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https://api.github.com/repos/huggingface/datasets/issues/6683 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6683/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6683/comments | https://api.github.com/repos/huggingface/datasets/issues/6683/events | https://github.com/huggingface/datasets/pull/6683 | 2,142,751,955 | PR_kwDODunzps5nTxGu | 6,683 | Fix imagefolder dataset url | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6683). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-02-19T16:26:51 | 2024-02-19T17:24:25 | 2024-02-19T17:18:10 | COLLABORATOR | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4996 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4996/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4996/comments | https://api.github.com/repos/huggingface/datasets/issues/4996/events | https://github.com/huggingface/datasets/issues/4996 | 1,379,345,161 | I_kwDODunzps5SNyMJ | 4,996 | Dataset Viewer issue for Jean-Baptiste/wikiner_fr | {
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"The script uses `Dataset.load_from_disk`, which as you can expect, doesn't work in streaming mode.\r\n\r\nIt would probably be more practical to load the dataset locally using `Dataset.load_from_disk` first and then `push_to_hub` to upload it in Parquet on the Hub",
"I've transferred this issue to the Hub repo: ... | 2022-09-20T12:32:07 | 2022-09-27T12:35:44 | 2022-09-27T12:35:44 | COLLABORATOR | null | null | {
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} | ### Link
https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr
### Description
```
Error code: StreamingRowsError
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response
rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)
File "/src/services/worker/src/worker/utils.py", line 123, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/responses/first_rows.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 718, in __iter__
for key, example in self._iter():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 708, in _iter
yield from ex_iterable
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 112, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/tmp/modules-cache/datasets_modules/datasets/Jean-Baptiste--wikiner_fr/683a580ba6ec769d508f7dfc603a651667b0ed3817b1ae5bfd45f97cc024923f/wikiner_fr.py", line 165, in _generate_examples
dataset = Dataset.load_from_disk(filepath)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1210, in load_from_disk
with open(Path(dataset_path, config.DATASET_STATE_JSON_FILENAME).as_posix(), encoding="utf-8") as state_file:
FileNotFoundError: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json'
```
Is it an error with the dataset script, or the data itself, @huggingface/datasets?
https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr/tree/main
### Owner
No | {
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https://api.github.com/repos/huggingface/datasets/issues/6461 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6461/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6461/comments | https://api.github.com/repos/huggingface/datasets/issues/6461/events | https://github.com/huggingface/datasets/pull/6461 | 2,018,850,731 | PR_kwDODunzps5gykvO | 6,461 | Fix shard retry mechanism in `push_to_hub` | {
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} | [] | closed | false | null | [] | null | [
"@Wauplin Maybe `504` should be added to the `retry_on_status_codes` tuple [here](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300) to guard against https://github.com/huggingface/datasets/issues/3872",
"We could but I'm not sure to have ... | 2023-11-30T14:57:14 | 2023-12-01T17:57:39 | 2023-12-01T17:51:33 | COLLABORATOR | null | null | null | When it fails, `preupload_lfs_files` throws a [`RuntimeError`](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/_commit_api.py#L402) error and chains the original HTTP error. This PR modifies the retry mechanism's error handling to account for that.
Fix https://github.com/huggingface/datasets/issues/6392 | {
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https://api.github.com/repos/huggingface/datasets/issues/7451 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7451/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7451/comments | https://api.github.com/repos/huggingface/datasets/issues/7451/events | https://github.com/huggingface/datasets/pull/7451 | 2,919,835,663 | PR_kwDODunzps6OpwDz | 7,451 | Fix resuming after `ds.set_epoch(new_epoch)` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7451). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-03-14T10:31:25 | 2025-03-14T10:50:11 | 2025-03-14T10:50:09 | MEMBER | null | null | null | close https://github.com/huggingface/datasets/issues/7447 | {
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https://api.github.com/repos/huggingface/datasets/issues/6236 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6236/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6236/comments | https://api.github.com/repos/huggingface/datasets/issues/6236/events | https://github.com/huggingface/datasets/issues/6236 | 1,893,648,480 | I_kwDODunzps5w3shg | 6,236 | Support buffer shuffle for to_tf_dataset | {
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"cc @Rocketknight1 ",
"Hey! You can implement this yourself, just:\r\n\r\n1) Create the dataset with `to_tf_dataset()` with `shuffle=False`\r\n2) Add an `unbatch()` at the end (or use batch_size=1)\r\n3) Add a `shuffle()` to the resulting dataset with your desired buffer size\r\n4) Add a `batch()` at the end agai... | 2023-09-13T03:19:44 | 2023-09-18T01:11:21 | null | NONE | null | null | {
"completed": 0,
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} | ### Feature request
I'm using to_tf_dataset to convert a large dataset to tf.data.Dataset and use Keras fit to train model.
Currently, to_tf_dataset only supports full size shuffle, which can be very slow on large dataset.
tf.data.Dataset support buffer shuffle by default.
shuffle(
buffer_size, seed=None, reshuffle_each_iteration=None, name=None
)
### Motivation
I'm very frustrated to find the loading with shuffling large dataset is very slow. It seems impossible to shuffle before training Keras with big dataset.
### Your contribution
NA | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6804/comments | https://api.github.com/repos/huggingface/datasets/issues/6804/events | https://github.com/huggingface/datasets/pull/6804 | 2,238,035,124 | PR_kwDODunzps5sYJFF | 6,804 | Fix --repo-type order in cli upload docs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6804). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-04-11T15:39:09 | 2024-04-11T16:24:57 | 2024-04-11T16:18:47 | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5708 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5708/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5708/comments | https://api.github.com/repos/huggingface/datasets/issues/5708/events | https://github.com/huggingface/datasets/issues/5708 | 1,655,023,642 | I_kwDODunzps5ipaga | 5,708 | Dataset sizes are in MiB instead of MB in dataset cards | {
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"Example of bulk edit: https://huggingface.co/datasets/aeslc/discussions/5",
"looks great! \r\n\r\nDo you encode the fact that you've already converted a dataset? (to not convert it twice) or do you base yourself on the info contained in `dataset_info`",
"I am only looping trough the dataset cards, assuming tha... | 2023-04-05T06:36:03 | 2023-12-21T10:20:28 | 2023-12-21T10:20:27 | MEMBER | null | null | {
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} | As @severo reported in an internal discussion (https://github.com/huggingface/moon-landing/issues/5929):
Now we show the dataset size:
- from the dataset card (in the side column)
- from the datasets-server (in the viewer)
But, even if the size is the same, we see a mismatch because the viewer shows MB, while the info from the README generally shows MiB (even if it's written MB -> https://huggingface.co/datasets/blimp/blob/main/README.md?code=true#L1932)
<img width="664" alt="Capture d’écran 2023-04-04 à 10 16 01" src="https://user-images.githubusercontent.com/1676121/229730887-0bd8fa6e-9462-46c6-bd4e-4d2c5784cabb.png">
TODO: Values to be fixed in: `Size of downloaded dataset files:`, `Size of the generated dataset:` and `Total amount of disk used:`
- [x] Bulk edit on the Hub to fix this in all canonical datasets
- [x] Bulk PR on the Hub to fix ancient canonical datasets that were moved to organizations | {
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https://api.github.com/repos/huggingface/datasets/issues/6731 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6731/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6731/comments | https://api.github.com/repos/huggingface/datasets/issues/6731/events | https://github.com/huggingface/datasets/issues/6731 | 2,182,844,673 | I_kwDODunzps6CG5EB | 6,731 | Unexpected behavior when using load_dataset with streaming=True in a for loop | {
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"This is normal behavior in python when using `lambda`: the `i` defined in your `lambda` refers to the global variable `i` in your loop, and `i` equals to `1` when you run your `for e in res[0]` line.\r\n\r\nYou should pass `fn_kwargs` that will be passed to your `lambda` instead of using the global variable:\r\n\r... | 2024-03-12T23:26:43 | 2024-04-16T00:00:00 | 2024-04-16T00:00:00 | NONE | null | null | {
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} | ### Describe the bug
### My Code
```
from datasets import load_dataset
res=[]
for i in [0,1]:
di=load_dataset(
"json",
data_files='path_to.json',
split='train',
streaming=True,
).map(lambda x: {"source": i})
res.append(di)
for e in res[0]:
print(e)
```
### Unexpected Behavior
Data in `res[0]` has `source=1`. However the expected value is 0.
### FYI
I further switch `streaming` to `False`. And the output value is as expected (0). So there may exist bugs in setting `streaming=True` in a for loop.
### Environment
Python 3.8.0
datasets==2.18.0
transformers==4.28.1
### Steps to reproduce the bug
1. Create a Json file with any content.
2. Run the provided code.
3. Switch `streaming` to `False` and run again to see the expected behavior.
### Expected behavior
The expected behavior is the data are mapped with its corresponding value in the for loop.
### Environment info
Python 3.8.0
datasets==2.18.0
transformers==4.28.1
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https://api.github.com/repos/huggingface/datasets/issues/7210 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7210/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7210/comments | https://api.github.com/repos/huggingface/datasets/issues/7210/events | https://github.com/huggingface/datasets/issues/7210 | 2,575,883,939 | I_kwDODunzps6ZiN6j | 7,210 | Convert Array features to numpy arrays rather than lists by default | {
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} | ### Feature request
It is currently quite easy to cause massive slowdowns when using datasets and not familiar with the underlying data conversions by e.g. making bad choices of formatting.
Would it be more user-friendly to set defaults that avoid this as much as possible? e.g. format Array features as numpy arrays rather than python lists
### Motivation
Default array formatting leads to slow performance: e.g.
```python
import numpy as np
from datasets import Dataset, Features, Array3D
features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")})
dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features)
```
```python
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~1.4 s
```python
ds = dataset.to_iterable_dataset()
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~10s
```python
ds = dataset.with_format("numpy")
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~0.04s
```python
ds = dataset.to_iterable_dataset().with_format("numpy")
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~0.04s
### Your contribution
May be able to contribute | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4957 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4957/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4957/comments | https://api.github.com/repos/huggingface/datasets/issues/4957/events | https://github.com/huggingface/datasets/pull/4957 | 1,366,532,849 | PR_kwDODunzps4-nGIk | 4,957 | Add `Dataset.from_generator` | {
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"I restarted the builder PR job just in case",
"_The documentation is not available anymore as the PR was closed or merged._",
"CI is now green. https://github.com/huggingface/doc-builder/pull/296 explains why it failed."
] | 2022-09-08T15:08:25 | 2022-09-16T14:46:35 | 2022-09-16T14:44:18 | COLLABORATOR | null | null | null | Add `Dataset.from_generator` to the API to allow creating datasets from data larger than RAM. The implementation relies on a packaged module not exposed in `load_dataset` to tie this method with `datasets`' caching mechanism.
Closes https://github.com/huggingface/datasets/issues/4417 | {
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https://api.github.com/repos/huggingface/datasets/issues/6890 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6890/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6890/comments | https://api.github.com/repos/huggingface/datasets/issues/6890/events | https://github.com/huggingface/datasets/issues/6890 | 2,288,699,041 | I_kwDODunzps6Iasah | 6,890 | add `with_transform` and/or `set_transform` to IterableDataset | {
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} | ### Feature request
when working with a really large dataset it would save us a lot of time (and compute resources) to use either with_transform or the set_transform from the Dataset class instead of waiting for the entire dataset to map
### Motivation
don't want to wait for a really long dataset to map, this would give IterableDataset an extra advantage over the Dataset class.
reducing time and resources
### Your contribution
I am a little busy with my job search lately, but would post about this feature in my social media.
Apologies again (dad going to kick me out soon), if I ever have some free time I will contribute to making this a reality, but that's going to be hard
/ (┬┬﹏┬┬)\ | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4628 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4628/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4628/comments | https://api.github.com/repos/huggingface/datasets/issues/4628/events | https://github.com/huggingface/datasets/pull/4628 | 1,293,361,308 | PR_kwDODunzps46zvFJ | 4,628 | Fix time type `_arrow_to_datasets_dtype` conversion | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-04T16:20:15 | 2022-07-07T14:08:38 | 2022-07-07T13:57:12 | COLLABORATOR | null | null | null | Fix #4620
The issue stems from the fact that `pa.array([time_data]).type` returns `DataType(time64[unit])`, which doesn't expose the `unit` attribute, instead of `Time64Type(time64[unit])`. I believe this is a bug in PyArrow. Luckily, the both types have the same `str()`, so in this PR I call `pa.type_for_alias(str(type))` to convert them both to the `Time64Type(time64[unit])` format.
cc @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/5263 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5263/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5263/comments | https://api.github.com/repos/huggingface/datasets/issues/5263/events | https://github.com/huggingface/datasets/issues/5263 | 1,455,252,626 | I_kwDODunzps5WvWSS | 5,263 | Save a dataset in a determined number of shards | {
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} | This is useful to distribute the shards to training nodes.
This can be implemented in `save_to_disk` and can also leverage multiprocessing to speed up the process | {
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https://api.github.com/repos/huggingface/datasets/issues/6301 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6301/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6301/comments | https://api.github.com/repos/huggingface/datasets/issues/6301/events | https://github.com/huggingface/datasets/pull/6301 | 1,940,183,999 | PR_kwDODunzps5cpPVh | 6,301 | Unpin `tensorflow` maximum version | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\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... | 2023-10-12T14:58:07 | 2023-10-12T15:58:20 | 2023-10-12T15:49:54 | COLLABORATOR | null | null | null | Removes the temporary pin introduced in #6264 | {
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https://api.github.com/repos/huggingface/datasets/issues/6579 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6579/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6579/comments | https://api.github.com/repos/huggingface/datasets/issues/6579/events | https://github.com/huggingface/datasets/issues/6579 | 2,075,407,473 | I_kwDODunzps57tDRx | 6,579 | Unable to load `eli5` dataset with streaming | {
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"Hi @haok1402, I have created an issue in the Discussion tab of the corresponding dataset: https://huggingface.co/datasets/eli5/discussions/7\r\nLet's continue the discussion there!"
] | 2024-01-10T23:44:20 | 2024-01-11T09:19:18 | 2024-01-11T09:19:17 | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Unable to load `eli5` dataset with streaming.
### Steps to reproduce the bug
This fails with FileNotFoundError: https://files.pushshift.io/reddit/submissions
```
from datasets import load_dataset
load_dataset("eli5", streaming=True)
```
This works correctly.
```
from datasets import load_dataset
load_dataset("eli5")
```
### Expected behavior
- Loading `eli5` dataset should not raise an error under the streaming mode.
- Or at the very least, show a warning that streaming mode is not supported with `eli5` dataset.
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-6.2.0-39-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.6.0
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https://api.github.com/repos/huggingface/datasets/issues/6831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6831/comments | https://api.github.com/repos/huggingface/datasets/issues/6831/events | https://github.com/huggingface/datasets/pull/6831 | 2,258,537,405 | PR_kwDODunzps5tdTy_ | 6,831 | Add docs about the CLI | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6831). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Concretely, the docs about convert_to_parquet are here: https://moon-ci-docs.huggingfac... | 2024-04-23T10:41:03 | 2024-04-26T16:51:09 | 2024-04-25T10:44:10 | MEMBER | null | null | null | Add docs about the CLI.
Close #6830.
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/6733 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6733/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6733/comments | https://api.github.com/repos/huggingface/datasets/issues/6733/events | https://github.com/huggingface/datasets/issues/6733 | 2,186,811,724 | I_kwDODunzps6CWBlM | 6,733 | EmptyDatasetError when loading dataset downloaded with HuggingFace cli | {
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"Hi! `datasets` is not compatible with `huggingface_hub`'s cache structure, hence the error.\r\n\r\nYou can track https://github.com/huggingface/datasets/issues/5080 to get notified when this is implemented."
] | 2024-03-14T16:41:27 | 2024-03-15T18:09:02 | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I am using a cluster that does not have access to the internet when given a job. I tried downloading the dataset using the huggingface-cli command and then loading it with load_dataset but I get an error:
```raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None```
The dataset I'm using is "lmsys/chatbot_arena_conversations". The folder structure is
- README.md
- data
- train-00000-of-00001-cced8514c7ed782a.parquet
### Steps to reproduce the bug
1. Download dataset using HuggingFace CLI: ```huggingface-cli download lmsys/chatbot_arena_conversations --local-dir ./lmsys/chatbot_arena_conversations```
2. In Python
```
from datasets import load_dataset
load_dataset("lmsys/chatbot_arena_conversations")
```
### Expected behavior
Should return a Dataset Dict in the form of
```
DatasetDict({
train: Dataset({
features: [...],
num_rows: 33,000
})
})
```
### Environment info
Python 3.11.5
Datasets 2.18.0
Transformers 4.38.2
Pytorch 2.2.0
Pyarrow 15.0.1
Rocky Linux release 8.9 (Green Obsidian)
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https://api.github.com/repos/huggingface/datasets/issues/6110 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6110/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6110/comments | https://api.github.com/repos/huggingface/datasets/issues/6110/events | https://github.com/huggingface/datasets/issues/6110 | 1,831,110,633 | I_kwDODunzps5tJIfp | 6,110 | [BUG] Dataset initialized from in-memory data does not create cache. | {
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"This is expected behavior. You must provide `cache_file_name` when performing `.map` on an in-memory dataset for the result to be cached."
] | 2023-08-01T11:58:58 | 2023-08-17T14:03:01 | 2023-08-17T14:03:00 | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
`Dataset` initialized from in-memory data (dictionary in my case, haven't tested with other types) does not create cache when processed with the `map` method, unlike `Dataset` initialized by other methods such as `load_dataset`.
### Steps to reproduce the bug
```python
# below code was run the second time so the map function can be loaded from cache if exists
from datasets import load_dataset, Dataset
dataset = load_dataset("tatsu-lab/alpaca")['train']
dataset = dataset.map(lambda x: {'input': x['input'] + 'hi'}) # some random map
print(len(dataset.cache_files))
# 1
# copy the exact same data but initialize from a dictionary
memory_dataset = Dataset.from_dict({
'instruction': dataset['instruction'],
'input': dataset['input'],
'output': dataset['output'],
'text': dataset['text']})
memory_dataset = memory_dataset.map(lambda x: {'input': x['input'] + 'hi'}) # exact same map
print(len(memory_dataset.cache_files))
# Map: 100%|██████████| 52002[/52002]
# 0
```
### Expected behavior
The `map` function should create cache regardless of the method the `Dataset` was created.
### Environment info
- `datasets` version: 2.14.2
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5731 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5731/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5731/comments | https://api.github.com/repos/huggingface/datasets/issues/5731/events | https://github.com/huggingface/datasets/pull/5731 | 1,662,012,913 | PR_kwDODunzps5N_7Un | 5,731 | Temporarily pin fsspec | {
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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==8.0.0\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... | 2023-04-11T08:33:15 | 2023-04-11T08:57:45 | 2023-04-11T08:47:55 | MEMBER | null | null | null | Fix #5730. | {
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https://api.github.com/repos/huggingface/datasets/issues/4825 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4825/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4825/comments | https://api.github.com/repos/huggingface/datasets/issues/4825/events | https://github.com/huggingface/datasets/pull/4825 | 1,335,856,882 | PR_kwDODunzps49BYWL | 4,825 | [Windows] Fix Access Denied when using os.rename() | {
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"Cool thank you ! Maybe we can just replace `os.rename` by `shutil.move` instead ?",
"> Cool thank you ! Maybe we can just replace `os.rename` by `shutil.move` instead ?\r\n\r\nYes, I think that could be a better solution, but I didn't test it in Linux (e.g. Ubuntu) to guarantee that `os.rename()` could be comple... | 2022-08-11T11:57:15 | 2022-08-24T13:09:07 | 2022-08-24T13:09:07 | CONTRIBUTOR | null | null | null | In this PR, we are including an additional step when `os.rename()` raises a PermissionError.
Basically, we will use `shutil.move()` on the temp files.
Fix #2937 | {
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https://api.github.com/repos/huggingface/datasets/issues/6859 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6859/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6859/comments | https://api.github.com/repos/huggingface/datasets/issues/6859/events | https://github.com/huggingface/datasets/pull/6859 | 2,274,996,774 | PR_kwDODunzps5uVIoZ | 6,859 | Support folder-based datasets with large metadata.jsonl | {
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} | [] | open | false | null | [] | null | [] | 2024-05-02T09:07:26 | 2024-05-02T09:07:26 | null | NONE | null | null | null | I tried creating an `imagefolder` dataset with a 714MB `metadata.jsonl` but got the error below. This pull request fixes the problem by increasing the block size like the message suggests.
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("imagefolder", data_dir="data-for-upload")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/path/to/datasets/load.py", line 2609, in load_dataset
builder_instance.download_and_prepare(
...
File "/path/to/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 245, in _read_metadata
return paj.read_json(f)
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7526 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7526/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7526/comments | https://api.github.com/repos/huggingface/datasets/issues/7526/events | https://github.com/huggingface/datasets/issues/7526 | 3,005,107,536 | I_kwDODunzps6zHk1Q | 7,526 | [WIP] Faster downloads/uploads with Xet storage | {
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Over the past few weeks, Hugging Face’s [Xet Team](https://huggingface.co/xet-team) took a major step forward by [migrating the first Model and Dataset repositories off LFS and to Xet storage](https://huggingface.co/posts/jsulz/911431940353906).
See more information on the HF blog: https://huggingface.co/blog/xet-on-the-hub
You can already enable Xet on Hugging Face account to benefit from faster downloads and uploads :)
We’re finalizing an official integration with the `huggingface_hub`library that will mean you get the benefits of Xet without any significant changes to your current workflow. In the meantime you might see this warning in `push_to_hub()`:
```
Uploading files as bytes or binary IO objects is not supported by Xet Storage.
```
This means the `huggingface_hub` + Xet integration isn't enabled for `datasets` yet. Stay tuned !
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https://api.github.com/repos/huggingface/datasets/issues/5326 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5326/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5326/comments | https://api.github.com/repos/huggingface/datasets/issues/5326/events | https://github.com/huggingface/datasets/issues/5326 | 1,471,634,168 | I_kwDODunzps5Xt1r4 | 5,326 | No documentation for main branch is built | {
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- #5250
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the docs for main branch are no longer built.
The change introduced only triggers the docs building for releases. | {
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https://api.github.com/repos/huggingface/datasets/issues/7467 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7467/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7467/comments | https://api.github.com/repos/huggingface/datasets/issues/7467/events | https://github.com/huggingface/datasets/issues/7467 | 2,930,067,107 | I_kwDODunzps6upUaj | 7,467 | load_dataset with streaming hangs on parquet datasets | {
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"Hi ! The issue comes from `pyarrow`, I reported it here: https://github.com/apache/arrow/issues/45214 (feel free to comment / thumb up).\n\nAlternatively we can try to find something else than `ParquetFileFragment.to_batches()` to iterate on Parquet data and keep the option the pass `filters=`..."
] | 2025-03-18T23:33:54 | 2025-03-25T10:28:04 | null | NONE | null | null | {
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} | ### Describe the bug
When I try to load a dataset with parquet files (e.g. "bigcode/the-stack") the dataset loads, but python interpreter can't exit and hangs
### Steps to reproduce the bug
```python3
import datasets
print('Start')
dataset = datasets.load_dataset("bigcode/the-stack", data_dir="data/yaml", streaming=True, split="train")
it = iter(dataset)
next(it)
print('Finish')
```
The program prints finish but doesn't exit and hangs indefinitely.
I tried this on two different machines and several datasets.
### Expected behavior
The program exits successfully
### Environment info
datasets==3.4.1
Python 3.12.9.
MacOS and Ubuntu Linux | null | {
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