 6,627 | Disable `tqdm` bars in non-interactive environments | {
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For more info, see a [similar PR](https://github.com/huggingface/huggingface_hub/pull/2000) in `huggingface_hub`. | {
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This should add an error message on datasets with unsupported split names like https://huggingface.co/datasets/open-source-metrics/test
cc @AndreaFrancis | {
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} | [] | closed | false | null | [] | null | 1 | 2024-01-28T03:56:05 | 2024-02-06T09:43:31 | 2024-02-06T09:43:31 | NONE | null | The laion coco dataset is not available now. How to download it
https://huggingface.co/datasets/laion/laion-coco | {
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] | open | false | null | [] | null | 23 | 2024-01-27T23:46:13 | 2024-10-16T00:55:19 | null | NONE | null | ### Feature request
Let’s say I have a dataset with 5 samples with values [1, 2, 3, 4, 5], with 2 GPUs (for DDP) and batch size of 2. This dataset is an `IterableDataset` since I am streaming it.
Now I split the dataset using `split_dataset_by_node` to ensure it doesn’t get repeated. And since it’s already splitted, I don’t have to use `DistributedSampler` (also they don't work with iterable datasets anyway)?
But in this case I noticed that the:
First iteraton:
first GPU will get → [1, 2]
first GPU will get → [3, 4]
Second iteraton:
first GPU will get → [5]
first GPU will get → Nothing
which actually creates an issue since in case of `DistributedSampler`, the samples are repeated internally to ensure non of the GPUs at any iteration is missing any data for gradient sync.
So my questions are:
1. Here since splitting is happening before hand, how to make sure each GPU get’s a batch at each iteration to avoid gradient sync issues?
2. Do we need to use `DistributedSampler`? If yes, how?
3. in the docstrings of `split_dataset_by_node`, this is mentioned: *"If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples."* Can you explain the last part here?
4. If `dataset.n_shards % world_size != 0`, is it possible to shard the streaming dataset on the fly to avoid the case where data is missing?
### Motivation
Somehow streaming datasets should work with DDP since for big LLMs a lot of data is required and DDP/multi-node is mostly used to train such models and streaming can actually help solve the data part of it.
### Your contribution
Yes, I can help in submitting the PR once we get mutual understanding on how it should behave. | null | {
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} | [] | closed | false | null | [] | null | 1 | 2024-01-27T20:06:08 | 2024-02-08T11:18:21 | 2024-02-08T11:18:21 | NONE | null | ### Describe the bug
Here is the code for single-GPU processing: https://pastebin.com/bfmEeK2y
Here is the code for multi-GPU processing: https://pastebin.com/gQ7i5AQy
Here is the video showing that the multi-GPU mapping does not work as expected (there are so many things wrong here, it's better to watch the 3-minute video than explain here):
https://youtu.be/RNbdPkSppc4
### Steps to reproduce the bug
-
### Expected behavior
-
### Environment info
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] | null | 1 | 2024-01-27T01:00:09 | 2024-02-06T09:40:19 | 2024-02-06T09:40:19 | NONE | null | ### Describe the bug
I'm trying to run a rag example, and the dataset is wiki_dpr.
wiki_dpr download and extracting have been completed successfully.
However, at the generating train split stage, an error from wiki_dpr.py keeps popping up.
Especially in "_generate_examples" :
1. The following error occurs in the line **id, text, title = line.strip().split("\t")**
ValueError: not enough values to unpack (expected 3, got 2)
-> This part handles exceptions so that even if an error occurs, it passes.
2. **ID mismatch between lines {id} and vector {vec_id}**
This error seems to occur at the line " assert int(id) == int(vec_id),".
After I handled the exception in the split error, generating train split progressed to 80%, but an id mismatch error occurred at about the 16200000th vector id.
Debugging is even more difficult because it takes a long time to download and split wiki_dpr. I need help. thank you in advance!!
### Steps to reproduce the bug
Occurs in the generating train split step when running the rag example in the transformers repository.
Specifically, it is an error in wiki_dpr.py.
### Expected behavior
.
### Environment info
python 3.8 | {
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https://api.github.com/repos/huggingface/datasets/issues/6619 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6619/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6619/comments | https://api.github.com/repos/huggingface/datasets/issues/6619/events | https://github.com/huggingface/datasets/pull/6619 | 2,102,407,478 | PR_kwDODunzps5lK2VY | 6,619 | Migrate from `setup.cfg` to `pyproject.toml` | {
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} | [] | closed | false | null | [] | null | 2 | 2024-01-26T15:27:10 | 2024-01-26T15:53:40 | 2024-01-26T15:47:32 | COLLABORATOR | null | Based on https://github.com/huggingface/huggingface_hub/pull/1971 in `hfh` | {
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https://api.github.com/repos/huggingface/datasets/issues/6618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6618/comments | https://api.github.com/repos/huggingface/datasets/issues/6618/events | https://github.com/huggingface/datasets/issues/6618 | 2,101,868,198 | I_kwDODunzps59R_am | 6,618 | While importing load_dataset from datasets | {
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} | [] | closed | false | null | [] | null | 5 | 2024-01-26T09:21:57 | 2024-07-23T09:31:07 | 2024-02-06T09:25:54 | NONE | null | ### Describe the bug
cannot import name 'DEFAULT_CIPHERS' from 'urllib3.util.ssl_' this is the error i received
### Steps to reproduce the bug
from datasets import load_dataset
### Expected behavior
No errors
### Environment info
python 3.11.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/6617 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6617/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6617/comments | https://api.github.com/repos/huggingface/datasets/issues/6617/events | https://github.com/huggingface/datasets/pull/6617 | 2,100,459,449 | PR_kwDODunzps5lEagV | 6,617 | Fix CI: pyarrow 15, pandas 2.2 and sqlachemy | {
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} | [] | closed | false | null | [] | null | 2 | 2024-01-25T13:57:41 | 2024-01-26T14:56:46 | 2024-01-26T14:50:44 | MEMBER | null | this should fix the CI failures on `main`
close https://github.com/huggingface/datasets/issues/5477 | {
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