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https://api.github.com/repos/huggingface/datasets/issues/5624 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5624/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5624/comments | https://api.github.com/repos/huggingface/datasets/issues/5624/events | https://github.com/huggingface/datasets/issues/5624 | 1,617,400,192 | I_kwDODunzps5gZ5GA | 5,624 | glue datasets returning -1 for test split | {
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"Hi @lithafnium, thanks for reporting.\r\n\r\nPlease note that you can use the \"Community\" tab in the corresponding dataset page to start any discussion: https://huggingface.co/datasets/glue/discussions\r\n\r\nIndeed this issue was already raised there (https://huggingface.co/datasets/glue/discussions/5) and answ... | 2023-03-09T14:47:18 | 2023-03-09T16:49:29 | 2023-03-09T16:49:29 | NONE | null | ### Describe the bug
Downloading any dataset from GLUE has -1 as class labels for test split. Train and validation have regular 0/1 class labels. This is also present in the dataset card online.
### Steps to reproduce the bug
```
dataset = load_dataset("glue", "sst2")
for d in dataset:
# prints out -1
print(d["label"]
```
### Expected behavior
Expected behavior should be 0/1 instead of -1.
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.16
- PyArrow version: 8.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/5623 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5623/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5623/comments | https://api.github.com/repos/huggingface/datasets/issues/5623/events | https://github.com/huggingface/datasets/pull/5623 | 1,616,712,665 | PR_kwDODunzps5Lpb4q | 5,623 | Remove set_access_token usage + fail tests if FutureWarning | {
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} | [] | closed | false | null | [] | null | [] | 2023-03-09T08:46:01 | 2023-03-09T15:39:00 | 2023-03-09T15:31:59 | CONTRIBUTOR | null | `set_access_token` is deprecated and will be removed in `huggingface_hub>=0.14`.
This PR removes it from the tests (it was not used in `datasets` source code itself). FYI, it was not needed since `set_access_token` was just setting git credentials and `datasets` doesn't seem to use git anywhere.
In the future, use `set_git_credential` if needed. It is a git-credential-agnostic helper, i.e. you can store your git token in `git-credential-cache`, `git-credential-store`, `osxkeychain`, etc. The legacy `set_access_token` could only set in `git-credential-store` no matter the user preference.
(for context, I found out about this while working on https://github.com/huggingface/huggingface_hub/pull/1381)
---
In addition to this, I have added
```
filterwarnings =
error::FutureWarning:huggingface_hub*
```
to the `setup.cfg` config file to fail on future warnings from `huggingface_hub`. In `hfh`'s CI we trigger on FutureWarning from any package but it's less robust (any package update leads can lead to a failure). No obligation to keep it like that (I can remove it if you prefer) but I think it's a good idea in order to track future FutureWarnings.
FYI, in `huggingface_hub` tests we use `-Werror::FutureWarning --log-cli-level=INFO -sv --durations=0`
- FutureWarning are processed as error
- verbose mode / INFO logs (and above) are captured for easier debugging in github report
- track each test duration, just to see where we can improve. We have a quite long CI (~10min) so it helped improve that. | {
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https://api.github.com/repos/huggingface/datasets/issues/5622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5622/comments | https://api.github.com/repos/huggingface/datasets/issues/5622/events | https://github.com/huggingface/datasets/pull/5622 | 1,615,190,942 | PR_kwDODunzps5LkSj8 | 5,622 | Update README template to better template | {
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https://api.github.com/repos/huggingface/datasets/issues/5621 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5621/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5621/comments | https://api.github.com/repos/huggingface/datasets/issues/5621/events | https://github.com/huggingface/datasets/pull/5621 | 1,615,029,615 | PR_kwDODunzps5LjwD8 | 5,621 | Adding Oracle Cloud to docs | {
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} | [] | closed | false | null | [] | null | [] | 2023-03-08T10:22:50 | 2023-03-11T00:57:18 | 2023-03-11T00:49:56 | CONTRIBUTOR | null | Adding Oracle Cloud's fsspec implementation to the list of supported cloud storage providers. | {
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https://api.github.com/repos/huggingface/datasets/issues/5620 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5620/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5620/comments | https://api.github.com/repos/huggingface/datasets/issues/5620/events | https://github.com/huggingface/datasets/pull/5620 | 1,613,460,520 | PR_kwDODunzps5LefAf | 5,620 | Bump pyarrow to 8.0.0 | {
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} | [] | closed | false | null | [] | null | [] | 2023-03-07T13:31:53 | 2023-03-08T14:01:27 | 2023-03-08T13:54:22 | MEMBER | null | Fix those for Pandas 2.0 (tested [here](https://github.com/huggingface/datasets/actions/runs/4346221280/jobs/7592010397) with pandas==2.0.0.rc0):
```python
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_in_memory - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
===== 2 failed, 2137 passed, 18 skipped, 32 warnings in 212.76s (0:03:32) ======
```
EDIT: also for performance - with 8.0 we can use `.to_reader()` | {
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https://api.github.com/repos/huggingface/datasets/issues/5619 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5619/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5619/comments | https://api.github.com/repos/huggingface/datasets/issues/5619/events | https://github.com/huggingface/datasets/pull/5619 | 1,613,439,709 | PR_kwDODunzps5LeaYP | 5,619 | unpin fsspec | {
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} | [] | closed | false | null | [] | null | [] | 2023-03-07T13:22:41 | 2023-03-07T13:47:01 | 2023-03-07T13:39:02 | MEMBER | null | close https://github.com/huggingface/datasets/issues/5618 | {
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https://api.github.com/repos/huggingface/datasets/issues/5618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5618/comments | https://api.github.com/repos/huggingface/datasets/issues/5618/events | https://github.com/huggingface/datasets/issues/5618 | 1,612,977,934 | I_kwDODunzps5gJBcO | 5,618 | Unpin fsspec < 2023.3.0 once issue fixed | {
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} | [] | closed | false | null | [] | null | [] | 2023-03-07T08:41:51 | 2023-03-07T13:39:03 | 2023-03-07T13:39:03 | MEMBER | null | Unpin `fsspec` upper version once root cause of our CI break is fixed.
See:
- #5614 | {
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https://api.github.com/repos/huggingface/datasets/issues/5617 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5617/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5617/comments | https://api.github.com/repos/huggingface/datasets/issues/5617/events | https://github.com/huggingface/datasets/pull/5617 | 1,612,947,422 | PR_kwDODunzps5LcvI- | 5,617 | Fix CI by temporarily pinning fsspec < 2023.3.0 | {
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} | [] | closed | false | null | [] | null | [] | 2023-03-07T08:18:20 | 2023-03-07T08:44:55 | 2023-03-07T08:37:28 | MEMBER | null | As a hotfix for our CI, temporarily pin `fsspec`:
Fix #5616.
Until root cause is fixed, see:
- #5614 | {
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https://api.github.com/repos/huggingface/datasets/issues/5616 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5616/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5616/comments | https://api.github.com/repos/huggingface/datasets/issues/5616/events | https://github.com/huggingface/datasets/issues/5616 | 1,612,932,508 | I_kwDODunzps5gI2Wc | 5,616 | CI is broken after fsspec-2023.3.0 release | {
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```
FAILED tests/test_filesystem.py::test_compression_filesystems[Bz2FileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
At index 0 diff: {'name': 'file.txt', 'size': 70, 'type': 'file', 'created': 1678175677.1887748, 'islink': False, 'mode': 33188, 'uid': 1001, 'gid': 123, 'mtime': 1678175677.1887748, 'ino': 286957, 'nlink': 1} != 'file.txt'
Full diff:
[
- 'file.txt',
+ {'created': 1678175677.1887748,
+ 'gid': 123,
+ 'ino': 286957,
+ 'islink': False,
+ 'mode': 33188,
+ 'mtime': 1678175677.1887748,
+ 'name': 'file.txt',
+ 'nlink': 1,
+ 'size': 70,
+ 'type': 'file',
+ 'uid': 1001},
]
```
Also:
```
FAILED tests/test_filesystem.py::test_compression_filesystems[GzipFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[Lz4FileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[XzFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[ZstdFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
===== 5 failed, 2134 passed, 18 skipped, 38 warnings in 157.21s (0:02:37) ======
```
See:
- fsspec/filesystem_spec#1205 | {
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https://api.github.com/repos/huggingface/datasets/issues/5615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5615/comments | https://api.github.com/repos/huggingface/datasets/issues/5615/events | https://github.com/huggingface/datasets/issues/5615 | 1,612,552,653 | I_kwDODunzps5gHZnN | 5,615 | IterableDataset.add_column is unable to accept another IterableDataset as a parameter. | {
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"Hi! You can use `concatenate_datasets([ids1, ids2], axis=1)` to do this."
] | 2023-03-07T01:52:00 | 2023-03-09T15:24:05 | 2023-03-09T15:23:54 | NONE | null | ### Describe the bug
`IterableDataset.add_column` occurs an exception when passing another `IterableDataset` as a parameter.
The method seems to accept only eager evaluated values.
https://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/iterable_dataset.py#L1388-L1391
I wrote codes below to make it.
```py
def add_column(dataset: IterableDataset, name: str, add_dataset: IterableDataset, key: str) -> IterableDataset:
iter_add_dataset = iter(add_dataset)
def add_column_fn(example):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: next(iter_add_dataset)[key]}
return dataset.map(add_column_fn)
```
Is there other way to do it? Or is it intended?
### Steps to reproduce the bug
Thie codes below occurs `NotImplementedError`
```py
from datasets import IterableDataset
def gen(num):
yield {f"col{num}": 1}
yield {f"col{num}": 2}
yield {f"col{num}": 3}
ids1 = IterableDataset.from_generator(gen, gen_kwargs={"num": 1})
ids2 = IterableDataset.from_generator(gen, gen_kwargs={"num": 2})
new_ids = ids1.add_column("new_col", ids1)
for row in new_ids:
print(row)
```
### Expected behavior
`IterableDataset.add_column` is able to task `IterableDataset` and lazy evaluated values as a parameter since IterableDataset is lazy evalued.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.7
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/5614 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5614/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5614/comments | https://api.github.com/repos/huggingface/datasets/issues/5614/events | https://github.com/huggingface/datasets/pull/5614 | 1,611,896,357 | PR_kwDODunzps5LZOTd | 5,614 | Fix archive fs test | {
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https://api.github.com/repos/huggingface/datasets/issues/5608 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5608/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5608/comments | https://api.github.com/repos/huggingface/datasets/issues/5608/events | https://github.com/huggingface/datasets/issues/5608 | 1,609,996,563 | I_kwDODunzps5f9pkT | 5,608 | audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files. | {
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"Hi!\r\n\r\n> naming convention of mp3 files\r\n\r\nYes, this could be the problem. MP3 files should end with `.mp3`/`.MP3` to be recognized as audio files.\r\n\r\nIf the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree ... | 2023-03-05T00:14:45 | 2023-03-12T00:02:57 | 2023-03-12T00:02:57 | NONE | null | ### Describe the bug
x = load_dataset("audiofolder", data_dir="x")
When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.)
### Steps to reproduce the bug
x = load_dataset("audiofolder", data_dir="x")
### Expected behavior
x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files).
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5607 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5607/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5607/comments | https://api.github.com/repos/huggingface/datasets/issues/5607/events | https://github.com/huggingface/datasets/pull/5607 | 1,609,166,035 | PR_kwDODunzps5LQPbG | 5,607 | Fix outdated `verification_mode` values | {
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} | [] | closed | false | null | [] | null | [] | 2023-03-03T19:50:29 | 2023-03-09T17:34:13 | 2023-03-09T17:27:07 | CONTRIBUTOR | null | ~I think it makes sense not to save `dataset_info.json` file to a dataset cache directory when loading dataset with `verification_mode="no_checks"` because otherwise when next time the dataset is loaded **without** `verification_mode="no_checks"`, it will be loaded successfully, despite some values in info might not correspond to the ones in the repo which was the reason for using `verification_mode="no_checks"` first.~
Updated values of `verification_mode` to the current ones in some places ("none" -> "no_checks", "all" -> "all_checks") | {
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https://api.github.com/repos/huggingface/datasets/issues/5603 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5603/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5603/comments | https://api.github.com/repos/huggingface/datasets/issues/5603/events | https://github.com/huggingface/datasets/pull/5603 | 1,607,143,509 | PR_kwDODunzps5LJZzG | 5,603 | Don't compute checksums if not necessary in `datasets-cli test` | {
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} | [] | closed | false | null | [] | null | [] | 2023-03-02T16:42:39 | 2023-03-03T15:45:32 | 2023-03-03T15:38:28 | MEMBER | null | we only need them if there exists a `dataset_infos.json` | {
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https://api.github.com/repos/huggingface/datasets/issues/5598 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5598/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5598/comments | https://api.github.com/repos/huggingface/datasets/issues/5598/events | https://github.com/huggingface/datasets/pull/5598 | 1,605,018,478 | PR_kwDODunzps5LCMiX | 5,598 | Fix push_to_hub with no dataset_infos | {
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cc @clefourrier | {
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https://api.github.com/repos/huggingface/datasets/issues/5597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5597/comments | https://api.github.com/repos/huggingface/datasets/issues/5597/events | https://github.com/huggingface/datasets/issues/5597 | 1,604,928,721 | I_kwDODunzps5fqUTR | 5,597 | in-place dataset update | {
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"We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications.\r\n\r\nIn your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nNote that datasets loaded from disk (memory mapped) are not load... | 2023-03-01T12:58:18 | 2023-03-02T13:30:41 | 2023-03-02T03:47:00 | NONE | null | ### 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` | {
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https://api.github.com/repos/huggingface/datasets/issues/5596 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5596/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5596/comments | https://api.github.com/repos/huggingface/datasets/issues/5596/events | https://github.com/huggingface/datasets/issues/5596 | 1,604,919,993 | I_kwDODunzps5fqSK5 | 5,596 | [TypeError: Couldn't cast array of type] Can only load a subset of the dataset | {
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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... | 2023-03-01T12:53:08 | 2023-03-02T11:12:11 | 2023-03-02T11:12:11 | NONE | null | ### 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/5592 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5592/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5592/comments | https://api.github.com/repos/huggingface/datasets/issues/5592/events | https://github.com/huggingface/datasets/pull/5592 | 1,603,619,124 | PR_kwDODunzps5K9dWr | 5,592 | Fix docstring example | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-28T18:42:37 | 2023-02-28T19:26:33 | 2023-02-28T19:19:15 | MEMBER | null | Fixes #5581 to use the correct output for the `set_format` method. | {
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https://api.github.com/repos/huggingface/datasets/issues/5588 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5588/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5588/comments | https://api.github.com/repos/huggingface/datasets/issues/5588/events | https://github.com/huggingface/datasets/pull/5588 | 1,603,304,766 | PR_kwDODunzps5K8YYz | 5,588 | Flatten dataset on the fly in `save_to_disk` | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-28T15:37:46 | 2023-02-28T17:28:35 | 2023-02-28T17:21:17 | CONTRIBUTOR | null | 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) | {
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https://api.github.com/repos/huggingface/datasets/issues/5587 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5587/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5587/comments | https://api.github.com/repos/huggingface/datasets/issues/5587/events | https://github.com/huggingface/datasets/pull/5587 | 1,603,139,420 | PR_kwDODunzps5K70pp | 5,587 | Fix `sort` with indices mapping | {
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Fix #5586 | {
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https://api.github.com/repos/huggingface/datasets/issues/5586 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5586/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5586/comments | https://api.github.com/repos/huggingface/datasets/issues/5586/events | https://github.com/huggingface/datasets/issues/5586 | 1,602,961,544 | I_kwDODunzps5fi0CI | 5,586 | .sort() is broken when used after .filter(), only in 2.10.0 | {
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"Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix"
] | 2023-02-28T12:18:09 | 2023-02-28T18:17:26 | 2023-02-28T17:21:59 | NONE | null | ### 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/5585 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5585/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5585/comments | https://api.github.com/repos/huggingface/datasets/issues/5585/events | https://github.com/huggingface/datasets/issues/5585 | 1,602,190,030 | I_kwDODunzps5ff3rO | 5,585 | Cache is not transportable | {
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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 ... | 2023-02-28T00:53:06 | 2023-02-28T21:26:52 | 2023-02-28T21:26:52 | NONE | null | ### 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
- ``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5584 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5584/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5584/comments | https://api.github.com/repos/huggingface/datasets/issues/5584/events | https://github.com/huggingface/datasets/issues/5584 | 1,601,821,808 | I_kwDODunzps5fedxw | 5,584 | Unable to load coyo700M dataset | {
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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... | 2023-02-27T19:35:03 | 2023-02-28T07:27:59 | 2023-02-28T07:27:58 | NONE | null | ### 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/5583 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5583/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5583/comments | https://api.github.com/repos/huggingface/datasets/issues/5583/events | https://github.com/huggingface/datasets/pull/5583 | 1,601,583,625 | PR_kwDODunzps5K2mIz | 5,583 | Do no write index by default when exporting a dataset | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-27T17:04:46 | 2023-02-28T13:52:15 | 2023-02-28T13:44:04 | CONTRIBUTOR | null | 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) | {
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"Thanks for reporting!"
] | 2023-02-27T08:03:09 | 2023-02-28T19:19:17 | 2023-02-28T19:19:17 | CONTRIBUTOR | null | ### 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/5580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5580/comments | https://api.github.com/repos/huggingface/datasets/issues/5580/events | https://github.com/huggingface/datasets/pull/5580 | 1,600,431,792 | PR_kwDODunzps5Kys1c | 5,580 | Support cloud storage in load_dataset via fsspec | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-27T04:06:05 | 2023-03-11T01:02:49 | 2023-03-11T00:55:40 | CONTRIBUTOR | null | 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. | {
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https://api.github.com/repos/huggingface/datasets/issues/5578 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5578/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5578/comments | https://api.github.com/repos/huggingface/datasets/issues/5578/events | https://github.com/huggingface/datasets/pull/5578 | 1,598,863,119 | PR_kwDODunzps5Kto96 | 5,578 | Add `huggingface_hub` version to env cli command | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-24T15:37:43 | 2023-02-27T17:28:25 | 2023-02-27T17:21:09 | CONTRIBUTOR | null | Add the `huggingface_hub` version to the `env` command's output. | {
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https://api.github.com/repos/huggingface/datasets/issues/5577 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5577/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5577/comments | https://api.github.com/repos/huggingface/datasets/issues/5577/events | https://github.com/huggingface/datasets/issues/5577 | 1,598,587,665 | I_kwDODunzps5fSIMR | 5,577 | Cannot load `the_pile_openwebtext2` | {
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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"
] | 2023-02-24T13:01:48 | 2023-02-24T14:01:09 | 2023-02-24T14:01:09 | NONE | null | ### 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/5576 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5576/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5576/comments | https://api.github.com/repos/huggingface/datasets/issues/5576/events | https://github.com/huggingface/datasets/issues/5576 | 1,598,582,744 | I_kwDODunzps5fSG_Y | 5,576 | 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. | {
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"Duplicated issue."
] | 2023-02-24T12:57:49 | 2023-02-24T12:58:31 | 2023-02-24T12:58:18 | NONE | null | 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_
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https://api.github.com/repos/huggingface/datasets/issues/5574 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5574/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5574/comments | https://api.github.com/repos/huggingface/datasets/issues/5574/events | https://github.com/huggingface/datasets/issues/5574 | 1,598,104,691 | I_kwDODunzps5fQSRz | 5,574 | c4 dataset streaming fails with `FileNotFoundError` | {
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"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 ... | 2023-02-24T07:57:32 | 2023-03-06T13:14:11 | 2023-02-27T04:03:38 | NONE | null | ### 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
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https://api.github.com/repos/huggingface/datasets/issues/5573 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5573/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5573/comments | https://api.github.com/repos/huggingface/datasets/issues/5573/events | https://github.com/huggingface/datasets/pull/5573 | 1,597,400,836 | PR_kwDODunzps5Kop7n | 5,573 | Use soundfile for mp3 decoding instead of torchaudio | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-23T19:19:44 | 2023-02-28T20:25:14 | 2023-02-28T20:16:02 | CONTRIBUTOR | null | 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/5572 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5572/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5572/comments | https://api.github.com/repos/huggingface/datasets/issues/5572/events | https://github.com/huggingface/datasets/issues/5572 | 1,597,257,624 | I_kwDODunzps5fNDeY | 5,572 | Datasets 2.10.0 does not reuse the dataset cache | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-23T17:28:11 | 2023-02-23T18:03:55 | 2023-02-23T18:03:55 | NONE | null | ### 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 | {
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"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!"
] | 2023-02-23T16:50:11 | 2023-02-24T13:21:47 | 2023-02-24T13:21:47 | NONE | null | ### 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 | {
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"follo... | null | [
"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... | 2023-02-23T13:45:33 | 2023-02-24T13:22:36 | 2023-02-24T13:22:36 | CONTRIBUTOR | null | ### 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
datasets==2.10.0 | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-22T15:09:30 | 2023-03-10T13:53:03 | 2023-03-10T13:45:43 | MEMBER | null | 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) | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-22T07:56:10 | 2023-02-23T11:07:49 | 2023-02-23T11:00:58 | CONTRIBUTOR | null | 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. | {
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If so - I need to double check the config but for me locally it works as expected. | {
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https://api.github.com/repos/huggingface/datasets/issues/5560 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5560/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5560/comments | https://api.github.com/repos/huggingface/datasets/issues/5560/events | https://github.com/huggingface/datasets/pull/5560 | 1,593,809,978 | PR_kwDODunzps5Kcml6 | 5,560 | Ensure last tqdm update in `map` | {
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* ensure the TQDM bar gets the last progress update
* when a map function fails, avoid throwing a chained exception in the single-proc mode | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-21T15:26:26 | 2023-02-21T17:21:37 | 2023-02-21T17:14:29 | MEMBER | null | #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` | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-21T15:13:36 | 2023-03-01T13:46:04 | 2023-02-23T13:50:27 | CONTRIBUTOR | null | Colab now has Ubuntu 20.04 which already has `ffmpeg` of required (>4) version. | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-21T14:04:42 | 2023-02-21T14:19:54 | 2023-02-21T14:12:39 | MEMBER | null | Otherwise it would show a `Map` progress bar, since it uses `map` under the hood | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-21T10:45:50 | 2023-02-21T12:49:50 | 2023-02-21T12:42:52 | MEMBER | null | ...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 | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-20T18:15:43 | 2023-02-21T12:46:10 | 2023-02-21T12:43:38 | MEMBER | null | 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. | {
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https://api.github.com/repos/huggingface/datasets/issues/5552 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5552/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5552/comments | https://api.github.com/repos/huggingface/datasets/issues/5552/events | https://github.com/huggingface/datasets/pull/5552 | 1,592,186,703 | PR_kwDODunzps5KXMjA | 5,552 | Make tiktoken tokenizers hashable | {
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https://api.github.com/repos/huggingface/datasets/issues/5551 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5551/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5551/comments | https://api.github.com/repos/huggingface/datasets/issues/5551/events | https://github.com/huggingface/datasets/pull/5551 | 1,592,140,836 | PR_kwDODunzps5KXCof | 5,551 | Suggest scikit-learn instead of sklearn | {
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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.
```
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https://api.github.com/repos/huggingface/datasets/issues/5550 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5550/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5550/comments | https://api.github.com/repos/huggingface/datasets/issues/5550/events | https://github.com/huggingface/datasets/pull/5550 | 1,591,409,475 | PR_kwDODunzps5KUl5i | 5,550 | Resolve four broken refs in the docs | {
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## 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/5549 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5549/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5549/comments | https://api.github.com/repos/huggingface/datasets/issues/5549/events | https://github.com/huggingface/datasets/pull/5549 | 1,590,836,848 | PR_kwDODunzps5KSsi3 | 5,549 | Apply ruff flake8-comprehension checks | {
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} | [] | closed | false | null | [] | null | [] | 2023-02-19T20:09:28 | 2023-02-23T14:06:39 | 2023-02-23T13:59:39 | CONTRIBUTOR | null | Fix #5548
Apply ruff's flake8-comprehension checks for better performance, and more readable code. | {
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] | closed | false | null | [] | null | [] | 2023-02-19T20:05:38 | 2023-02-23T13:59:41 | 2023-02-23T13:59:41 | CONTRIBUTOR | null | ### 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. | {
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