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1,607,054,110 | https://api.github.com/repos/huggingface/datasets/issues/5602/labels{/name} | This PR introduces new logic to `to_tf_dataset` affecting the returned data structure, enabling a dictionary structure to be returned, even if only one feature column is selected.
If the passed in `columns` or `label_cols` to `to_tf_dataset` are a list, they are returned as a dictionary, respectively. If they are a string, the tensor is returned.
An outline of the behaviour:
```
dataset,to_tf_dataset(columns=["col_1"], label_cols="col_2")
# ({'col_1': col_1}, col_2}
dataset,to_tf_dataset(columns="col1", label_cols="col_2")
# (col1, col2)
dataset,to_tf_dataset(columns="col1")
# col1
dataset,to_tf_dataset(columns=["col_1"], labels=["col_2"])
# ({'col1': tensor}, {'col2': tensor}}
dataset,to_tf_dataset(columns="col_1", labels=["col_2"])
# (col1, {'col2': tensor}}
```
## Motivation
Currently, when calling `to_tf_dataset`, the returned dataset will always return a tuple structure if a single feature column is used. This can cause issues when calling `model.fit` on models which train without labels e.g. [TFVitMAEForPreTraining](https://github.com/huggingface/transformers/blob/b6f47b539377ac1fd845c7adb4ccaa5eb514e126/src/transformers/models/vit_mae/modeling_vit_mae.py#L849). Specifically, [this line](https://github.com/huggingface/transformers/blob/d9e28d91a8b2d09b51a33155d3a03ad9fcfcbd1f/src/transformers/modeling_tf_utils.py#L1521) where it's assumed the input `x` is a dictionary if there is no label.
## Example
Previous behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec=TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)>
```
New behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec={'pixel_values': TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)}>
``` | 2023-04-12T15:54:53Z | 5,602 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-03-02T15:51:12Z | https://api.github.com/repos/huggingface/datasets/issues/5602/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5602/timeline | Return dict structure if columns are lists - to_tf_dataset | https://api.github.com/repos/huggingface/datasets/issues/5602/events | null | {
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} | PR_kwDODunzps5LJGfa | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5602). All of your documentation changes will be reflected on that endpoint.",
"This is a great PR! Thinking about the UX though, maybe we could do it without the extra argument? Before this PR, the logic in `to_tf_dataset` was... | {
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1,606,685,976 | https://api.github.com/repos/huggingface/datasets/issues/5601/labels{/name} | ### Describe the bug
Get `Authorization error` when try to push data into hugginface datasets hub.
### Steps to reproduce the bug
I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share),
1. `huggingface-cli login` with WRITE token
2. `git lfs install`
3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name`
4.
```
cp /somewhere/data/*.json .
git lfs track *.json
git add .gitattributes
git add *.json
git commit -m "add json files"
```
but when I execute `git push` I got the error:
```
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
batch response: Authorization error.
error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news'
```
Size of data ~100Gb. I have five json files - different parts.
### Expected behavior
All my data pushed into hub
### Environment info
- `datasets` version: 2.10.1
- Platform: macOS-13.2.1-arm64-arm-64bit
- Python version: 3.10.10
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | 2023-03-14T16:55:35Z | 5,601 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-03-02T12:08:39Z | https://api.github.com/repos/huggingface/datasets/issues/5601/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5601/timeline | Authorization error | https://api.github.com/repos/huggingface/datasets/issues/5601/events | null | {
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} | [] | null | completed | NONE | 2023-03-14T16:55:34Z | null | I_kwDODunzps5fxBUY | [
"Hi! \r\n\r\nIt's better to report this kind of issue in the `huggingface_hub` repo, so if you still haven't resolved it, I suggest you open an issue there.",
"Yeah, I solved it. Problem was in osxkeychain. When I do `hugginface-cli login` it's add token with default account (username)`hg_user` but my repo cont... | {
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1,606,585,596 | https://api.github.com/repos/huggingface/datasets/issues/5600/labels{/name} | ### Describe the bug
Dataloader getitem is not working as before (see example of [DreamboothDatasets](https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py#L451C14-L529))
moving Datasets to 2.8.0 solved the issue.
### Steps to reproduce the bug
1- using DreamBoothDataset to load some images
2- error after loading when trying to visualise the images
### Expected behavior
I was expecting a numpy array of the image
### Environment info
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5 | 2023-03-13T17:59:35Z | 5,600 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-03-02T11:00:27Z | https://api.github.com/repos/huggingface/datasets/issues/5600/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5600/timeline | Dataloader getitem not working for DreamboothDatasets | https://api.github.com/repos/huggingface/datasets/issues/5600/events | null | {
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} | [] | null | completed | NONE | 2023-03-13T17:59:35Z | null | I_kwDODunzps5fwoz8 | [
"Hi! \r\n\r\n> (see example of DreamboothDatasets)\r\n\r\n\r\nCould you please provide a link to it? If you are referring to the example in the `diffusers` repo, your issue is unrelated to `datasets` as that example uses `Dataset` from PyTorch to load data."
] | {
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1,605,018,478 | https://api.github.com/repos/huggingface/datasets/issues/5598/labels{/name} | As reported in https://github.com/vijaydwivedi75/lrgb/issues/10, `push_to_hub` fails if the remote repository already exists and has a README.md without `dataset_info` in the YAML tags
cc @clefourrier | 2023-03-02T13:47:13Z | 5,598 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-03-01T13:54:06Z | https://api.github.com/repos/huggingface/datasets/issues/5598/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5598/timeline | Fix push_to_hub with no dataset_infos | https://api.github.com/repos/huggingface/datasets/issues/5598/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | {
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1,604,928,721 | https://api.github.com/repos/huggingface/datasets/issues/5597/labels{/name} | ### Motivation
For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds = ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Feature request
Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds.add_item_({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Related Functions
* `.map`
* `.filter`
* `.add_item` | 2023-03-02T13:30:41Z | 5,597 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,604,919,993 | https://api.github.com/repos/huggingface/datasets/issues/5596/labels{/name} | ### Describe the bug
I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error:
```
casted_values = _c(array.values, feature[0])
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper
return func(array, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, 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
struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>>
to
{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}
```
But I can succesfully load a subset of the dataset, for example this works:
```python
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)])
```
and `ds.features` returns:
```
{'repo': Value(dtype='string', id=None),
'org': Value(dtype='string', id=None),
'issue_id': Value(dtype='int64', id=None),
'issue_number': Value(dtype='int64', id=None),
'pull_request': {'user_login': Value(dtype='string', id=None),
'repo': Value(dtype='string', id=None),
'number': Value(dtype='int64', id=None)},
'events': [{'type': Value(dtype='string', id=None),
'action': Value(dtype='string', id=None),
'datetime': Value(dtype='timestamp[s]', id=None),
'author': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None),
'description': Value(dtype='string', id=None),
'comment_id': Value(dtype='int64', id=None),
'comment': Value(dtype='string', id=None)}]}
```
So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue.
Side note:
I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train")
```
### Expected behavior
Load the entire dataset succesfully.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4 | 2023-12-05T03:22:00Z | 5,596 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-03-01T12:53:08Z | https://api.github.com/repos/huggingface/datasets/issues/5596/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5596/timeline | [TypeError: Couldn't cast array of type] Can only load a subset of the dataset | https://api.github.com/repos/huggingface/datasets/issues/5596/events | null | {
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"Apparently some JSON objects have a `\"labels\"` field. Since this field is not present in every object, you must specify all the fields types in the README.md\r\n\r\nEDIT: actually specifying the feature types doesn’t solve the issue, it raises an error because “labels” is missing in the data",
"We've updated t... | {
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1,604,070,629 | https://api.github.com/repos/huggingface/datasets/issues/5595/labels{/name} | Closes #5477 | 2023-04-04T08:20:19Z | 5,595 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-03-01T01:33:45Z | https://api.github.com/repos/huggingface/datasets/issues/5595/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5595/timeline | Unpins sqlAlchemy | https://api.github.com/repos/huggingface/datasets/issues/5595/events | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5595). All of your documentation changes will be reflected on that endpoint.",
"It looks like this issue hasn't been fixed yet, so let's wait a bit more.",
"@lazarust thanks for your work, but unfortunately we cannot merge it... | {
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1,603,980,995 | https://api.github.com/repos/huggingface/datasets/issues/5594/labels{/name} | ### Describe the bug
Hi,
I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed
```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4...
Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s]
Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last):
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single
for key, record in generator:
File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples
yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs)
File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples
for path, file in filepath:
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__
yield from self.generator(*self.args, **self.kwargs)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path
yield from cls._iter_tar(f)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar
for tarinfo in stream:
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__
tarinfo = self.next()
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next
raise ReadError("unexpected end of data")
tarfile.ReadError: unexpected end of data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module>
main()
File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main
train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload")
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset
builder_instance.download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare
self._download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare
super()._download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
```
train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload")
```
### Expected behavior
Download the udpos dataset
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.8
- PyArrow version: 10.0.1
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"Hi! I cannot reproduce this error on my machine.\r\n\r\nThe raised error could mean that one of the downloaded files is corrupted. To verify this is not the case, you can run `load_dataset` as follows:\r\n```python\r\ntrain_dataset = load_dataset('xtreme', 'udpos.English', split=\"train\", cache_dir=args.cache_dir... | {
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1,603,619,124 | https://api.github.com/repos/huggingface/datasets/issues/5592/labels{/name} | Fixes #5581 to use the correct output for the `set_format` method. | 2023-02-28T19:26:33Z | 5,592 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-28T18:42:37Z | https://api.github.com/repos/huggingface/datasets/issues/5592/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5592/timeline | Fix docstring example | https://api.github.com/repos/huggingface/datasets/issues/5592/events | null | {
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1,603,535,704 | https://api.github.com/repos/huggingface/datasets/issues/5589/labels{/name} | This reverts commit b91070b9c09673e2e148eec458036ab6a62ac042 (temporarily)
It hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images unnecessarily). I think we need to fix this before re-adding it
cc @mariosasko @Hubert-Bonisseur | 2023-09-24T10:07:33Z | 5,589 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-28T17:52:04Z | https://api.github.com/repos/huggingface/datasets/issues/5589/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5589/timeline | Revert "pass the dataset features to the IterableDataset.from_generator" | https://api.github.com/repos/huggingface/datasets/issues/5589/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | {
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1,603,304,766 | https://api.github.com/repos/huggingface/datasets/issues/5588/labels{/name} | Flatten a dataset on the fly in `save_to_disk` instead of doing it with `flatten_indices` to avoid creating an additional cache file.
(this is one of the sub-tasks in https://github.com/huggingface/datasets/issues/5507) | 2023-02-28T17:28:35Z | 5,588 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-28T15:37:46Z | https://api.github.com/repos/huggingface/datasets/issues/5588/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5588/timeline | Flatten dataset on the fly in `save_to_disk` | https://api.github.com/repos/huggingface/datasets/issues/5588/events | null | {
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1,603,139,420 | https://api.github.com/repos/huggingface/datasets/issues/5587/labels{/name} | Fixes the `key` range in the `query_table` call in `sort` to account for an indices mapping
Fix #5586 | 2023-02-28T17:28:57Z | 5,587 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-28T14:05:08Z | https://api.github.com/repos/huggingface/datasets/issues/5587/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5587/timeline | Fix `sort` with indices mapping | https://api.github.com/repos/huggingface/datasets/issues/5587/events | null | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | {
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1,602,961,544 | https://api.github.com/repos/huggingface/datasets/issues/5586/labels{/name} | ### Describe the bug
Hi, thank you for your support!
It seems like the addition of multiple key sort (#5502) in 2.10.0 broke the `.sort()` method.
After filtering a dataset with `.filter()`, the `.sort()` seems to refer to the query_table index of the previous unfiltered dataset, resulting in an IndexError.
This only happens with the 2.10.0 release.
### Steps to reproduce the bug
```Python
from datasets import load_dataset
# dataset with length of 1104
ds = load_dataset('glue', 'ax')['test']
ds = ds.filter(lambda x: x['idx'] > 1100)
ds.sort('premise')
print('Done')
```
File "/home/dongkeun/datasets_test/test.py", line 5, in <module>
ds.sort('premise')
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 528, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 511, in wrapper
out = func(dataset, *args, **kwargs)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3959, in sort
sort_table = query_table(
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 588, in query_table
_check_valid_index_key(key, size)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 537, in _check_valid_index_key
_check_valid_index_key(max(key), size=size)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 531, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 1103 is out of bounds for size 3
### Expected behavior
It should sort the dataset and print "Done". Which it does on 2.9.0.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | 2023-02-28T18:17:26Z | 5,586 | null | https://api.github.com/repos/huggingface/datasets | null | [
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} | [] | null | completed | NONE | 2023-02-28T17:21:59Z | null | I_kwDODunzps5fi0CI | [
"Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix"
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1,602,190,030 | https://api.github.com/repos/huggingface/datasets/issues/5585/labels{/name} | ### Describe the bug
I would like to share cache between two machines (a Windows host machine and a WSL instance).
I run most my code in WSL. I have just run out of space in the virtual drive. Rather than expand the drive size, I plan to move to cache to the host Windows machine, thereby sharing the downloads.
I'm hoping that I can just copy/paste the cache files, but I notice that a lot of the file names start with the path name, e.g. `_home_davidg_.cache_huggingface_datasets_conll2003_default-451...98.lock` where `home/davidg` is where the cache is in WSL.
This seems to suggest that the cache is not portable/cannot be centralised or shared. Is this the case, or are the files that start with path names not integral to the caching mechanism? Because copying the cache files _seems_ to work, but I'm not filled with confidence that something isn't going to break.
A related issue, when trying to load a dataset that should come from cache (running in WSL, pointing to cache on the Windows host) it seemed to work fine, but it still uses a WSL directory for `.cache\huggingface\modules\datasets_modules`. I see nothing in the docs about this, or how to point it to a different place.
I have asked a related question on the forum: https://discuss.huggingface.co/t/is-datasets-cache-operating-system-agnostic/32656
### Steps to reproduce the bug
View the cache directory in WSL/Windows.
### Expected behavior
Cache can be shared between (virtual) machines and be transportable.
It would be nice to have a simple way to say "Dear Hugging Face packages, please put ALL your cache in `blah/de/blah`" and have all the Hugging Face packages respect that single location.
### Environment info
```
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- ``` | 2023-02-28T21:26:52Z | 5,585 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-28T00:53:06Z | https://api.github.com/repos/huggingface/datasets/issues/5585/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5585/timeline | Cache is not transportable | https://api.github.com/repos/huggingface/datasets/issues/5585/events | null | {
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"Hi ! No the cache is not transportable in general. It will work on a shared filesystem if you use the same python environment, but not across machines/os/environments.\r\n\r\nIn particular, reloading cached datasets does work, but reloading cached processed datasets (e.g. from `map`) may not work. This is because ... | {
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1,601,821,808 | https://api.github.com/repos/huggingface/datasets/issues/5584/labels{/name} | ### Describe the bug
Seeing this error when downloading https://huggingface.co/datasets/kakaobrain/coyo-700m:
```ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.```
Full stack trace
```Downloading and preparing dataset parquet/kakaobrain--coyo-700m to /root/.cache/huggingface/datasets/kakaobrain___parquet/kakaobrain--coyo-700m-ae729692ae3e0073/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 100%
1/1 [00:00<00:00, 63.35it/s]
Extracting data files: 100%
1/1 [00:00<00:00, 5.00it/s]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1859 _time = time.time()
-> 1860 for _, table in generator:
1861 if max_shard_size is not None and writer._num_bytes > max_shard_size:
9 frames
ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1891 e = e.__context__
-> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1893
1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset```
### Steps to reproduce the bug
```
from datasets import load_dataset
hf_dataset = load_dataset("kakaobrain/coyo-700m")
```
### Expected behavior
The above commands load the dataset successfully. Or handles exception and continue loading the remainder.
### Environment info
colab. any | 2023-02-28T07:27:59Z | 5,584 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-27T19:35:03Z | https://api.github.com/repos/huggingface/datasets/issues/5584/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5584/timeline | Unable to load coyo700M dataset | https://api.github.com/repos/huggingface/datasets/issues/5584/events | null | {
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"Hi @manuaero \r\n\r\nThank you for your interest in the COYO dataset.\r\n\r\nOur dataset provides the img-url and alt-text in the form of a parquet, so to utilize the coyo dataset you will need to download it directly.\r\n\r\nWe provide a [guide](https://github.com/kakaobrain/coyo-dataset/blob/main/download/README... | {
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1,601,583,625 | https://api.github.com/repos/huggingface/datasets/issues/5583/labels{/name} | Ensures all the writers that use Pandas for conversion (JSON, CSV, SQL) do not export `index` by default (https://github.com/huggingface/datasets/pull/5490 only did this for CSV) | 2023-02-28T13:52:15Z | 5,583 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-27T17:04:46Z | https://api.github.com/repos/huggingface/datasets/issues/5583/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5583/timeline | Do no write index by default when exporting a dataset | https://api.github.com/repos/huggingface/datasets/issues/5583/events | null | {
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1,600,932,092 | https://api.github.com/repos/huggingface/datasets/issues/5582/labels{/name} | This PR closes #5383
* Add column_names property to IterableDataset
* Add multiple tests for this new property | 2023-03-13T19:10:22Z | 5,582 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-27T10:50:07Z | https://api.github.com/repos/huggingface/datasets/issues/5582/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5582/timeline | Add column_names to IterableDataset | https://api.github.com/repos/huggingface/datasets/issues/5582/events | null | {
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1,600,675,489 | https://api.github.com/repos/huggingface/datasets/issues/5581/labels{/name} | ### Describe the bug
https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format
<img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png">
While actually running it will result in:
<img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png">
### Steps to reproduce the bug
_
### Expected behavior
_
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5 | 2023-02-28T19:19:17Z | 5,581 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,600,431,792 | https://api.github.com/repos/huggingface/datasets/issues/5580/labels{/name} | Closes https://github.com/huggingface/datasets/issues/5281
This PR uses fsspec to support datasets on cloud storage (tested manually with GCS). ETags are currently unsupported for cloud storage. In general, a much larger refactor could be done to just use fsspec for all schemes (ftp, http/s, s3, gcs) to unify the interfaces here, but I ultimately opted to leave that out of this PR
I didn't create a GCS filesystem class in `datasets.filesystems` since the S3 one appears to be a wrapper around `s3fs.S3FileSystem` and mainly used to generate docs. | 2023-03-11T01:02:49Z | 5,580 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-27T04:06:05Z | https://api.github.com/repos/huggingface/datasets/issues/5580/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5580/timeline | Support cloud storage in load_dataset via fsspec | https://api.github.com/repos/huggingface/datasets/issues/5580/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Regarding the tests I think it should be possible to use the mockfs fixture, it allows to play with a dummy fsspec FileSystem with the \"mock://\" protocol.\r\n\r\n> However it requires some storage_options to be passed. Maybe it c... | {
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1,599,732,211 | https://api.github.com/repos/huggingface/datasets/issues/5579/labels{/name} | The following adds instructions on how to create a `DataLoader` from the guide on how to use object detection with augmentations (#4710). I am open to hearing any suggestions for improvement ! | 2023-03-23T19:24:59Z | 5,579 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-25T14:53:17Z | https://api.github.com/repos/huggingface/datasets/issues/5579/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5579/timeline | Add instructions to create `DataLoader` from augmented dataset in object detection guide | https://api.github.com/repos/huggingface/datasets/issues/5579/events | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5579). All of your documentation changes will be reflected on that endpoint.",
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1,598,587,665 | https://api.github.com/repos/huggingface/datasets/issues/5577/labels{/name} | ### Describe the bug
I met the same bug mentioned in #3053 which is never fixed. Because several `reddit_scores` are larger than `int8` even `int16`. https://huggingface.co/datasets/the_pile_openwebtext2/blob/main/the_pile_openwebtext2.py#L62
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("the_pile_openwebtext2")
```
### Expected behavior
load as normal.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | 2023-02-24T14:01:09Z | 5,577 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-24T13:01:48Z | https://api.github.com/repos/huggingface/datasets/issues/5577/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5577/timeline | Cannot load `the_pile_openwebtext2` | https://api.github.com/repos/huggingface/datasets/issues/5577/events | null | {
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"Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.\r\n\r\n"
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1,598,582,744 | https://api.github.com/repos/huggingface/datasets/issues/5576/labels{/name} | I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.
I worked around this by downloading the `the_pile_openwebtext2.py` and editing it to use local files and drop reddit scores as a column (not needed for my purposes).
_Originally posted by @tc-wolf in https://github.com/huggingface/datasets/issues/3053#issuecomment-1281392422_
| 2023-02-24T12:58:31Z | 5,576 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-24T12:57:49Z | https://api.github.com/repos/huggingface/datasets/issues/5576/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5576/timeline | I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers. | https://api.github.com/repos/huggingface/datasets/issues/5576/events | null | {
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1,598,396,552 | https://api.github.com/repos/huggingface/datasets/issues/5575/labels{/name} | ### Feature request
Being able to put some metadata for each column as a string or any other type.
### Motivation
I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata
### Your contribution
Maybe we could map another relational like database as the metadata? | 2024-01-05T21:48:35Z | 5,575 | {
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"Hi! Indeed it would be useful to support this. PyArrow natively supports schema-level and column-level metadata, so implementing this should be straightforward. The API I have in mind would work as follows:\r\n```python\r\ncol_feature = Value(\"string\", metadata=\"Some column-level metadata\")\r\n\r\nfeatures = F... | {
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1,598,104,691 | https://api.github.com/repos/huggingface/datasets/issues/5574/labels{/name} | ### Describe the bug
Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("c4", "en", split="train", streaming=True)
next(iter(dataset))
```
causes a
```
FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz
```
I can download this file manually though e.g. by entering this URL in a browser.
There is an underlying HTTP 403 status code:
```
aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX')
```
### Expected behavior
This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
| 2023-12-18T07:32:32Z | 5,574 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-24T07:57:32Z | https://api.github.com/repos/huggingface/datasets/issues/5574/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5574/timeline | c4 dataset streaming fails with `FileNotFoundError` | https://api.github.com/repos/huggingface/datasets/issues/5574/events | null | {
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} | [] | null | completed | NONE | 2023-02-27T04:03:38Z | null | I_kwDODunzps5fQSRz | [
"Also encountering this issue for every dataset I try to stream! Installed datasets from main:\r\n```\r\n- `datasets` version: 2.10.1.dev0\r\n- Platform: macOS-13.1-arm64-arm-64bit\r\n- Python version: 3.9.13\r\n- PyArrow version: 10.0.1\r\n- Pandas version: 1.5.2\r\n```\r\n\r\nRepro:\r\n```python\r\nfrom datasets ... | {
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1,597,400,836 | https://api.github.com/repos/huggingface/datasets/issues/5573/labels{/name} | I've removed `torchaudio` completely and switched to use `soundfile` for everything. With the new version of `soundfile` package this should work smoothly because the `libsndfile` C library is bundled, in Linux wheels too.
Let me know if you think it's too harsh and we should continue to support `torchaudio` decoding.
I decided that we can drop it completely because:
1. it's always something wrong with `torchaudio` (for example recently https://github.com/huggingface/datasets/issues/5488 )
2. the results of mp3 decoding are different depending on `torchaudio` version
3. `soundfile` is slightly faster then the latest `torchaudio`
4. anyway users can pass any custom decoding function with any library they want if needed (worth putting a snippet in the docs).
cc @sanchit-gandhi @vaibhavad | 2023-02-28T20:25:14Z | 5,573 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-23T19:19:44Z | https://api.github.com/repos/huggingface/datasets/issues/5573/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5573/timeline | Use soundfile for mp3 decoding instead of torchaudio | https://api.github.com/repos/huggingface/datasets/issues/5573/events | null | {
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} | [] | null | null | CONTRIBUTOR | 2023-02-28T20:16:02Z | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@mariosasko thank you for the review! do you have any idea why `test_hash_torch_tensor` fails on \"ubuntu-latest deps-minimum\"? I removed the `torchaudio<0.12.0` test dependency so it uses the latest `torch` now, might it be connect... | {
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1,597,257,624 | https://api.github.com/repos/huggingface/datasets/issues/5572/labels{/name} | ### Describe the bug
download_mode="reuse_dataset_if_exists" will always consider that a dataset doesn't exist.
Specifically, upon losing an internet connection trying to load a dataset for a second time in ten seconds, a connection error results, showing a breakpoint of:
```
File ~/jupyterlab/.direnv/python-3.9.6/lib/python3.9/site-packages/datasets/load.py:1174, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1165 except Exception as e: # noqa: catch any exception of hf_hub and consider that the dataset doesn't exist
1166 if isinstance(
1167 e,
1168 (
(...)
1172 ),
1173 ):
-> 1174 raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})")
1175 elif "404" in str(e):
1176 msg = f"Dataset '{path}' doesn't exist on the Hub"
ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
This has been around since at least v2.0.
### Steps to reproduce the bug
```
from datasets import load_dataset
import numpy as np
tenk = load_dataset("lsb/tenk") # ten thousand integers
print(np.average(tenk['train']['a'])) # prints 4999.5
### now disconnect your internet
tenk_too = load_dataset("lsb/tenk", download_mode="reuse_dataset_if_exists")
# Raises ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
### Expected behavior
I expected that I would be able to reuse the dataset I just downloaded.
### Environment info
- `datasets` version: 2.10.0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.2 | 2023-02-23T18:03:55Z | 5,572 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-23T17:28:11Z | https://api.github.com/repos/huggingface/datasets/issues/5572/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5572/timeline | Datasets 2.10.0 does not reuse the dataset cache | https://api.github.com/repos/huggingface/datasets/issues/5572/events | null | {
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1,597,198,953 | https://api.github.com/repos/huggingface/datasets/issues/5571/labels{/name} | ### Describe the bug
Steps:
1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method.
2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json"
3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON.
4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py.
raise InvalidConfigName(
f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. "
f"They could create issues when creating a directory for this config on Windows filesystem."
6. When I bring the data to the current directory, it works fine.
### Steps to reproduce the bug
Steps:
1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method.
2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json"
3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON.
4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py.
raise InvalidConfigName(
f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. "
f"They could create issues when creating a directory for this config on Windows filesystem."
6. When I bring the data to the current directory, it works fine.
### Expected behavior
Should be able to read from a path different than current directory in Windows machine.
### Environment info
datasets version: 2.3.1
python version: 3.8
Windows OS | 2023-02-24T13:21:47Z | 5,571 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-23T16:50:11Z | https://api.github.com/repos/huggingface/datasets/issues/5571/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5571/timeline | load_dataset fails for JSON in windows | https://api.github.com/repos/huggingface/datasets/issues/5571/events | null | {
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} | [] | null | completed | NONE | 2023-02-24T13:21:47Z | null | I_kwDODunzps5fM1Jp | [
"Hi! \r\n\r\nYou need to pass an input json file explicitly as `data_files` to `load_dataset` to avoid this error:\r\n```python\r\n ds = load_dataset(\"json\", data_files=args.input_json)\r\n```\r\n\r\n",
"Thanks it worked!"
] | {
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1,597,190,926 | https://api.github.com/repos/huggingface/datasets/issues/5570/labels{/name} | ### Describe the bug
When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting.
### Steps to reproduce the bug
```
from datasets import load_dataset
imagenet = load_dataset("imagenet-1k", split="train", streaming=True)
FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub
```
tested on a colab notebook.
### Expected behavior
I would expect a specific error indicating that I have to login then accept the dataset licence.
I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable)
### Environment info
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"Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?",
"The error is now more informative:\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the sa... | {
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"_The documentation is not available anymore as the PR was closed or merged._",
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1,596,900,532 | https://api.github.com/repos/huggingface/datasets/issues/5568/labels{/name} | ### Describe the bug
Hello,
I like the new `to_iterable_dataset` feature but I noticed something that seems to be missing.
When using `to_iterable_dataset` to transform your map style dataset into iterable dataset, you lose valuable metadata like the features.
These metadata are useful if you want to interleave iterable datasets, cast columns etc.
### Steps to reproduce the bug
```python
dataset = load_dataset("lhoestq/demo1")["train"]
print(dataset.features)
# {'id': Value(dtype='string', id=None), 'package_name': Value(dtype='string', id=None), 'review': Value(dtype='string', id=None), 'date': Value(dtype='string', id=None), 'star': Value(dtype='int64', id=None), 'version_id': Value(dtype='int64', id=None)}
dataset = dataset.to_iterable_dataset()
print(dataset.features)
# None
```
### Expected behavior
Keep the relevant information
### Environment info
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"Hi ! Oh good catch. I think the features should be passed to `IterableDataset.from_generator()` in `to_iterable_dataset()` indeed.\r\n\r\nSetting this as a good first issue if someone would like to contribute, otherwise we can take care of it :)",
"#self-assign",
"seems like the feature parameter is missing fr... | {
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1,595,916,674 | https://api.github.com/repos/huggingface/datasets/issues/5566/labels{/name} | ### Feature request
Right now, we have to read the get the parquet file to the local storage. So having ability to read given the bucket directly address would be benificial
### Motivation
In a production set up, this feature can help us a lot. So we do not need move training datafiles in between storage.
### Your contribution
I am willing to help if there's anyway. | 2023-02-23T11:03:29Z | 5,566 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi ! I think is in the scope of this other issue: to https://github.com/huggingface/datasets/issues/5281 "
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1,595,281,752 | https://api.github.com/repos/huggingface/datasets/issues/5565/labels{/name} | This way we can control the size of the record_batches/row_groups of arrow/parquet files.
This can be useful for `datasets-server` to keep control of the row groups size which can affect random access performance for audio/image/video datasets
Right now having 1,000 examples per row group cause some image datasets to be pretty slow for random access (e.g. 4seconds for `beans` to get 20 rows) | 2023-03-10T13:53:03Z | 5,565 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-22T15:09:30Z | https://api.github.com/repos/huggingface/datasets/issues/5565/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5565/timeline | Add writer_batch_size for ArrowBasedBuilder | https://api.github.com/repos/huggingface/datasets/issues/5565/events | null | {
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1,594,625,539 | https://api.github.com/repos/huggingface/datasets/issues/5562/labels{/name} | Removed mangle_dup_cols=True from BuilderConfig.
It triggered following deprecation warning:
/usr/local/lib/python3.8/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
Further documentation of pandas: https://pandas.pydata.org/docs/whatsnew/v1.4.0.html#mangle-dupe-cols-in-read-csv-no-longer-renames-unique-columns-conflicting-with-target-names
At first sight it seems like this flag is resolved internally, it might need some more research. | 2023-02-23T11:07:49Z | 5,562 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-22T07:56:10Z | https://api.github.com/repos/huggingface/datasets/issues/5562/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5562/timeline | Update csv.py | https://api.github.com/repos/huggingface/datasets/issues/5562/events | null | {
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1,593,862,388 | https://api.github.com/repos/huggingface/datasets/issues/5561/labels{/name} | @huggingface/datasets do you think it would be useful? Motivation - sometimes PRs are like 30% "fix: style" commits :)
If so - I need to double check the config but for me locally it works as expected. | 2023-02-28T15:37:22Z | 5,561 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-21T17:35:07Z | https://api.github.com/repos/huggingface/datasets/issues/5561/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5561/timeline | Add pre-commit config yaml file to enable automatic code formatting | https://api.github.com/repos/huggingface/datasets/issues/5561/events | null | {
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"@Skylion007 hi! I agree with @nateraw here, I'd better not force to use ... | {
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1,593,809,978 | https://api.github.com/repos/huggingface/datasets/issues/5560/labels{/name} | This PR modifies `map` to:
* ensure the TQDM bar gets the last progress update
* when a map function fails, avoid throwing a chained exception in the single-proc mode | 2023-02-21T18:26:23Z | 5,560 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-21T16:56:17Z | https://api.github.com/repos/huggingface/datasets/issues/5560/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5560/timeline | Ensure last tqdm update in `map` | https://api.github.com/repos/huggingface/datasets/issues/5560/events | null | {
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1,593,676,489 | https://api.github.com/repos/huggingface/datasets/issues/5559/labels{/name} | #5455 introduced a small bug that lead `map` to ignore the `suffix_template` argument and not put suffixes to cached files in multiprocessing.
I fixed this and also improved a few things:
- regarding logging: "Loading cached processed dataset" is now logged only once even in multiprocessing (it used to be logged `num_proc` times)
- regarding new_fingerprint: I made sure that the returned dataset satisfies `ds._fingerprint==new_fingerprint` if `new_fingerprint` is passed to `map` | 2023-02-21T17:21:37Z | 5,559 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-21T15:26:26Z | https://api.github.com/repos/huggingface/datasets/issues/5559/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5559/timeline | Fix map suffix_template | https://api.github.com/repos/huggingface/datasets/issues/5559/events | null | {
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1,593,655,815 | https://api.github.com/repos/huggingface/datasets/issues/5558/labels{/name} | Colab now has Ubuntu 20.04 which already has `ffmpeg` of required (>4) version. | 2023-03-01T13:46:04Z | 5,558 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-21T15:13:36Z | https://api.github.com/repos/huggingface/datasets/issues/5558/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5558/timeline | Remove instructions for `ffmpeg` system package installation on Colab | https://api.github.com/repos/huggingface/datasets/issues/5558/events | null | {
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1,593,545,324 | https://api.github.com/repos/huggingface/datasets/issues/5557/labels{/name} | Otherwise it would show a `Map` progress bar, since it uses `map` under the hood | 2023-02-21T14:19:54Z | 5,557 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-21T14:04:42Z | https://api.github.com/repos/huggingface/datasets/issues/5557/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5557/timeline | Add filter desc | https://api.github.com/repos/huggingface/datasets/issues/5557/events | null | {
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1,593,246,936 | https://api.github.com/repos/huggingface/datasets/issues/5556/labels{/name} | ...instead of relying on the optional librosa dependency `resampy`.
It was only used for `_decode_non_mp3_file_like` anyway and not for the other ones - removing it fixes consistency between decoding methods (except torchaudio decoding)
Therefore I think it is a better solution than adding `resampy` as a dependency in https://github.com/huggingface/datasets/pull/5554
cc @polinaeterna | 2023-02-21T12:49:50Z | 5,556 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-21T10:45:50Z | https://api.github.com/repos/huggingface/datasets/issues/5556/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5556/timeline | Use default audio resampling type | https://api.github.com/repos/huggingface/datasets/issues/5556/events | null | {
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1,592,469,938 | https://api.github.com/repos/huggingface/datasets/issues/5555/labels{/name} | ### Describe the bug
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [16], line 1
----> 1 train_dataset = train_dataset.shuffle()
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3616, in Dataset.shuffle(self, seed, generator, keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint)
3610 return self._new_dataset_with_indices(
3611 fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name
3612 )
3614 permutation = generator.permutation(len(self))
-> 3616 return self.select(
3617 indices=permutation,
3618 keep_in_memory=keep_in_memory,
3619 indices_cache_file_name=indices_cache_file_name if not keep_in_memory else None,
3620 writer_batch_size=writer_batch_size,
3621 new_fingerprint=new_fingerprint,
3622 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3266, in Dataset.select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
3263 return self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
3265 # If not contiguous, we need to create a new indices mapping
-> 3266 return self._select_with_indices_mapping(
3267 indices,
3268 keep_in_memory=keep_in_memory,
3269 indices_cache_file_name=indices_cache_file_name,
3270 writer_batch_size=writer_batch_size,
3271 new_fingerprint=new_fingerprint,
3272 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3389, in Dataset._select_with_indices_mapping(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
3387 logger.info(f"Caching indices mapping at {indices_cache_file_name}")
3388 tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
-> 3389 writer = ArrowWriter(
3390 path=tmp_file.name, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices"
3391 )
3393 indices = indices if isinstance(indices, list) else list(indices)
3395 size = len(self)
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_writer.py:315, in ArrowWriter.__init__(self, schema, features, path, stream, fingerprint, writer_batch_size, hash_salt, check_duplicates, disable_nullable, update_features, with_metadata, unit, embed_local_files, storage_options)
312 self._disable_nullable = disable_nullable
314 if stream is None:
--> 315 fs_token_paths = fsspec.get_fs_token_paths(path, storage_options=storage_options)
316 self._fs: fsspec.AbstractFileSystem = fs_token_paths[0]
317 self._path = (
318 fs_token_paths[2][0]
319 if not is_remote_filesystem(self._fs)
320 else self._fs.unstrip_protocol(fs_token_paths[2][0])
321 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:593, in get_fs_token_paths(urlpath, mode, num, name_function, storage_options, protocol, expand)
591 else:
592 urlpath = stringify_path(urlpath)
--> 593 chain = _un_chain(urlpath, storage_options or {})
594 if len(chain) > 1:
595 inkwargs = {}
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:330, in _un_chain(path, kwargs)
328 for bit in reversed(bits):
329 protocol = split_protocol(bit)[0] or "file"
--> 330 cls = get_filesystem_class(protocol)
331 extra_kwargs = cls._get_kwargs_from_urls(bit)
332 kws = kwargs.get(protocol, {})
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/registry.py:240, in get_filesystem_class(protocol)
238 if protocol not in registry:
239 if protocol not in known_implementations:
--> 240 raise ValueError("Protocol not known: %s" % protocol)
241 bit = known_implementations[protocol]
242 try:
ValueError: Protocol not known: parent
```
This is what the `train_dataset` object looks like
```
Dataset({
features: ['label', 'input_ids', 'attention_mask'],
num_rows: 364166
})
```
### Steps to reproduce the bug
The `train_dataset` obj is created by concatenating two datasets
And then shuffle is called, but it throws the mentioned error.
### Expected behavior
Should shuffle the dataset properly.
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-1022-aws-x86_64-with-glibc2.31
- Python version: 3.9.13
- PyArrow version: 10.0.0
- Pandas version: 1.4.4
| 2023-02-27T09:23:34Z | 5,555 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-20T21:33:45Z | https://api.github.com/repos/huggingface/datasets/issues/5555/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5555/timeline | `.shuffle` throwing error `ValueError: Protocol not known: parent` | https://api.github.com/repos/huggingface/datasets/issues/5555/events | null | {
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"Hi ! The indices mapping is written in the same cachedirectory as your dataset.\r\n\r\nCan you run this to show your current cache directory ?\r\n```python\r\nprint(train_dataset.cache_files)\r\n```",
"```\r\n[{'filename': '.../train/dataset.arrow'}, {'filename': '.../train/dataset.arrow'}]\r\n```\r\n\r\nThese a... | {
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1,592,285,062 | https://api.github.com/repos/huggingface/datasets/issues/5554/labels{/name} | In librosa 0.10 they removed the `resmpy` dependency and set it to optional.
However it is necessary for resampling. I added it to the "audio" extra dependencies. | 2023-09-24T10:07:29Z | 5,554 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-20T18:15:43Z | https://api.github.com/repos/huggingface/datasets/issues/5554/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5554/timeline | Add resampy dep | https://api.github.com/repos/huggingface/datasets/issues/5554/events | null | {
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1,592,236,998 | https://api.github.com/repos/huggingface/datasets/issues/5553/labels{/name} | Solves #5539 | 2023-02-21T13:08:25Z | 5,553 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-20T17:29:14Z | https://api.github.com/repos/huggingface/datasets/issues/5553/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5553/timeline | improved message error row formatting | https://api.github.com/repos/huggingface/datasets/issues/5553/events | null | {
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1,592,186,703 | https://api.github.com/repos/huggingface/datasets/issues/5552/labels{/name} | Fix for https://discord.com/channels/879548962464493619/1075729627546406912/1075729627546406912
| 2023-02-21T13:20:42Z | 5,552 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-20T16:50:09Z | https://api.github.com/repos/huggingface/datasets/issues/5552/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5552/timeline | Make tiktoken tokenizers hashable | https://api.github.com/repos/huggingface/datasets/issues/5552/events | null | {
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1,592,140,836 | https://api.github.com/repos/huggingface/datasets/issues/5551/labels{/name} | This is kinda unimportant fix but, the suggested `pip install sklearn` does not work.
The current error message if sklearn is not installed:
```
ImportError: To be able to use [dataset name], you need to install the following dependency: sklearn.
Please install it using 'pip install sklearn' for instance.
```
| 2023-02-21T13:27:57Z | 5,551 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-20T16:16:57Z | https://api.github.com/repos/huggingface/datasets/issues/5551/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5551/timeline | Suggest scikit-learn instead of sklearn | https://api.github.com/repos/huggingface/datasets/issues/5551/events | null | {
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"good catch!",
"_The documentation is not available anymore as the PR was closed or merged._",
"The test fail is unrelated to this PR and fixed on `main` - merging :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: ... | {
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1,591,409,475 | https://api.github.com/repos/huggingface/datasets/issues/5550/labels{/name} | Hello!
## Pull Request overview
* Resolve 4 broken references in the docs
## The problems
Two broken references [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.class_encode_column):
---
One broken reference [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique):
---
One missing reference [here](https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column):
- Tom Aarsen | 2023-02-20T15:16:13Z | 5,550 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-20T08:52:11Z | https://api.github.com/repos/huggingface/datasets/issues/5550/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5550/timeline | Resolve four broken refs in the docs | https://api.github.com/repos/huggingface/datasets/issues/5550/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"See the resolved changes [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.Dataset.class_encode_column), [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/pa... | {
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1,590,836,848 | https://api.github.com/repos/huggingface/datasets/issues/5549/labels{/name} | Fix #5548
Apply ruff's flake8-comprehension checks for better performance, and more readable code. | 2023-02-23T14:06:39Z | 5,549 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-19T20:09:28Z | https://api.github.com/repos/huggingface/datasets/issues/5549/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5549/timeline | Apply ruff flake8-comprehension checks | https://api.github.com/repos/huggingface/datasets/issues/5549/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | {
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1,590,835,479 | https://api.github.com/repos/huggingface/datasets/issues/5548/labels{/name} | ### Feature request
Apply ruff flake8 comprehension checks to codebase.
### Motivation
This should strictly improve the performance / readability of the codebase by removing unnecessary iteration, function calls, etc. This should generate better Python bytecode which should strictly improve performance.
I already applied this fixes to PyTorch and Sympy with little issue and have opened PRs to diffusers and transformers todo this as well.
### Your contribution
Making a PR. | 2023-02-23T13:59:41Z | 5,548 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,590,468,200 | https://api.github.com/repos/huggingface/datasets/issues/5547/labels{/name} | ## What's in this PR?
After exploring for a while the JAX integration in 🤗`datasets`, I found out that, even though JAX prioritizes the TPU and GPU as the default device when available, the `JaxFormatter` doesn't let you specify the device where you want to place the `jax.Array`s in case you don't want to rely on JAX's default array placement.
So on, I've included the `device` param in `JaxFormatter` but there are some things to take into consideration:
* A formatted `Dataset` is copied with `copy.deepcopy` which means that if one adds the param `device` in `JaxFormatter` as a `jaxlib.xla_extension.Device`, it "fails" because that object cannot be serialized (instead of serializing the param adds a random hash instead). That's the reason why I added a function `_map_devices_to_str` to basically create a mapping of strings to `jaxlib.xla_extension.Device`s so that `self.device` is a string and not a `jaxlib.xla_extension.Device`.
* To create a `jax.Array` in a device you need to either create it in the default device and then move it to the desired device with `jax.device_put` or directly create it in the device you want with `jax.default_device()` context manager.
* JAX will create an array by default in `jax.devices()[0]`
More information on JAX device management is available at https://jax.readthedocs.io/en/latest/faq.html#controlling-data-and-computation-placement-on-devices
## What's missing in this PR?
I've tested it both locally in CPU (Mac M2 and Mac M1, as no GPU support for Mac yet), and in GPU and TPU in Google Colab, let me know if you want me to provide you the Notebook for the latter.
But I did not implement any integration test as I wanted to get your feedback first. | 2023-02-21T16:10:55Z | 5,547 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-18T20:57:40Z | https://api.github.com/repos/huggingface/datasets/issues/5547/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5547/timeline | Add JAX device selection when formatting | https://api.github.com/repos/huggingface/datasets/issues/5547/events | null | {
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"The code below was throwing a warning:\r\n\r\n```python\r\nclass JaxFormatter(Formatter[Mapping, \"jax.Array\", Mapping]):\r\n def __init__(self, features=None, device=None, **jnp_array_kwargs):\r\n super().__init__(features=features)\r\n import jax\r\n from jaxlib.xla_extension import Devi... | {
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1,590,346,349 | https://api.github.com/repos/huggingface/datasets/issues/5546/labels{/name} | ### Describe the bug
In the huggingface course (https://huggingface.co/course/chapter3/2?fw=pt) it said that if we set HF_HOME, downloaded datasets would be cached at specified address but it does not. downloaded models from checkpoint names are downloaded and cached at HF_HOME but this is not the case for datasets, they are still cached at ~/.cache/huggingface/datasets.
### Steps to reproduce the bug
Run the following code
```
from datasets import load_dataset
raw_datasets = load_dataset("glue", "mrpc")
raw_datasets
```
it downloads and store dataset at ~/.cache/huggingface/datasets
### Expected behavior
to cache dataset at HF_HOME.
### Environment info
python 3.10.6
Kubuntu 22.04
HF_HOME located on a separate partition | 2023-07-24T14:22:43Z | 5,546 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-18T13:30:35Z | https://api.github.com/repos/huggingface/datasets/issues/5546/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5546/timeline | Downloaded datasets do not cache at $HF_HOME | https://api.github.com/repos/huggingface/datasets/issues/5546/events | null | {
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"Hi ! Can you make sure you set `HF_HOME` before importing `datasets` ?\r\n\r\nThen you can print\r\n```python\r\nprint(datasets.config.HF_CACHE_HOME)\r\nprint(datasets.config.HF_DATASETS_CACHE)\r\n```"
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1,590,315,972 | https://api.github.com/repos/huggingface/datasets/issues/5545/labels{/name} | Hi,
I was missing the ability to easily open the pushed dataset and it seemed like a quick fix.
Maybe we also want to log this info somewhere, but let me know if I need to add that too.
Cheers,
David | 2023-12-18T16:57:56Z | 5,545 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-18T11:26:25Z | https://api.github.com/repos/huggingface/datasets/issues/5545/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5545/timeline | Added return methods for URL-references to the pushed dataset | https://api.github.com/repos/huggingface/datasets/issues/5545/events | null | {
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"Hi ! Maybe we'd need to align with `transformers` and other libraries that implement `push_to_hub` to agree on what it should return.\r\n\r\ne.g. in `transformers` the typing says it returns a string, but in practice it returns a `CommitInfo`.\r\n\r\nTherefore I'd not add an output to `push_to_hub` here unless we ... | {
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1,588,951,379 | https://api.github.com/repos/huggingface/datasets/issues/5543/labels{/name} | ### Describe the bug
in #3627, the host url of the pile dataset became `https://mystic.the-eye.eu`. Now the new url is broken, but `https://the-eye.eu` seems to work again.
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("bookcorpusopen")
```
shows
```python3
ConnectionError: Couldn't reach https://mystic.the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz (ProxyError(MaxRetryError("HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_pr
eliminary_components/books1.tar.gz (Caused by ProxyError('Cannot connect to proxy.', OSError('Tunnel connection failed: 504 Gateway Timeout')))")))
```
### Expected behavior
Downloading as normal.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 6.0.1
- Pandas version: 1.5.3 | 2023-02-21T06:37:00Z | 5,543 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-17T08:40:11Z | https://api.github.com/repos/huggingface/datasets/issues/5543/comments | {
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"Thanks for reporting, @wjfwzzc.\r\n\r\nI am transferring this issue to the corresponding dataset on the Hub: https://huggingface.co/datasets/bookcorpusopen/discussions/1",
"Thank you. All fixes are done:\r\n- [x] https://huggingface.co/datasets/bookcorpusopen/discussions/2\r\n- [x] https://huggingface.co/dataset... | {
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1,588,633,724 | https://api.github.com/repos/huggingface/datasets/issues/5542/labels{/name} | Fixes https://github.com/huggingface/datasets/issues/5541 | 2023-02-17T19:20:49Z | 5,542 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-17T01:52:38Z | https://api.github.com/repos/huggingface/datasets/issues/5542/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5542/timeline | Avoid saving sparse ChunkedArrays in pyarrow tables | https://api.github.com/repos/huggingface/datasets/issues/5542/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | {
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1,588,633,555 | https://api.github.com/repos/huggingface/datasets/issues/5541/labels{/name} | ### Describe the bug
If we perform a `select` (or `shuffle`, `train_test_split`, etc.) operation on a dataset , we end up with a dataset with an `indices_table`. Currently, flattening such dataset consumes a lot of memory and the resulting flat dataset contains ChunkedArrays with as many chunks as there are rows. This is extremely inefficient and slows down the operations on the flat dataset, e.g., saving/loading the dataset to disk becomes really slow.
Perhaps more importantly, loading the dataset back from disk basically loads the whole table into RAM, as it cannot take advantage of memory mapping.
### Steps to reproduce the bug
The following script reproduces the issue:
```python
import gc
import os
import psutil
import tempfile
import time
from datasets import Dataset
DATASET_SIZE = 5000000
def profile(func):
def wrapper(*args, **kwargs):
mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
start = time.time()
# Run function here
out = func(*args, **kwargs)
end = time.time()
mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
print(f"{func.__name__} -- RAM memory used: {mem_after - mem_before} MB -- Total time: {end - start:.6f} s")
return out
return wrapper
def main():
ds = Dataset.from_list([{'col': i} for i in range(DATASET_SIZE)])
print(f"Num chunks for original ds: {ds.data['col'].num_chunks}")
with tempfile.TemporaryDirectory() as tmpdir:
path1 = os.path.join(tmpdir, 'ds1')
print("Original ds save/load")
profile(ds.save_to_disk)(path1)
ds_loaded = profile(Dataset.load_from_disk)(path1)
print(f"Num chunks for original ds after reloading: {ds_loaded.data['col'].num_chunks}")
print("")
ds_select = ds.select(reversed(range(len(ds))))
print(f"Num chunks for selected ds: {ds_select.data['col'].num_chunks}")
del ds
del ds_loaded
gc.collect()
# This would happen anyway when we call save_to_disk
ds_select = profile(ds_select.flatten_indices)()
print(f"Num chunks for selected ds after flattening: {ds_select.data['col'].num_chunks}")
print("")
path2 = os.path.join(tmpdir, 'ds2')
print("Selected ds save/load")
profile(ds_select.save_to_disk)(path2)
del ds_select
gc.collect()
ds_select_loaded = profile(Dataset.load_from_disk)(path2)
print(f"Num chunks for selected ds after reloading: {ds_select_loaded.data['col'].num_chunks}")
if __name__ == '__main__':
main()
```
Sample result:
```
Num chunks for original ds: 1
Original ds save/load
save_to_disk -- RAM memory used: 0.515625 MB -- Total time: 0.253888 s
load_from_disk -- RAM memory used: 42.765625 MB -- Total time: 0.015176 s
Num chunks for original ds after reloading: 5000
Num chunks for selected ds: 1
flatten_indices -- RAM memory used: 4852.609375 MB -- Total time: 46.116774 s
Num chunks for selected ds after flattening: 5000000
Selected ds save/load
save_to_disk -- RAM memory used: 1326.65625 MB -- Total time: 42.309825 s
load_from_disk -- RAM memory used: 2085.953125 MB -- Total time: 11.659137 s
Num chunks for selected ds after reloading: 5000000
```
### Expected behavior
Saving/loading the dataset should be much faster and consume almost no extra memory thanks to pyarrow memory mapping.
### Environment info
- `datasets` version: 2.9.1.dev0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | 2023-02-22T13:15:20Z | 5,541 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-17T01:52:24Z | https://api.github.com/repos/huggingface/datasets/issues/5541/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5541/timeline | Flattening indices in selected datasets is extremely inefficient | https://api.github.com/repos/huggingface/datasets/issues/5541/events | null | {
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} | [] | null | completed | CONTRIBUTOR | 2023-02-17T11:12:33Z | null | I_kwDODunzps5esJ_T | [
"Running the script above on the branch https://github.com/huggingface/datasets/pull/5542 results in the expected behaviour:\r\n```\r\nNum chunks for original ds: 1\r\nOriginal ds save/load\r\nsave_to_disk -- RAM memory used: 0.671875 MB -- Total time: 0.255265 s\r\nload_from_disk -- RAM memory used: 42.796875 MB -... | {
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1,588,438,344 | https://api.github.com/repos/huggingface/datasets/issues/5540/labels{/name} | A tutorial for creating datasets based on the folder-based builders and `from_dict` and `from_generator` methods. I've also mentioned loading scripts as a next step, but I think we should keep the tutorial focused on the low-code methods. Let me know what you think! 🙂 | 2023-02-17T18:50:46Z | 5,540 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-16T22:09:35Z | https://api.github.com/repos/huggingface/datasets/issues/5540/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5540/timeline | Tutorial for creating a dataset | https://api.github.com/repos/huggingface/datasets/issues/5540/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | {
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1,587,970,083 | https://api.github.com/repos/huggingface/datasets/issues/5539/labels{/name} | ### Describe the bug
When dataset contains a 0-dim tensor, formatting.py raises a following error and fails.
```bash
Traceback (most recent call last):
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 501, in format_row
return _unnest(formatted_batch)
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in _unnest
return {key: array[0] for key, array in py_dict.items()}
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in <dictcomp>
return {key: array[0] for key, array in py_dict.items()}
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
```
### Steps to reproduce the bug
Load whichever dataset and add transform method to add 0-dim tensor. Or create/find a dataset containing 0-dim tensor. E.g.
```python
from datasets import load_dataset
import torch
dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
def t(batch):
return {"test": torch.tensor(1)}
dataset.set_transform(t)
d_0 = dataset[0]
```
### Expected behavior
Extractor will correctly get a row from the dataset, even if it contains 0-dim tensor.
### Environment info
`datasets==2.8.0`, but it looks like it is also applicable to main branch version (as of 16th February) | 2023-02-22T10:30:30Z | 5,539 | null | https://api.github.com/repos/huggingface/datasets | null | [
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} | [] | null | completed | NONE | 2023-02-21T13:03:57Z | null | I_kwDODunzps5epoAj | [
"Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nimport torch\r\n\r\ndataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='t... | {
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1,587,732,596 | https://api.github.com/repos/huggingface/datasets/issues/5538/labels{/name} | TimeoutError Traceback (most recent call last)
~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
1345 try:
-> 1346 h.request(req.get_method(), req.selector, req.data, headers,
1347 encode_chunked=req.has_header('Transfer-encoding'))
~\anaconda3\lib\http\client.py in request(self, method, url, body, headers, encode_chunked)
1278 """Send a complete request to the server."""
-> 1279 self._send_request(method, url, body, headers, encode_chunked)
1280
~\anaconda3\lib\http\client.py in _send_request(self, method, url, body, headers, encode_chunked)
1324 body = _encode(body, 'body')
-> 1325 self.endheaders(body, encode_chunked=encode_chunked)
1326
~\anaconda3\lib\http\client.py in endheaders(self, message_body, encode_chunked)
1273 raise CannotSendHeader()
-> 1274 self._send_output(message_body, encode_chunked=encode_chunked)
1275
~\anaconda3\lib\http\client.py in _send_output(self, message_body, encode_chunked)
1033 del self._buffer[:]
-> 1034 self.send(msg)
1035
~\anaconda3\lib\http\client.py in send(self, data)
973 if self.auto_open:
--> 974 self.connect()
975 else:
~\anaconda3\lib\http\client.py in connect(self)
1440
-> 1441 super().connect()
1442
~\anaconda3\lib\http\client.py in connect(self)
944 """Connect to the host and port specified in __init__."""
--> 945 self.sock = self._create_connection(
946 (self.host,self.port), self.timeout, self.source_address)
~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address)
843 try:
--> 844 raise err
845 finally:
~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address)
831 sock.bind(source_address)
--> 832 sock.connect(sa)
833 # Break explicitly a reference cycle
TimeoutError: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond
During handling of the above exception, another exception occurred:
URLError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_12220/2927704185.py in <module>
1 import seaborn as sn
----> 2 iris = sn.load_dataset('iris')
~\anaconda3\lib\site-packages\seaborn\utils.py in load_dataset(name, cache, data_home, **kws)
594 if name not in get_dataset_names():
595 raise ValueError(f"'{name}' is not one of the example datasets.")
--> 596 urlretrieve(url, cache_path)
597 full_path = cache_path
598 else:
~\anaconda3\lib\urllib\request.py in urlretrieve(url, filename, reporthook, data)
237 url_type, path = _splittype(url)
238
--> 239 with contextlib.closing(urlopen(url, data)) as fp:
240 headers = fp.info()
241
~\anaconda3\lib\urllib\request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
212 else:
213 opener = _opener
--> 214 return opener.open(url, data, timeout)
215
216 def install_opener(opener):
~\anaconda3\lib\urllib\request.py in open(self, fullurl, data, timeout)
515
516 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
--> 517 response = self._open(req, data)
518
519 # post-process response
~\anaconda3\lib\urllib\request.py in _open(self, req, data)
532
533 protocol = req.type
--> 534 result = self._call_chain(self.handle_open, protocol, protocol +
535 '_open', req)
536 if result:
~\anaconda3\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args)
492 for handler in handlers:
493 func = getattr(handler, meth_name)
--> 494 result = func(*args)
495 if result is not None:
496 return result
~\anaconda3\lib\urllib\request.py in https_open(self, req)
1387
1388 def https_open(self, req):
-> 1389 return self.do_open(http.client.HTTPSConnection, req,
1390 context=self._context, check_hostname=self._check_hostname)
1391
~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
1347 encode_chunked=req.has_header('Transfer-encoding'))
1348 except OSError as err: # timeout error
-> 1349 raise URLError(err)
1350 r = h.getresponse()
1351 except:
URLError: <urlopen error [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond>
| 2023-02-16T14:44:36Z | 5,538 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-16T14:01:58Z | https://api.github.com/repos/huggingface/datasets/issues/5538/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5538/timeline | load_dataset in seaborn is not working for me. getting this error. | https://api.github.com/repos/huggingface/datasets/issues/5538/events | null | {
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"Hi! `seaborn`'s `load_dataset` pulls datasets from [here](https://github.com/mwaskom/seaborn-data) and not from our Hub, so this issue is not related to our library in any way and should be reported in their repo instead."
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1,587,567,464 | https://api.github.com/repos/huggingface/datasets/issues/5537/labels{/name} | Certain datasets like `bigcode/the-stack-dedup` have so many files that loading them takes forever right from the data files resolution step.
`datasets` uses file patterns to check the structure of the repository but it takes too much time to iterate over and over again on all the data files.
This comes from `resolve_patterns_in_dataset_repository` which calls `_resolve_single_pattern_in_dataset_repository`, which iterates on all the files at
```python
glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)]
```
but calling `glob` on such a dataset is too expensive. Indeed it calls `ls()` in `hffilesystem.py` too many times.
Maybe `glob` can be more optimized in `hffilesystem.py`, or the data files resolution can directly be implemented in the filesystem by checking its `dir_cache` ? | 2023-12-15T13:12:31Z | 5,537 | null | https://api.github.com/repos/huggingface/datasets | null | [
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... | null | completed | MEMBER | 2023-12-15T13:12:31Z | null | I_kwDODunzps5eoFto | [
"#self-assign",
"You were right, if `self.dir_cache` is not None in glob, it is exactly the same as what is returned by find, at least for all the tests we have, and some extended evaluation I did across a random sample of about 1000 datasets. \r\n\r\nThanks for the nice hints, and let me know if this is not exac... | {
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1,586,930,643 | https://api.github.com/repos/huggingface/datasets/issues/5536/labels{/name} | ### Describe the bug
_Parameter 'function'=<function process at 0x7f1ec4388af0> of the transform datasets.arrow_dataset.Dataset.\_map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed._
This issue with `.map()` happens for me consistently, as also described in closed issue #4506
Dataset indices can be individually serialized using dill and pickle without any errors. I'm using tiktoken to encode in the function passed to map(). Similarly, indices can be individually encoded without error.
### Steps to reproduce the bug
```py
from datasets import load_dataset
import tiktoken
dataset = load_dataset("stas/openwebtext-10k")
enc = tiktoken.get_encoding("gpt2")
tokenized = dataset.map(
process,
remove_columns=['text'],
desc="tokenizing the OWT splits",
)
def process(example):
ids = enc.encode(example['text'])
ids.append(enc.eot_token)
out = {'ids': ids, 'len': len(ids)}
return out
```
### Expected behavior
Should encode simple text objects.
### Environment info
Python versions tried: both 3.8 and 3.10.10
`PYTHONUTF8=1` as env variable
Datasets tried:
- stas/openwebtext-10k
- rotten_tomatoes
- local text file
OS: Ubuntu Linux 20.04
Package versions:
- torch 1.13.1
- dill 0.3.4 (if using 0.3.6 - same issue)
- datasets 2.9.0
- tiktoken 0.2.0
| 2023-09-08T21:06:01Z | 5,536 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-16T03:12:07Z | https://api.github.com/repos/huggingface/datasets/issues/5536/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5536/timeline | Failure to hash function when using .map() | https://api.github.com/repos/huggingface/datasets/issues/5536/events | null | {
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} | [] | null | completed | NONE | 2023-02-16T14:56:41Z | null | I_kwDODunzps5elqPT | [
"Hi ! `enc` is not hashable:\r\n```python\r\nimport tiktoken\r\nfrom datasets.fingerprint import Hasher\r\n\r\nenc = tiktoken.get_encoding(\"gpt2\")\r\nHasher.hash(enc)\r\n# raises TypeError: cannot pickle 'builtins.CoreBPE' object\r\n```\r\nIt happens because it's not picklable, and because of that it's not possib... | {
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1,586,520,369 | https://api.github.com/repos/huggingface/datasets/issues/5535/labels{/name} | ## What's in this PR?
As a follow-up of #5522, I've created this entry in the documentation to explain how to use `.with_format("jax")` and why is it useful.
@lhoestq Feel free to drop any feedback and/or suggestion, as probably more useful features can be included there! | 2023-02-20T10:39:42Z | 5,535 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-15T20:35:11Z | https://api.github.com/repos/huggingface/datasets/issues/5535/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5535/timeline | Add JAX-formatting documentation | https://api.github.com/repos/huggingface/datasets/issues/5535/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Awesome thank you !\r\n> \r\n> Could you also explain how to use certain types like ClassLabel, Image or Audio with jax ? You can get a lot of inspiration from the \"Other feature types\" section in the [PyTorch page](https://huggi... | {
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1,586,177,862 | https://api.github.com/repos/huggingface/datasets/issues/5534/labels{/name} | ### Describe the bug
`map()` magically breaks when using a `Array3D` feature and mapping it. I created a very simple dummy dataset (see below). When filtering it down to 95 elements I can apply map, but it breaks when filtering it down to just 96 entries with the following exception:
```
Traceback (most recent call last):
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3255, in _map_single
writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 581, in finalize
self.write_examples_on_file()
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 440, in write_examples_on_file
batch_examples[col] = array_concat(arrays)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1931, in array_concat
return _concat_arrays(arrays)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1901, in _concat_arrays
return array_type.wrap_array(_concat_arrays([array.storage for array in arrays]))
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays
_concat_arrays([array.values for array in arrays]),
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays
_concat_arrays([array.values for array in arrays]),
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1920, in _concat_arrays
return pa.ListArray.from_arrays(
File "pyarrow/array.pxi", line 1997, in pyarrow.lib.ListArray.from_arrays
File "pyarrow/array.pxi", line 1527, in pyarrow.lib.Array.validate
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Negative offsets in list array
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2815, in map
return self._map_single(
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 546, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 513, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/fingerprint.py", line 480, in wrapper
out = func(self, *args, **kwargs)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3259, in _map_single
writer.finalize()
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 581, in finalize
self.write_examples_on_file()
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 440, in write_examples_on_file
batch_examples[col] = array_concat(arrays)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1931, in array_concat
return _concat_arrays(arrays)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1901, in _concat_arrays
return array_type.wrap_array(_concat_arrays([array.storage for array in arrays]))
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays
_concat_arrays([array.values for array in arrays]),
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays
_concat_arrays([array.values for array in arrays]),
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1920, in _concat_arrays
return pa.ListArray.from_arrays(
File "pyarrow/array.pxi", line 1997, in pyarrow.lib.ListArray.from_arrays
File "pyarrow/array.pxi", line 1527, in pyarrow.lib.Array.validate
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Negative offsets in list array
```
### Steps to reproduce the bug
1. put following dataset loading script into: debug/debug.py
```python
import datasets
import numpy as np
class DEBUG(datasets.GeneratorBasedBuilder):
"""DEBUG dataset."""
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("uint8"),
"img_data": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
},
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN)]
def _generate_examples(self):
for i in range(149):
image_np = np.zeros(shape=(3, 224, 224), dtype=np.int8).tolist()
yield f"id_{i}", {"id": i, "img_data": image_np}
```
2. try the following code:
```python
import datasets
def add_dummy_col(ex):
ex["dummy"] = "test"
return ex
ds = datasets.load_dataset(path="debug", split="train")
# works
ds_filtered_works = ds.filter(lambda example: example["id"] < 95)
print(f"filtered result size: {len(ds_filtered_works)}")
# output:
# filtered result size: 95
ds_mapped_works = ds_filtered_works.map(add_dummy_col)
# fails
ds_filtered_error = ds.filter(lambda example: example["id"] < 96)
print(f"filtered result size: {len(ds_filtered_error)}")
# output:
# filtered result size: 96
ds_mapped_error = ds_filtered_error.map(add_dummy_col)
```
### Expected behavior
The example code does not fail.
### Environment info
Python 3.9.16 (main, Jan 11 2023, 16:05:54); [GCC 11.2.0] :: Anaconda, Inc. on linux
datasets 2.9.0 | 2023-03-03T16:31:33Z | 5,534 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-15T16:34:25Z | https://api.github.com/repos/huggingface/datasets/issues/5534/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5534/timeline | map() breaks at certain dataset size when using Array3D | https://api.github.com/repos/huggingface/datasets/issues/5534/events | null | {
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"Hi! This code works for me locally or in Colab. What's the output of `python -c \"import pyarrow as pa; print(pa.__version__)\"` when you run it inside your environment?",
"Thanks for looking into this!\r\nThe output of `python -c \"import pyarrow as pa; print(pa.__version__)\"` is:\r\n```\r\n11.0.0\r\n```\r\n\... | {
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1,585,885,871 | https://api.github.com/repos/huggingface/datasets/issues/5533/labels{/name} | This PR closes #5496 .
I tried to imitate the `reduce`-method from `functools`, i.e. the function input must be a binary operation. I assume that the input type has an empty element, i.e. `input_type()` is defined, as the acumulant is instantiated as this object - im not sure that is this a reasonable assumption?
If `batched= True` the reduction of each shard is _not_ returned, but the reduction of the entire dataset. I was unsure wether this was an intuitive API, or it would make more sense to return the reduction of each shard? | 2023-02-28T14:46:13Z | 5,533 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-15T13:44:01Z | https://api.github.com/repos/huggingface/datasets/issues/5533/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5533/timeline | Add reduce function | https://api.github.com/repos/huggingface/datasets/issues/5533/events | null | {
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"I agree that it would be a good idea to introduce a `combiner` argument in another PR.\r\n\r\nI did take quite a lot of inspiration from the implementation of `map`, but it did not seem obvious how to resuse `map` for the implementation. Do you have any suggestions, i could give a try?\r\n\r\nThose were exactly m... | {
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1,584,505,128 | https://api.github.com/repos/huggingface/datasets/issues/5532/labels{/name} | ### Describe the bug
When I have a dataset with very few (e.g. 1) examples per class and I call the train_test_split function on it, sometimes the single class will be in the test set. thus will never be considered for training.
### Steps to reproduce the bug
```
import numpy as np
from datasets import Dataset
data = [
{'label': 0, 'text': "example1"},
{'label': 1, 'text': "example2"},
{'label': 1, 'text': "example3"},
{'label': 1, 'text': "example4"},
{'label': 0, 'text': "example5"},
{'label': 1, 'text': "example6"},
{'label': 2, 'text': "example7"},
{'label': 2, 'text': "example8"}
]
for _ in range(10):
data_set = Dataset.from_list(data)
data_set = data_set.train_test_split(test_size=0.5)
data_set["train"]
unique_labels_train = np.unique(data_set["train"][:]["label"])
unique_labels_test = np.unique(data_set["test"][:]["label"])
assert len(unique_labels_train) >= len(unique_labels_test)
```
### Expected behavior
I expect to have every available class at least once in my training set.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 11.0.0
- Pandas version: 1.3.5
| 2023-02-15T16:09:19Z | 5,532 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-14T16:52:29Z | https://api.github.com/repos/huggingface/datasets/issues/5532/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5532/timeline | train_test_split in arrow_dataset does not ensure to keep single classes in test set | https://api.github.com/repos/huggingface/datasets/issues/5532/events | null | {
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"Hi! You can get this behavior by specifying `stratify_by_column=\"label\"` in `train_test_split`.\r\n\r\nThis is the full example:\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset, ClassLabel\r\n\r\ndata = [\r\n {'label': 0, 'text': \"example1\"},\r\n {'label': 1, 'text': \"example2\"},\r\n... | {
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1,584,387,276 | https://api.github.com/repos/huggingface/datasets/issues/5531/labels{/name} | This code fails:
```python
from datasets import Dataset
ds = Dataset.from_json(path_to_file)
ds.data.validate()
```
raises
```python
ArrowInvalid: Column 2: In chunk 1: Invalid: Struct child array #3 invalid: Invalid: Length spanned by list offsets (4064) larger than values array (length 4063)
```
This causes many issues for @TevenLeScao:
- `map` fails because it fails to rewrite invalid arrow arrays
```python
~/Desktop/hf/datasets/src/datasets/arrow_writer.py in write_examples_on_file(self)
438 if all(isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) for row in self.current_examples):
439 arrays = [row[0][col] for row in self.current_examples]
--> 440 batch_examples[col] = array_concat(arrays)
441 else:
442 batch_examples[col] = [
~/Desktop/hf/datasets/src/datasets/table.py in array_concat(arrays)
1885
1886 if not _is_extension_type(array_type):
-> 1887 return pa.concat_arrays(arrays)
1888
1889 def _offsets_concat(offsets):
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.concat_arrays()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowIndexError: array slice would exceed array length
```
- `to_dict()` **segfaults** ⚠️
```python
/Users/runner/work/crossbow/crossbow/arrow/cpp/src/arrow/array/data.cc:99: Check failed: (off) <= (length) Slice offset greater
than array length
```
To reproduce: unzip the archive and run the above code using `sanity_oscar_en.jsonl`
[sanity_oscar_en.jsonl.zip](https://github.com/huggingface/datasets/files/10734124/sanity_oscar_en.jsonl.zip)
PS: reading using pandas and converting to Arrow works though (note that the dataset lives in RAM in this case):
```python
ds = Dataset.from_pandas(pd.read_json(path_to_file, lines=True))
ds.data.validate()
```
| 2023-02-14T15:46:09Z | 5,531 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,582,938,241 | https://api.github.com/repos/huggingface/datasets/issues/5530/labels{/name} | ## What's in this PR?
As discussed with @lhoestq in https://github.com/huggingface/datasets/pull/5522, the license for `NumpyFormatter` at `datasets/formatting/np_formatter.py` was missing, but present on the rest of the `formatting/*.py` files. So this PR is basically to include it there. | 2023-02-14T14:40:41Z | 5,530 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-13T19:33:23Z | https://api.github.com/repos/huggingface/datasets/issues/5530/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5530/timeline | Add missing license in `NumpyFormatter` | https://api.github.com/repos/huggingface/datasets/issues/5530/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | {
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1,582,501,233 | https://api.github.com/repos/huggingface/datasets/issues/5529/labels{/name} | ## What's in this PR?
After playing around a little bit with 🤗`datasets` in Google Cloud Storage (GCS), I found out some things that should be fixed IMO in the code:
* `datasets.load_from_disk` is not checking whether `state.json` is there too when trying to load a `Dataset`, just `dataset_info.json` is checked
* `DatasetDict.load_from_disk` is not checking whether `state.json` is there too when redirecting the user to load it as `datasets.load_from_disk`, just `dataset_info.json` is checked, which is misleading, as it won't be loadable that way either
* `Dataset.load_from_disk` is missing the `extract_path_from_uri` call before checking in the `fs` whether `dataset_info.json` and `dataset_dict.json` exist, which when using `gcsfs` leads to 400 error code (not blocking) due to `gcsfs.retry.HttpError: Invalid bucket name: 'gs:', 400`
* And, finally, the exception messages are a little bit misleading / incomplete IMO so I've tried to include all the relevant information in the messages to avoid issues when interpreting the exceptions | 2023-02-23T18:14:32Z | 5,529 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-13T14:54:55Z | https://api.github.com/repos/huggingface/datasets/issues/5529/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5529/timeline | Fix `datasets.load_from_disk`, `DatasetDict.load_from_disk` and `Dataset.load_from_disk` | https://api.github.com/repos/huggingface/datasets/issues/5529/events | null | {
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1,582,195,085 | https://api.github.com/repos/huggingface/datasets/issues/5528/labels{/name} | Fixes #5492.
Introduce new kwarg `create_pr` in `push_to_hub`, which is passed to `HFapi.upload_file`. | 2023-10-06T21:58:02Z | 5,528 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-13T11:43:47Z | https://api.github.com/repos/huggingface/datasets/issues/5528/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5528/timeline | Push to hub in a pull request | https://api.github.com/repos/huggingface/datasets/issues/5528/events | null | {
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1,581,228,531 | https://api.github.com/repos/huggingface/datasets/issues/5527/labels{/name} | fix https://github.com/huggingface/datasets/actions/runs/4156059127/jobs/7189576331 | 2023-02-13T10:29:03Z | 5,527 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-12T11:51:25Z | https://api.github.com/repos/huggingface/datasets/issues/5527/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5527/timeline | Fix benchmarks CI - pin protobuf | https://api.github.com/repos/huggingface/datasets/issues/5527/events | null | {
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1,580,488,133 | https://api.github.com/repos/huggingface/datasets/issues/5526/labels{/name} | Fixes #5428
Allow loading/saving of FAISS index using fsspec:
1. Simply use BufferedIOWriter/Reader to Read/Write indices on fsspec stream.
2. Needed `mockfs` in the test, so I took it out of the `TestCase`. Let me know if that makes sense.
I can work on the documentation once the code changes are approved.
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the quick review! I updated the code with your suggestion",
"Thanks for the quick review @albertvillanova! I updated the code with your suggestions",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n... | {
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1,580,342,729 | https://api.github.com/repos/huggingface/datasets/issues/5525/labels{/name} | ### Describe the bug
Processing a dataset I alredy uploaded to the Hub (https://huggingface.co/datasets/tj-solergibert/Europarl-ST) I found that for some splits and some languages (test split, source_lang = "nl") after applying a map function I get the mentioned error.
I alredy tried reseting the shorter strings (reset_cortas function). It only happends with NL, PL, RO and PT. It does not make sense since when processing the other languages I also use the corpus of those that fail and it does not cause any errors.
I suspect that the error may be in this direction:
We use cast_array_to_feature to support casting to custom types like Audio and Image # Also, when trying type "string", we don't want to convert integers or floats to "string". # We only do it if trying_type is False - since this is what the user asks for.
### Steps to reproduce the bug
Here I link a colab notebook to reproduce the error:
https://colab.research.google.com/drive/1JCrS7FlGfu_kFqChMrwKZ_bpabnIMqbP?authuser=1#scrollTo=FBAvlhMxIzpA
### Expected behavior
Data processing does not fail. A correct example can be seen here: https://huggingface.co/datasets/tj-solergibert/Europarl-ST-processed-mt-en
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5 | 2023-02-14T17:41:08Z | 5,525 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-10T21:12:36Z | https://api.github.com/repos/huggingface/datasets/issues/5525/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5525/timeline | TypeError: Couldn't cast array of type string to null | https://api.github.com/repos/huggingface/datasets/issues/5525/events | null | {
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"Thanks for reporting, @TJ-Solergibert.\r\n\r\nWe cannot access your Colab notebook: `There was an error loading this notebook. Ensure that the file is accessible and try again.`\r\nCould you please make it publicly accessible?\r\n",
"I swear it's public, I've checked the settings and I've been able to open it in... | {
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1,580,219,454 | https://api.github.com/repos/huggingface/datasets/issues/5524/labels{/name} | Hi to whoever is reading this! 🤗
## What's in this PR?
~~Basically, I've removed the 🤗`datasets` installation as `python -m pip install ".[quality]" in the `check_code_quality` job in `.github/workflows/ci.yaml`, as we don't need to install the whole package to run the CI, unless that's done on purpose e.g. to check that the Python package installation succeeds before running the tests over the matrix of os?~~
~~So I just wanted to check whether the time was reduced doing this (which I assume it will), plus whether this is something that can be improved, or just discarded in case you're also using that step to make sure that the package can be installed.~~
## What's missing?
~~I was just wondering whether you consider replacing `isort` and `flake8` with `ruff` (if possible), since it's way faster, more information at [`ruff`](https://github.com/charliermarsh/ruff). Before creating this PR the average time of the `check_code_quality` job was around 40s.~~
## Edit
Sorry for the inconvenience this may have caused, didn't realise that the config is defined in `setup.cfg` and `pyproject.toml`, so running those without installing the Python package leads to failure, my bad 😞
| 2023-02-10T19:51:45Z | 5,524 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-10T19:35:50Z | https://api.github.com/repos/huggingface/datasets/issues/5524/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5524/timeline | [INVALID PR] | https://api.github.com/repos/huggingface/datasets/issues/5524/events | null | {
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1,580,193,015 | https://api.github.com/repos/huggingface/datasets/issues/5523/labels{/name} | ### Describe the bug
Verification of split names (=indexing data by split) happens after downloading the data. So when the split name is incorrect, users learn about that only after the data is fully downloaded, for large datasets it might take a lot of time.
### Steps to reproduce the bug
Load any dataset with random split name, for example:
```python
from datasets import load_dataset
load_dataset("mozilla-foundation/common_voice_11_0", "en", split="blabla")
```
and the download will start smoothly, despite there is no split named "blabla".
### Expected behavior
Raise error when split name is incorrect.
### Environment info
`datasets==2.9.1.dev0` | 2023-02-10T19:14:50Z | 5,523 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,580,183,124 | https://api.github.com/repos/huggingface/datasets/issues/5522/labels{/name} | Hi to whoever is reading this! 🤗
## What's in this PR?
I was exploring the code regarding the `JaxFormatter` implemented in 🤗`datasets`, and found some things that IMO could be changed. Those are mainly regarding the docstrings and the type-hints based on `jax`'s 0.4.1 release where `jax.Array` was introduced as the default type for JAX-arrays (instead of `jnp.DeviceArray`, `jnp.SharedDeviceArray`, and `jnp.GlobalDeviceArray`). Even though `isinstance(..., jax.Array)` also works with lower versions such as e.g. `0.3.25`.
More information about the latter at [`jax` v0.4.1 - Release Notes](https://github.com/google/jax/releases/tag/jax-v0.4.1) and [jax.Array migration - JAX documentation](https://jax.readthedocs.io/en/latest/jax_array_migration.html).
## What's missing?
* Do you want me to write an entry in the documentation on how to use 🤗`datasets` with JAX as https://huggingface.co/docs/datasets/use_with_pytorch with PyTorch?
* Do we need to actually include `pyarrow` under the `TYPE_CHECKING` when needed? I just did it for JAX, but if we are OK with that, I can do that with the rest of the formatters, just LMK.
* Should the License header be included in `datasets.formatting.np_formatter`? If so, do I include the one from 2020 e.g. https://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/tf_formatter.py#L1-L13
* Is there any reason why `jnp.array` is being used instead of `jnp.asarray`? There's no difference between both, just that `jnp.asarray` has `copy=False` as default, even though `numpy` to `jax.numpy` conversion is not zero-copy, but just asking :) | 2023-02-15T14:48:27Z | 5,522 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-10T19:05:00Z | https://api.github.com/repos/huggingface/datasets/issues/5522/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5522/timeline | Minor changes in JAX-formatting docstrings & type-hints | https://api.github.com/repos/huggingface/datasets/issues/5522/events | null | {
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1,578,418,289 | https://api.github.com/repos/huggingface/datasets/issues/5521/labels{/name} | Fix https://github.com/huggingface/datasets/issues/5520. | 2023-02-13T20:40:48Z | 5,521 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-09T18:47:59Z | https://api.github.com/repos/huggingface/datasets/issues/5521/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5521/timeline | Fix bug when casting empty array to class labels | https://api.github.com/repos/huggingface/datasets/issues/5521/events | null | {
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1,578,417,074 | https://api.github.com/repos/huggingface/datasets/issues/5520/labels{/name} | ### Describe the bug
`ClassLabel.cast_storage` raises `TypeError` when called on an empty `IntegerArray`.
### Steps to reproduce the bug
Minimal steps:
```python
import pyarrow as pa
from datasets import ClassLabel
ClassLabel(names=['foo', 'bar']).cast_storage(pa.array([], pa.int64()))
```
In practice, this bug arises in situations like the one below:
```python
from datasets import ClassLabel, Dataset, Features, Sequence
dataset = Dataset.from_dict({'labels': [[], []]}, features=Features({'labels': Sequence(ClassLabel(names=['foo', 'bar']))}))
# this raises TypeError
dataset.map(batched=True, batch_size=1)
```
### Expected behavior
`ClassLabel.cast_storage` should return an empty Int64Array.
### Environment info
- `datasets` version: 2.9.1.dev0
- Platform: Linux-4.15.0-1032-aws-x86_64-with-glibc2.27
- Python version: 3.10.6
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | 2023-02-12T11:17:18Z | 5,520 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-09T18:46:52Z | https://api.github.com/repos/huggingface/datasets/issues/5520/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5520/timeline | ClassLabel.cast_storage raises TypeError when called on an empty IntegerArray | https://api.github.com/repos/huggingface/datasets/issues/5520/events | null | {
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1,578,341,785 | https://api.github.com/repos/huggingface/datasets/issues/5519/labels{/name} | Use `ruff` for formatting instead of `isort` and `black` to be consistent with [`transformers`](https://github.com/huggingface/transformers/pull/21480) and [`hfh`](https://github.com/huggingface/huggingface_hub/pull/1323).
TODO:
- [x] ~Merge the community contributors' PR to avoid having to run `make style` on their PR branches~ (we have some new PRs, but fixing those shouldn't be too big of a problem) | 2023-02-14T16:28:27Z | 5,519 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-09T17:50:21Z | https://api.github.com/repos/huggingface/datasets/issues/5519/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5519/timeline | Format code with `ruff` | https://api.github.com/repos/huggingface/datasets/issues/5519/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | {
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1,578,203,962 | https://api.github.com/repos/huggingface/datasets/issues/5518/labels{/name} | Fix https://github.com/huggingface/datasets/issues/3841 | 2023-02-13T13:55:49Z | 5,518 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-09T16:22:29Z | https://api.github.com/repos/huggingface/datasets/issues/5518/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5518/timeline | Remove py.typed | https://api.github.com/repos/huggingface/datasets/issues/5518/events | null | {
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1,577,976,608 | https://api.github.com/repos/huggingface/datasets/issues/5517/labels{/name} | ### Describe the bug
When I create a dataset with a `float64` feature, then apply numpy formatting the returned numpy arrays are silently downcasted to `float32`.
### Steps to reproduce the bug
```python
import datasets
dataset = datasets.Dataset.from_dict({'a': [1.0, 2.0, 3.0]}).with_format("numpy")
print("feature dtype:", dataset.features['a'].dtype)
print("array dtype:", dataset['a'].dtype)
```
output:
```
feature dtype: float64
array dtype: float32
```
### Expected behavior
```
feature dtype: float64
array dtype: float64
```
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-135-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 10.0.1
- Pandas version: 1.4.4
### Suggested Fix
Changing [the `_tensorize` function of the numpy formatter](https://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/np_formatter.py#L32) to
```python
def _tensorize(self, value):
if isinstance(value, (str, bytes, type(None))):
return value
elif isinstance(value, (np.character, np.ndarray)) and np.issubdtype(value.dtype, np.character):
return value
elif isinstance(value, np.number):
return value
return np.asarray(value, **self.np_array_kwargs)
```
fixes this particular issue for me. Not sure if this would break other tests. This should also avoid unnecessary copying of the array.
| 2024-01-18T08:42:17Z | 5,517 | {
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"Hi! This behavior stems from these lines:\r\n\r\nhttps://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/np_formatter.py#L45-L46\r\n\r\nI agree we should preserve the original type whenever possible and downcast explicitly with a warning.\r\n\r\n@lhoestq Do you... | {
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1,577,661,640 | https://api.github.com/repos/huggingface/datasets/issues/5516/labels{/name} | Resolves #5482.
Attaches feature metadata to parquet files serialised using `Dataset.to_parquet`.
This allows retrieving data with "rich" feature types (e.g., `datasets.features.image.Image` or `datasets.features.audio.Audio`) from parquet files without cumbersome casting (for an example, see #5482).
@lhoestq It seems that it is sufficient to attach metadata to the schema prior to serialising and features are loaded back with correct types afterwards automatically.
I used the following script to test the implementation:
```python
from pathlib import Path
import datasets
dataset_name = "Maysee/tiny-imagenet"
ds = datasets.load_dataset(dataset_name, split=datasets.Split.TRAIN)
output_directory_path = Path(__file__).parent.joinpath("example_test_outputs", dataset_name.replace("/", "_"))
output_directory_path.mkdir(exist_ok=True, parents=True)
output_filepath = output_directory_path.joinpath("ds.parquet")
ds.to_parquet(str(output_filepath))
reloaded_ds = datasets.load_dataset(str(output_directory_path), split=datasets.Split.TRAIN)
assert ds.features == reloaded_ds.features
```
Prior to the change in this PR this script raises an `AssertionError` and the `Image` features lose their type after serialisation. After the change in this PR, the assertion does not raise an error and manual inspection of the features shows type `Image` for the respective columns of `reloaded_ds `.
Some open questions:
* How/where can I best add new unit tests for this implementation?
* What dataset would I best use in the tests? I chose `Maysee/tiny-imagenet` mainly because it is small and contains an ?Image` feature that can be used to test, but I'd be happy for suggestions on a suitable data source to use.
* Currently I'm calling `datasets.arrow_writer.ArrowWriter._build_metadata` as I need the same logic. However, I'm not happy with the coupling between `datasets.io.parquet` and `datasets.arrow_writer` it leaves me with. Suggest to factor this common logic out into a helper function and reuse it from both of these. Do you agree and if yes, could you please guide me where I would best place this function?
Many thanks in advance and kind regards,
MFreidank
| 2023-02-12T16:00:00Z | 5,516 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-09T10:52:15Z | https://api.github.com/repos/huggingface/datasets/issues/5516/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5516/timeline | Reload features from Parquet metadata | https://api.github.com/repos/huggingface/datasets/issues/5516/events | null | {
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"Thanks a lot for your help @lhoestq. I've simplified what turned out to be a simple fix and added the unit test.\r\n\r\nDoes this look ready to be merged or is there anything I'm still missing?",
"Cool ! I think you just need to remove the unused import in `io/parquet.py`\r\n```\r\nsrc/datasets/io/parquet.py:4:1... | {
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1,577,590,611 | https://api.github.com/repos/huggingface/datasets/issues/5515/labels{/name} | * Updating type annotations for #`load_from_cache_file`
* Added logic for cache checking if needed
* Updated documentation following the wording of `Dataset.map` | 2023-02-14T15:38:13Z | 5,515 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-09T10:04:46Z | https://api.github.com/repos/huggingface/datasets/issues/5515/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5515/timeline | Unify `load_from_cache_file` type and logic | https://api.github.com/repos/huggingface/datasets/issues/5515/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The commit also includes the changes to the `DatasetDict` methods or am I missing something?",
"Oh, indeed. Feel free to mark the PR as \"Ready for review\" then.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0... | {
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1,576,453,837 | https://api.github.com/repos/huggingface/datasets/issues/5514/labels{/name} | ### Feature request
1. Replace the `load_from_cache_file` default value to `True`.
2. Remove or alter checks from `is_caching_enabled` logic.
### Motivation
I stumbled over an inconsistency in the `Dataset.map` interface. The documentation (and source) states for the parameter `load_from_cache_file`:
```
load_from_cache_file (`bool`, defaults to `True` if caching is enabled):
If a cache file storing the current computation from `function`
can be identified, use it instead of recomputing.
```
1. `load_from_cache_file` default value is `None`, while being annotated as `bool`
2. It is inconsistent with other method signatures like `filter`, that have the default value `True`
3. The logic is inconsistent, as the `map` method checks if caching is enabled through `is_caching_enabled`. This logic is not used for other similar methods.
### Your contribution
I am not fully aware of the logic behind caching checks. If this is just a inconsistency that historically grew, I would suggest to remove the `is_caching_enabled` logic as the "default" logic. Maybe someone can give insights, if environment variables have a higher priority than local variables or vice versa.
If this is clarified, I could adjust the source according to the "Feature request" section of this issue. | 2023-02-14T14:26:44Z | 5,514 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi, thanks for noticing this! We can't just remove the cache control as this allows us to control where the arrow files generated by the ops are written (cached on disk if enabled or a temporary directory if disabled). The right way to address this inconsistency would be by having `load_from_cache_file=None` by de... | {
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1,576,300,803 | https://api.github.com/repos/huggingface/datasets/issues/5513/labels{/name} | Hi @mariosasko, @lhoestq, or whoever reads this! :)
After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, shouldn't that be renamed to `format_type` before the 3.0.0 is released?
Just wanted to get your input, and if applicable, tackle this issue myself! Thanks 🤗 | 2023-07-24T16:02:18Z | 5,513 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-08T15:13:46Z | https://api.github.com/repos/huggingface/datasets/issues/5513/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5513/timeline | Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name? | https://api.github.com/repos/huggingface/datasets/issues/5513/events | null | {
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"Hi! Let's not do this - renaming it would be a breaking change, and going through the deprecation cycle is only worth it if it improves user experience.",
"Hi @mariosasko, ok it makes sense. Anyway, don't you think it's worth it at some point to start a deprecation cycle e.g. `fs` in `load_from_disk`? It doesn't... | {
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1,576,142,432 | https://api.github.com/repos/huggingface/datasets/issues/5512/labels{/name} | I implemented `__getitems__` to speed up batched data loading in PyTorch
close https://github.com/huggingface/datasets/issues/5505 | 2023-02-19T18:35:09Z | 5,512 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-08T13:38:59Z | https://api.github.com/repos/huggingface/datasets/issues/5512/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5512/timeline | Speed up batched PyTorch DataLoader | https://api.github.com/repos/huggingface/datasets/issues/5512/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | {
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1,575,851,768 | https://api.github.com/repos/huggingface/datasets/issues/5511/labels{/name} | ### Describe the bug
I often want to create a dummy dataset from a bigger dataset for fast iteration when training. However, I'm having a hard time doing this especially when trying to upload the dataset to the Hub.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("lambdalabs/pokemon-blip-captions")
dataset["train"] = dataset["train"].select(range(20))
dataset.push_to_hub("patrickvonplaten/dummy_image_data")
```
gives:
```
~/python_bin/datasets/arrow_dataset.py in _push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files)
4003 base_wait_time=2.0,
4004 max_retries=5,
-> 4005 max_wait_time=20.0,
4006 )
4007 return repo_id, split, uploaded_size, dataset_nbytes
~/python_bin/datasets/utils/file_utils.py in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
328 while True:
329 try:
--> 330 return func(*func_args, **func_kwargs)
331 except exceptions as err:
332 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
~/hf/lib/python3.7/site-packages/huggingface_hub/utils/_validators.py in _inner_fn(*args, **kwargs)
122 )
123
--> 124 return fn(*args, **kwargs)
125
126 return _inner_fn # type: ignore
TypeError: upload_file() got an unexpected keyword argument 'identical_ok'
In [2]:
```
### Expected behavior
I would have expected this to work. It's for me the most intuitive way of creating a dummy dataset.
### Environment info
```
- `datasets` version: 2.1.1.dev0
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.3
- PyArrow version: 11.0.0
- Pandas version: 1.3.5
``` | 2023-12-28T18:21:01Z | 5,511 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-08T10:18:41Z | https://api.github.com/repos/huggingface/datasets/issues/5511/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5511/timeline | Creating a dummy dataset from a bigger one | https://api.github.com/repos/huggingface/datasets/issues/5511/events | null | {
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"Update `datasets` or downgrade `huggingface-hub` ;)\r\n\r\nThe `huggingface-hub` lib did a breaking change a few months ago, and you're using an old version of `datasets` that does't support it",
"Awesome thanks a lot! Everything works just fine with `datasets==2.9.0` :-) ",
"Getting same error with latest ver... | {
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1,575,191,549 | https://api.github.com/repos/huggingface/datasets/issues/5510/labels{/name} | Signed-off-by: Filip Haltmayer <filip.haltmayer@zilliz.com> | 2023-02-24T16:45:09Z | 5,510 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-07T23:30:26Z | https://api.github.com/repos/huggingface/datasets/issues/5510/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5510/timeline | Milvus integration for search | https://api.github.com/repos/huggingface/datasets/issues/5510/events | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5510). All of your documentation changes will be reflected on that endpoint.",
"To the maintainer, sorry about the repeated run requests for formatting. Missed the `make style` outlined in contributing guidelines. ",
"Anythin... | {
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1,574,177,320 | https://api.github.com/repos/huggingface/datasets/issues/5509/labels{/name} | This adds a static `__all__` field to `__init__.py`, allowing typecheckers to know which symbols are accessible from `datasets` at runtime. In particular [Pyright](https://github.com/microsoft/pylance-release/issues/2328#issuecomment-1029381258) seems to rely on this. At this point I have added all (modulo oversight) the symbols mentioned in the Reference part of [the docs](https://huggingface.co/docs/datasets), but that could be adjusted. As a side effect, only these symbols will be imported by `from datasets import *`, which may or may not be a good thing (and if it isn't, that's easy to fix).
Another option would be to add a pyi stub, but I think `__all__` should be the most pythonic solution.
This should fix #3841. | 2023-02-08T17:48:24Z | 5,509 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-07T11:42:40Z | https://api.github.com/repos/huggingface/datasets/issues/5509/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5509/timeline | Add a static `__all__` to `__init__.py` for typecheckers | https://api.github.com/repos/huggingface/datasets/issues/5509/events | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5509). All of your documentation changes will be reflected on that endpoint.",
"Hi! I've commented on the original issue to provide some context. Feel free to share your opinion there."
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1,573,290,359 | https://api.github.com/repos/huggingface/datasets/issues/5508/labels{/name} | ### Describe the bug
Saving a dataset after setting format to torch doesn't work, but only if filtering
### Steps to reproduce the bug
```
a = Dataset.from_dict({"b": [1, 2]})
a.set_format('torch')
a.save_to_disk("test_save") # saves successfully
a.filter(None).save_to_disk("test_save_filter") # does not
>> [...] TypeError: Provided `function` which is applied to all elements of table returns a `dict` of types [<class 'torch.Tensor'>]. When using `batched=True`, make sure provided `function` returns a `dict` of types like `(<class 'list'>, <class 'numpy.ndarray'>)`.
# note: skipping the format change to torch lets this work.
### Expected behavior
Saving to work
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-6.1.9-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.9
- PyArrow version: 9.0.0
- Pandas version: 1.4.4 | 2023-02-09T14:55:26Z | 5,508 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-06T21:08:58Z | https://api.github.com/repos/huggingface/datasets/issues/5508/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5508/timeline | Saving a dataset after setting format to torch doesn't work, but only if filtering | https://api.github.com/repos/huggingface/datasets/issues/5508/events | null | {
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"Hey, I'm a research engineer working on language modelling wanting to contribute to open source. I was wondering if I could give it a shot?",
"Hi! This issue was fixed in https://github.com/huggingface/datasets/pull/4972, so please install `datasets>=2.5.0` to avoid it."
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1,572,667,036 | https://api.github.com/repos/huggingface/datasets/issues/5507/labels{/name} | _Originally [posted](https://huggingface.slack.com/archives/C02V51Q3800/p1675443873878489?thread_ts=1675418893.373479&cid=C02V51Q3800) on Slack_
Considering all this, perhaps for Datasets 3.0, we can do the following:
* [ ] have `continuous=True` by default in `.shard` (requested in the survey and makes more sense for us since it doesn't create an indices mapping)
* [x] allow calling `save_to_disk` on "unflattened" datasets
* [ ] remove "hidden" expensive calls in `save_to_disk`, `unique`, `concatenate_datasets`, etc. For instance, instead of silently calling `flatten_indices` where it's needed, it's probably better to be explicit (considering how expensive these ops can be) and raise an error instead | 2023-02-28T18:19:18Z | 5,507 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,571,838,641 | https://api.github.com/repos/huggingface/datasets/issues/5506/labels{/name} | ### Describe the bug
I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256.
Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](https://github.com/huggingface/datasets/issues/2252), I swapped to loading my dataset as contiguous shards and passing those to an `IterableDataset`. I observed an unexpected drop in GPU memory utilization, and found the batch size returned from the model had been cut in half.
When using `Trainer` with 2 GPUs and a batch size of 256, `Dataset` returns a batch of size 512 (256 per GPU), while `IterableDataset` returns a batch size of 256 (256 total). My guess is `IterableDataset` isn't accounting for multiple cards.
### Steps to reproduce the bug
```python
import datasets
from datasets import IterableDataset
from transformers import RobertaConfig
from transformers import RobertaTokenizerFast
from transformers import RobertaForMaskedLM
from transformers import DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments
use_iterable_dataset = True
def gen_from_shards(shards):
for shard in shards:
for example in shard:
yield example
dataset = datasets.load_from_disk('my_dataset.hf')
if use_iterable_dataset:
n_shards = 100
shards = [dataset.shard(num_shards=n_shards, index=i) for i in range(n_shards)]
dataset = IterableDataset.from_generator(gen_from_shards, gen_kwargs={"shards": shards})
tokenizer = RobertaTokenizerFast.from_pretrained("./my_tokenizer", max_len=160, use_fast=True)
config = RobertaConfig(
vocab_size=8248,
max_position_embeddings=256,
num_attention_heads=8,
num_hidden_layers=6,
type_vocab_size=1)
model = RobertaForMaskedLM(config=config)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15)
training_args = TrainingArguments(
per_device_train_batch_size=256
# other args removed for brevity
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
)
trainer.train()
```
### Expected behavior
Expected `Dataset` and `IterableDataset` to have the same batch size behavior. If the current behavior is intentional, the batch size printout at the start of training should be updated. Currently, both dataset classes result in `Trainer` printing the same total batch size, even though the batch size sent to the GPUs are different.
### Environment info
datasets 2.7.1
transformers 4.25.1 | 2023-02-08T18:30:08Z | 5,506 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-06T03:26:03Z | https://api.github.com/repos/huggingface/datasets/issues/5506/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5506/timeline | IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs | https://api.github.com/repos/huggingface/datasets/issues/5506/events | null | {
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} | [] | null | completed | NONE | 2023-02-08T18:30:07Z | null | I_kwDODunzps5dsFqx | [
"Hi ! `datasets` doesn't do batching - the PyTorch DataLoader does and is created by the `Trainer`. Do you pass other arguments to training_args with respect to data loading ?\r\n\r\nAlso we recently released `.to_iterable_dataset` that does pretty much what you implemented, but using contiguous shards to get a bet... | {
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1,571,720,814 | https://api.github.com/repos/huggingface/datasets/issues/5505/labels{/name} | ### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is a mistake in the docs or the code, but it seems that the only way for a Dataset to be passed a list of indexes by PyTorch (instead of one index at a time) is to define a `__getitems__` method (note the plural) on the Dataset object, and since the HF Dataset doesn't have this, PyTorch executes [this line of code](https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/fetch.py#L58), reverting to fetching one-by-one.
### Steps to reproduce the bug
You can put a breakpoint in `Dataset.__getitem__()` or just print the args from there and see that it's called multiple times for a single `next(iter(dataloader))`, even when using the code from the docs:
```py
from torch.utils.data.sampler import BatchSampler, RandomSampler
batch_sampler = BatchSampler(RandomSampler(ds), batch_size=32, drop_last=False)
dataloader = DataLoader(ds, batch_sampler=batch_sampler)
```
### Expected behavior
The expected behaviour would be for it to fetch batches from the dataset, rather than one-by-one.
To demonstrate that there is room for improvement: once I have a HF dataset `ds`, if I just add this line:
```py
ds.__getitems__ = ds.__getitem__
```
...then the time taken to loop over the dataset improves considerably (for wikitext-103, from one minute to 13 seconds with batch size 32). Probably not a big deal in the grand scheme of things, but seems like an easy win.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.3 | 2023-02-19T18:27:30Z | 5,505 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-06T01:14:55Z | https://api.github.com/repos/huggingface/datasets/issues/5505/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5505/timeline | PyTorch BatchSampler still loads from Dataset one-by-one | https://api.github.com/repos/huggingface/datasets/issues/5505/events | null | {
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} | [] | null | completed | NONE | 2023-02-19T18:27:30Z | null | I_kwDODunzps5dro5u | [
"This change seems to come from a few months ago in the PyTorch side. That's good news and it means we may not need to pass a batch_sampler as soon as we add `Dataset.__getitems__` to get the optimal speed :)\r\n\r\nThanks for reporting ! Would you like to open a PR to add `__getitems__` and remove this outdated do... | {
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1,570,621,242 | https://api.github.com/repos/huggingface/datasets/issues/5504/labels{/name} | Fixes https://github.com/huggingface/datasets/issues/5495
I'm not sure whether we prefer a test here or if timestamps are known to be unsupported (like booleans). The current test at least covers the bug | 2023-02-08T17:28:50Z | 5,504 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-03T23:39:04Z | https://api.github.com/repos/huggingface/datasets/issues/5504/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5504/timeline | don't zero copy timestamps | https://api.github.com/repos/huggingface/datasets/issues/5504/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | {
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1,570,091,225 | https://api.github.com/repos/huggingface/datasets/issues/5502/labels{/name} | Added functionality implementation: sort datasets by multiple keys/columns as discussed in https://github.com/huggingface/datasets/issues/5425. | 2023-02-21T14:46:49Z | 5,502 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-03T16:17:00Z | https://api.github.com/repos/huggingface/datasets/issues/5502/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5502/timeline | Added functionality: sort datasets by multiple keys | https://api.github.com/repos/huggingface/datasets/issues/5502/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Thanks! I've left some comments.\r\n> \r\n> We should also add some tests, mainly to make sure `reverse` behaves as expected. Let me know if you need help with that.\r\n\r\nThanks for the offer! I couldn't find any guidelines on ho... | {
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1,569,644,159 | https://api.github.com/repos/huggingface/datasets/issues/5501/labels{/name} | Original fix: https://github.com/huggingface/huggingface_hub/pull/1267
Not sure this function is actually still called though.
I haven't done benches on this. Is there a dataset where files are hosted on the hub through cloudfront so we can have the same setup as in `hf_hub` ? | 2023-02-09T11:04:11Z | 5,501 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2023-02-03T10:50:10Z | https://api.github.com/repos/huggingface/datasets/issues/5501/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5501/timeline | Increase chunk size for speeding up file downloads | https://api.github.com/repos/huggingface/datasets/issues/5501/events | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5501). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | {
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1,569,257,240 | https://api.github.com/repos/huggingface/datasets/issues/5500/labels{/name} | ### Describe the bug
I use the following scripts to download data from WMT19:
```python
import datasets
from datasets import inspect_dataset, load_dataset_builder
from wmt19.wmt_utils import _TRAIN_SUBSETS,_DEV_SUBSETS
## this is a must due to: https://discuss.huggingface.co/t/load-dataset-hangs-with-local-files/28034/3
if __name__ == '__main__':
dev_subsets,train_subsets = [],[]
for subset in _TRAIN_SUBSETS:
if subset.target=='en' and 'de' in subset.sources:
train_subsets.append(subset.name)
for subset in _DEV_SUBSETS:
if subset.target=='en' and 'de' in subset.sources:
dev_subsets.append(subset.name)
inspect_dataset("wmt19", "./wmt19")
builder = load_dataset_builder(
"./wmt19/wmt_utils.py",
language_pair=("de", "en"),
subsets={
datasets.Split.TRAIN: train_subsets,
datasets.Split.VALIDATION: dev_subsets,
},
)
builder.download_and_prepare()
ds = builder.as_dataset()
ds.to_json("../data/wmt19/ende/data.json")
```
And I got the following error:
```
Traceback (most recent call last): | 0/2 [00:00<?, ?obj/s]
File "draft.py", line 26, in <module>
builder.download_and_prepare() | 0/1 [00:00<?, ?obj/s]
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 605, in download_and_prepare
self._download_and_prepare(%| | 0/1 [00:00<?, ?obj/s]
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 1104, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) | 0/1 [00:00<?, ?obj/s]
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 676, in _download_and_prepare
verify_checksums(s #13: 0%| | 0/1 [00:00<?, ?obj/s]
File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 35, in verify_checksums
raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) | 0/1 [00:00<?, ?obj/s]
datasets.utils.info_utils.UnexpectedDownloadedFile: {'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-de.zipporah0-dedup-clean.tgz', 'https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-europarl-v7.zip', 'https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/rapid2016.zip', 'https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/training-parallel-nc-v13.zip', 'https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/training-parallel-nc-v12.zip', 'https://huggingface.co/datasets/wmt/wmt14/resolve/main-zip/training-parallel-nc-v9.zip', 'https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/training-parallel-nc-v10.zip', 'https://huggingface.co/datasets/wmt/wmt16/resolve/main-zip/translation-task/training-parallel-nc-v11.zip'}
```
### Steps to reproduce the bug
see above
### Expected behavior
download data successfully
### Environment info
datasets==2.1.0
python==3.8
| 2023-02-03T05:52:56Z | 5,500 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-03T05:45:37Z | https://api.github.com/repos/huggingface/datasets/issues/5500/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5500/timeline | WMT19 custom download checksum error | https://api.github.com/repos/huggingface/datasets/issues/5500/events | null | {
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} | [] | null | completed | NONE | 2023-02-03T05:52:56Z | null | I_kwDODunzps5diPcY | [
"I update the `datatsets` version and it works."
] | {
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1,568,937,026 | https://api.github.com/repos/huggingface/datasets/issues/5499/labels{/name} | ### Feature request
When loading a dataset that has been cached locally, the `load_dataset` function takes a lot longer than it should take to fetch the dataset from disk (or memory).
This is particularly noticeable for smaller datasets. For example, wikitext-2, comparing `load_data` (once cached) and `load_from_disk`, the `load_dataset` method takes 40 times longer.
⏱ 4.84s ⮜ load_dataset
⏱ 119ms ⮜ load_from_disk
### Motivation
I assume this is doing something like checking for a newer version.
If so, that's an age old problem: do you make the user wait _every single time they load from cache_ or do you do something like load from cache always, _then_ check for a newer version and alert if they have stale data. The decision usually revolves around what percentage of the time the data will have been updated, and how dangerous old data is.
For most datasets it's extremely unlikely that there will be a newer version on any given run, so 99% of the time this is just wasted time.
Maybe you don't want to make that decision for all users, but at least having the _option_ to not wait for checks would be an improvement.
### Your contribution
. | 2023-02-07T19:35:11Z | 5,499 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"color": "a2eeef",
"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement"
}
] | 2023-02-02T23:34:50Z | https://api.github.com/repos/huggingface/datasets/issues/5499/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5499/timeline | `load_dataset` has ~4 seconds of overhead for cached data | https://api.github.com/repos/huggingface/datasets/issues/5499/events | null | {
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} | [] | null | null | NONE | null | null | I_kwDODunzps5dhBRC | [
"Hi ! To skip the verification step that checks if newer data exist, you can enable offline mode with `HF_DATASETS_OFFLINE=1`.\r\n\r\nAlthough I agree this step should be much faster for datasets hosted on the HF Hub - we could just compare the commit hash from the local data and the remote git repository. We're no... | {
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1,568,190,529 | https://api.github.com/repos/huggingface/datasets/issues/5498/labels{/name} | ### Describe the bug
Hi,
Thanks for the amazing work on the library!
**Describe the bug**
I think I might have noticed a small bug in the filter method.
Having loaded a dataset using `load_dataset`, when I try to filter out empty entries with `batched=True`, I get a TypeError.
### Steps to reproduce the bug
```
train_dataset = train_dataset.filter(
function=lambda example: example["image"] is not None,
batched=True,
batch_size=10)
```
Error message:
```
File .../lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
...
-> 5666 indices_array = [i for i, to_keep in zip(indices, mask) if to_keep]
5667 if indices_mapping is not None:
5668 indices_array = pa.array(indices_array, type=pa.uint64())
TypeError: 'bool' object is not iterable
```
**Removing batched=True allows to bypass the issue.**
### Expected behavior
According to the doc, "[batch_size corresponds to the] number of examples per batch provided to function if batched = True", so we shouldn't need to remove the batchd=True arg?
source: https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.Dataset.filter
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.9.10
- PyArrow version: 10.0.1
- Pandas version: 1.5.3 | 2023-10-08T06:12:47Z | 5,498 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2023-02-02T14:46:49Z | https://api.github.com/repos/huggingface/datasets/issues/5498/comments | null | https://api.github.com/repos/huggingface/datasets/issues/5498/timeline | TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset | https://api.github.com/repos/huggingface/datasets/issues/5498/events | null | {
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} | [] | null | completed | NONE | 2023-02-04T17:19:36Z | null | I_kwDODunzps5deLBB | [
"Hi! Instead of a single boolean, your filter function should return an iterable (of booleans) in the batched mode like so:\r\n```python\r\ntrain_dataset = train_dataset.filter(\r\n function=lambda batch: [image is not None for image in batch[\"image\"]], \r\n batched=True,\r\n batc... | {
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