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https://api.github.com/repos/huggingface/datasets/issues/7668 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7668/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7668/comments | https://api.github.com/repos/huggingface/datasets/issues/7668/events | https://github.com/huggingface/datasets/issues/7668 | 3,199,039,322 | I_kwDODunzps6-rXda | 7,668 | Broken EXIF crash the whole program | {
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"total": 0
} | ### Describe the bug
When parsing this image in the ImageNet1K dataset, the `datasets` crashs whole training process just because unable to parse an invalid EXIF tag.
### Steps to reproduce the bug
Use the `datasets.Image.decode_example` method to decode the aforementioned image could reproduce the bug.
The decoding function will throw an unhandled exception at the `image.getexif()` method call due to invalid utf-8 stream in EXIF tags.
```
File lib/python3.12/site-packages/datasets/features/image.py:188, in Image.decode_example(self, value, token_per_repo_id)
186 image = PIL.Image.open(BytesIO(bytes_))
187 image.load() # to avoid "Too many open files" errors
--> 188 if image.getexif().get(PIL.Image.ExifTags.Base.Orientation) is not None:
189 image = PIL.ImageOps.exif_transpose(image)
190 if self.mode and self.mode != image.mode:
File lib/python3.12/site-packages/PIL/Image.py:1542, in Image.getexif(self)
1540 xmp_tags = self.info.get("XML:com.adobe.xmp")
1541 if not xmp_tags and (xmp_tags := self.info.get("xmp")):
-> 1542 xmp_tags = xmp_tags.decode("utf-8")
1543 if xmp_tags:
1544 match = re.search(r'tiff:Orientation(="|>)([0-9])', xmp_tags)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa8 in position 4312: invalid start byte
```
### Expected behavior
The invalid EXIF tag should simply be ignored or issue a warning message, instead of crash the whole program at once.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-6.5.0-18-generic-x86_64-with-glibc2.35
- Python version: 3.12.11
- `huggingface_hub` version: 0.33.0
- PyArrow version: 20.0.0
- Pandas version: 2.3.0
- `fsspec` version: 2025.3.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7667 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7667/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7667/comments | https://api.github.com/repos/huggingface/datasets/issues/7667/events | https://github.com/huggingface/datasets/pull/7667 | 3,196,251,707 | PR_kwDODunzps6dGmm8 | 7,667 | Fix infer list of images | {
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https://api.github.com/repos/huggingface/datasets/issues/7666 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7666/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7666/comments | https://api.github.com/repos/huggingface/datasets/issues/7666/events | https://github.com/huggingface/datasets/pull/7666 | 3,196,220,722 | PR_kwDODunzps6dGf7E | 7,666 | Backward compat list feature | {
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https://api.github.com/repos/huggingface/datasets/issues/7665 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7665/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7665/comments | https://api.github.com/repos/huggingface/datasets/issues/7665/events | https://github.com/huggingface/datasets/issues/7665 | 3,193,239,955 | I_kwDODunzps6-VPmT | 7,665 | Function load_dataset() misinterprets string field content as part of dataset schema when dealing with `.jsonl` files | {
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} | [] | closed | false | null | [] | null | 1 | 2025-07-01T17:14:53Z | 2025-07-01T17:17:48Z | 2025-07-01T17:17:48Z | NONE | null | null | {
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} | ### Describe the bug
When loading a `.jsonl` file using `load_dataset("json", data_files="data.jsonl", split="train")`, the function misinterprets the content of a string field as if it were part of the dataset schema.
In my case there is a field `body:` with a string value
```
"### Describe the bug (...) ,action: string, datetime: timestamp[s], author: string, (...) Pandas version: 1.3.4"
```
As a result, I got an exception
```
"TypeError: Couldn't cast array of type timestamp[s] to null".
```
Full stack-trace in the attached file below.
I also attach a minimized dataset (data.json, a single entry) that reproduces the error.
**Observations**(on the minimal example):
- if I remove _all fields before_ `body`, a different error appears,
- if I remove _all fields after_ `body`, yet another error appears,
- if `body` is _the only field_, the error disappears.
So this might be one complex bug or several edge cases interacting. I haven’t dug deeper.
Also changing the file extension to `.json` or `.txt` avoids the problem. This suggests **a possible workaround** for the general case: convert `.jsonl` to `.json`. Though I haven’t verified correctness of that workaround yet.
Anyway my understanding is that `load_dataset` with first argument set to "json" should properly handle `.jsonl` files. Correct me if I'm wrong.
[stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt)
[data.json](https://github.com/user-attachments/files/21004164/data.json)
P.S.
I discovered this while going through the HuggingFace tutorial. Specifically [this part](https://huggingface.co/learn/llm-course/chapter5/5?fw=pt).I will try to inform the tutorial team about this issue, as it can be a showstopper for young 🤗 adepts.
### Steps to reproduce the bug
1. Download attached [data.json](https://github.com/user-attachments/files/21004164/data.json) file.
2. Run the following code which should work correctly:
```
from datasets import load_dataset
load_dataset("json", data_files="data.json", split="train")
```
3. Change extension of the `data` file to `.jsonl` and run:
```
from datasets import load_dataset
load_dataset("json", data_files="data.jsonl", split="train")
```
This will trigger an error like the one in the attached [stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt).
One can also try removing fields before the `body` field and after it. These actions give different errors.
### Expected behavior
Parsing data in `.jsonl` format should yield the same result as parsing the same data in `.json` format. In any case, the content of a string field should never be interpreted as part of the dataset schema.
### Environment info
datasets version: _3.6.0_
pyarrow version: _20.0.0_
Python version: _3.11.9_
platform version: _macOS-15.5-arm64-arm-64bit_ | {
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https://api.github.com/repos/huggingface/datasets/issues/7664 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7664/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7664/comments | https://api.github.com/repos/huggingface/datasets/issues/7664/events | https://github.com/huggingface/datasets/issues/7664 | 3,193,239,035 | I_kwDODunzps6-VPX7 | 7,664 | Function load_dataset() misinterprets string field content as part of dataset schema when dealing with `.jsonl` files | {
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} | [] | open | false | null | [] | null | 1 | 2025-07-01T17:14:32Z | 2025-07-03T13:01:59Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
When loading a `.jsonl` file using `load_dataset("json", data_files="data.jsonl", split="train")`, the function misinterprets the content of a string field as if it were part of the dataset schema.
In my case there is a field `body:` with a string value
```
"### Describe the bug (...) ,action: string, datetime: timestamp[s], author: string, (...) Pandas version: 1.3.4"
```
As a result, I got an exception
```
"TypeError: Couldn't cast array of type timestamp[s] to null".
```
Full stack-trace in the attached file below.
I also attach a minimized dataset (data.json, a single entry) that reproduces the error.
**Observations**(on the minimal example):
- if I remove _all fields before_ `body`, a different error appears,
- if I remove _all fields after_ `body`, yet another error appears,
- if `body` is _the only field_, the error disappears.
So this might be one complex bug or several edge cases interacting. I haven’t dug deeper.
Also changing the file extension to `.json` or `.txt` avoids the problem. This suggests **a possible workaround** for the general case: convert `.jsonl` to `.json`. Though I haven’t verified correctness of that workaround yet.
Anyway my understanding is that `load_dataset` with first argument set to "json" should properly handle `.jsonl` files. Correct me if I'm wrong.
[stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt)
[data.json](https://github.com/user-attachments/files/21004164/data.json)
P.S.
I discovered this while going through the HuggingFace tutorial. Specifically [this part](https://huggingface.co/learn/llm-course/chapter5/5?fw=pt). I will try to inform the tutorial team about this issue, as it can be a showstopper for young 🤗 adepts.
### Steps to reproduce the bug
1. Download attached [data.json](https://github.com/user-attachments/files/21004164/data.json) file.
2. Run the following code which should work correctly:
```
from datasets import load_dataset
load_dataset("json", data_files="data.json", split="train")
```
3. Change extension of the `data` file to `.jsonl` and run:
```
from datasets import load_dataset
load_dataset("json", data_files="data.jsonl", split="train")
```
This will trigger an error like the one in the attached [stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt).
One can also try removing fields before the `body` field and after it. These actions give different errors.
### Expected behavior
Parsing data in `.jsonl` format should yield the same result as parsing the same data in `.json` format. In any case, the content of a string field should never be interpreted as part of the dataset schema.
### Environment info
datasets version: _3.6.0_
pyarrow version: _20.0.0_
Python version: _3.11.9_
platform version: _macOS-15.5-arm64-arm-64bit_ | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7663 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7663/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7663/comments | https://api.github.com/repos/huggingface/datasets/issues/7663/events | https://github.com/huggingface/datasets/pull/7663 | 3,192,582,371 | PR_kwDODunzps6c6aJF | 7,663 | Custom metadata filenames | {
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} | [] | closed | false | null | [] | null | 1 | 2025-07-01T13:50:36Z | 2025-07-01T13:58:41Z | 2025-07-01T13:58:39Z | MEMBER | null | null | null | example: https://huggingface.co/datasets/lhoestq/overlapping-subsets-imagefolder/tree/main
To make multiple subsets for an imagefolder (one metadata file per subset), e.g.
```yaml
configs:
- config_name: default
metadata_filenames:
- metadata.csv
- config_name: other
metadata_filenames:
- metadata2.csv
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7662 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7662/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7662/comments | https://api.github.com/repos/huggingface/datasets/issues/7662/events | https://github.com/huggingface/datasets/issues/7662 | 3,190,805,531 | I_kwDODunzps6-L9Qb | 7,662 | Applying map after transform with multiprocessing will cause OOM | {
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} | [] | open | false | null | [] | null | 0 | 2025-07-01T05:45:57Z | 2025-07-01T05:45:57Z | null | NONE | null | null | {
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} | ### Describe the bug
I have a 30TB dataset. When I perform add_column and cast_column operations on it and then execute a multiprocessing map, it results in an OOM (Out of Memory) error. However, if I skip the add_column and cast_column steps and directly run the map, there is no OOM. After debugging step by step, I found that the OOM is caused at this point, and I suspect it’s because the add_column and cast_column operations are not cached, which causes the entire dataset to be loaded in each subprocess, leading to the OOM. The critical line of code is: https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/utils/py_utils.py#L607
Note num_process=1 would not cause OOM. I'm confused.
### Steps to reproduce the bug
For reproduce, you can load dataset and set cache_dir (for caching): amphion/Emilia-Dataset which is a veru large datasets that RAM can not fits.
And apply the map with multiprocessing after a transform operation (e.g. add_column, cast_column).
As long as num_process>1, it must cause OOM.
### Expected behavior
It should not cause OOM.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-5.10.134-16.101.al8.x86_64-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.33.1
- PyArrow version: 20.0.0
- Pandas version: 2.3.0
- `fsspec` version: 2024.6.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7661 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7661/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7661/comments | https://api.github.com/repos/huggingface/datasets/issues/7661/events | https://github.com/huggingface/datasets/pull/7661 | 3,190,408,237 | PR_kwDODunzps6czBDi | 7,661 | fix del tqdm lock error | {
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} | [] | open | false | null | [] | null | 0 | 2025-07-01T02:04:02Z | 2025-07-01T02:33:04Z | null | NONE | null | null | null | for issue https://github.com/huggingface/datasets/issues/7660 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7660 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7660/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7660/comments | https://api.github.com/repos/huggingface/datasets/issues/7660/events | https://github.com/huggingface/datasets/issues/7660 | 3,189,028,251 | I_kwDODunzps6-FLWb | 7,660 | AttributeError: type object 'tqdm' has no attribute '_lock' | {
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} | [] | open | false | null | [] | null | 2 | 2025-06-30T15:57:16Z | 2025-07-03T15:14:27Z | null | NONE | null | null | {
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} | ### Describe the bug
`AttributeError: type object 'tqdm' has no attribute '_lock'`
It occurs when I'm trying to load datasets in thread pool.
Issue https://github.com/huggingface/datasets/issues/6066 and PR https://github.com/huggingface/datasets/pull/6067 https://github.com/huggingface/datasets/pull/6068 tried to fix this.
### Steps to reproduce the bug
Will have to try several times to reproduce the error because it is concerned with threads.
1. save some datasets for test
```pythonfrom datasets import Dataset, DatasetDict
import os
os.makedirs("test_dataset_shards", exist_ok=True)
for i in range(10):
data = Dataset.from_dict({"text": [f"example {j}" for j in range(1000000)]})
data = DatasetDict({'train': data})
data.save_to_disk(f"test_dataset_shards/shard_{i}")
```
2. load them in a thread pool
```python
from datasets import load_from_disk
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor, as_completed
import glob
datas = glob.glob('test_dataset_shards/shard_*')
with ThreadPoolExecutor(max_workers=10) as pool:
futures = [pool.submit(load_from_disk, it) for it in datas]
datas = []
for future in tqdm(as_completed(futures), total=len(futures)):
datas.append(future.result())
```
### Expected behavior
no exception raised
### Environment info
datasets==2.19.0
python==3.10 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7659/comments | https://api.github.com/repos/huggingface/datasets/issues/7659/events | https://github.com/huggingface/datasets/pull/7659 | 3,187,882,217 | PR_kwDODunzps6cqkou | 7,659 | Update the beans dataset link in Preprocess | {
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} | [] | closed | false | null | [] | null | 0 | 2025-06-30T09:58:44Z | 2025-07-01T14:01:42Z | 2025-07-01T14:01:42Z | CONTRIBUTOR | null | null | null | In the Preprocess tutorial, the to "the beans dataset" is incorrect. Fixed. | {
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https://api.github.com/repos/huggingface/datasets/issues/7658 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7658/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7658/comments | https://api.github.com/repos/huggingface/datasets/issues/7658/events | https://github.com/huggingface/datasets/pull/7658 | 3,187,800,504 | PR_kwDODunzps6cqTMs | 7,658 | Fix: Prevent loss of info.features and column_names in IterableDatasetDict.map when features is None | {
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} | [] | closed | false | null | [] | null | 5 | 2025-06-30T09:31:12Z | 2025-07-01T16:26:30Z | 2025-07-01T16:26:12Z | CONTRIBUTOR | null | null | null | This PR fixes a bug where calling `IterableDatasetDict.map()` or `IterableDataset.map()` with the default `features=None` argument would overwrite the existing `info.features` attribute with `None`. This, in turn, caused the resulting dataset to lose its schema, breaking downstream usage of attributes like `column_names`.
Why
Previously, the code would always set `info.features = features`, even if `features` was `None`. When mapping with removal of columns or other transformations, this led to the destruction of the schema and caused failures in code that relied on the dataset schema being present.
How
We now update `info.features` only if `features` is not `None`. This preserves the original schema unless the user explicitly provides a new one.
Reference
Fixes #7568 | {
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https://api.github.com/repos/huggingface/datasets/issues/7657 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7657/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7657/comments | https://api.github.com/repos/huggingface/datasets/issues/7657/events | https://github.com/huggingface/datasets/pull/7657 | 3,186,036,016 | PR_kwDODunzps6cks2E | 7,657 | feat: add subset_name as alias for name in load_dataset | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-29T10:39:00Z | 2025-06-29T10:55:11Z | null | CONTRIBUTOR | null | null | null | fixes #7637
This PR introduces subset_name as a user-facing alias for the name (previously `config_name`) argument in load_dataset. It aligns terminology with the Hugging Face Hub UI (which shows “Subset”), reducing confusion for new users.
Supports `subset_name` in `load_dataset()`
Adds `.subset_name` property to DatasetBuilder
Maintains full backward compatibility
Raises clear error if name and `subset_name` conflict | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7656 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7656/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7656/comments | https://api.github.com/repos/huggingface/datasets/issues/7656/events | https://github.com/huggingface/datasets/pull/7656 | 3,185,865,686 | PR_kwDODunzps6ckPHc | 7,656 | fix(iterable): ensure MappedExamplesIterable supports state_dict for resume | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-29T07:50:13Z | 2025-06-29T07:50:13Z | null | CONTRIBUTOR | null | null | null | Fixes #7630
### Problem
When calling `.map()` on an `IterableDataset`, resuming from a checkpoint skips a large number of samples. This is because `MappedExamplesIterable` did not implement `state_dict()` or `load_state_dict()`, so checkpointing was not properly delegated to the underlying iterable.
### What This PR Does
This patch adds:
```python
def state_dict(self):
return self.ex_iterable.state_dict()
def load_state_dict(self, state):
self.ex_iterable.load_state_dict(state)
```
to MappedExamplesIterable, so the wrapped base iterable's state can be saved and restored as expected.
Result
Using .map() no longer causes sample skipping after checkpoint resume.
Let me know if a dedicated test case is required — happy to add one! | null | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-28T09:20:21Z | 2025-06-28T09:20:21Z | null | CONTRIBUTOR | null | null | null | Fixes #7504
This PR resolves a compatibility error when loading datasets via `load_dataset()` using outdated arguments like `use_auth_token`.
**What was happening:**
Users passing `use_auth_token` in `load_dataset(..., use_auth_token=...)` encountered a `ValueError`: BuilderConfig ParquetConfig(...) doesn't have a 'use_auth_token' key.
**Why:**
`use_auth_token` has been deprecated and removed from config definitions (replaced by `token`), but the `load_dataset()` function still forwarded it via `**config_kwargs` to BuilderConfigs, leading to unrecognized key errors.
**Fix:**
We now intercept and strip `use_auth_token` from `config_kwargs` inside `load_dataset`, replacing it with a warning:
```python
if "use_auth_token" in config_kwargs:
logger.warning("The 'use_auth_token' argument is deprecated. Please use 'token' instead.")
config_kwargs.pop("use_auth_token")
```
This ensures legacy compatibility while guiding users to switch to the token argument.
Let me know if you'd prefer a deprecation error instead of a warning. Thanks! | null | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-28T08:47:36Z | 2025-06-28T08:47:36Z | null | CONTRIBUTOR | null | null | null | ### Related Issue
Fixes #7503
Partially addresses #5044 by allowing `load_dataset()` to auto-detect and gracefully delegate to `load_from_disk()` for locally saved datasets.
---
### What does this PR do?
This PR introduces a minimal fallback mechanism in `load_dataset()` that detects when the provided `path` points to a dataset saved using `save_to_disk()`, and automatically redirects to `load_from_disk()`.
#### 🐛 Before (unexpected metadata-only rows):
```python
ds = load_dataset("/path/to/saved_dataset")
# → returns rows with only internal metadata (_data_files, _fingerprint, etc.)
````
#### ✅ After (graceful fallback):
```python
ds = load_dataset("/path/to/saved_dataset")
# → logs a warning and internally switches to load_from_disk()
```
---
### Why is this useful?
* Prevents confusion when reloading local datasets saved via `save_to_disk()`.
* Enables smoother compatibility with frameworks (e.g., TRL, `lighteval`) that rely on `load_dataset()` calls.
* Fully backward-compatible — hub-based loading, custom builders, and streaming remain untouched.
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} | [] | open | false | null | [] | null | 2 | 2025-06-27T16:18:42Z | 2025-07-03T09:52:48Z | null | CONTRIBUTOR | null | null | null | Fixes #7594
This PR adds support for filtering specific columns when loading datasets from .json or .jsonl files — similar to how the columns=... argument works for Parquet.
As suggested, support for the `columns=...` argument (previously available for Parquet) has now been extended to **JSON and JSONL** loading via `load_dataset(...)`. You can now load only specific keys/columns and skip the rest — which should help in cases where some fields are unclean, inconsistent, or just unnecessary.
### Example:
```python
from datasets import load_dataset
dataset = load_dataset("json", data_files="your_data.jsonl", columns=["id", "title"])
print(dataset["train"].column_names)
# Output: ['id', 'title']
```
### Summary of changes:
* Added `columns: Optional[List[str]]` to `JsonConfig`
* Updated `_generate_tables()` to filter selected columns
* Forwarded `columns` argument from `load_dataset()` to the config
* Added test for validation(should be fine!)
Let me know if you'd like the same to be added for CSV or others as a follow-up — happy to help. | null | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-27T13:12:11Z | 2025-06-30T08:19:37Z | null | NONE | null | null | null | Fixes #7650.
The metadata files generated by the `DatasetDict.save_to_file` function are not included in the folder_based_builder's metadata list, causing issues when only 1 actual data file is present, as described in issue #7650.
This PR adds these filenames to the builder, allowing correct loading. | null | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-27T12:54:25Z | 2025-06-27T12:54:25Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I currently have multiple datasets (train+validation) saved as 50MB shards. For one dataset the validation pair is small enough to fit into a single shard and this apparently causes problems when loading the dataset. I created the datasets using a DatasetDict, saved them as 50MB arrow files for streaming and then load each dataset. I have no problem loading any of the other datasets with more than 1 arrow file/shard.
The error indicates the training set got loaded in arrow format (correct) and the validation set in json (incorrect). This seems to be because some of the metadata files are considered as dataset files.
```
Error loading /nfs/dataset_pt-uk: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('validation'): ('json', {})}
```
Concretely, there is a mismatch between the metadata created by the `DatasetDict.save_to_file` and the builder for `datasets.load_dataset`:
https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/data_files.py#L107
The `folder_based_builder` lists all files and with 1 arrow file the json files (that are actually metadata) are in the majority.
https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58
### Steps to reproduce the bug
Create a dataset with metadata and 1 arrow file in validation and multiple arrow files in the training set, following the above description. In my case, I saved the files via:
```python
dataset = DatasetDict({
'train': train_dataset,
'validation': val_dataset
})
dataset.save_to_disk(output_path, max_shard_size="50MB")
```
### Expected behavior
The dataset would get loaded.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-6.14.0-22-generic-x86_64-with-glibc2.41
- Python version: 3.12.7
- `huggingface_hub` version: 0.31.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7649 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7649/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7649/comments | https://api.github.com/repos/huggingface/datasets/issues/7649/events | https://github.com/huggingface/datasets/pull/7649 | 3,181,481,444 | PR_kwDODunzps6cW0sQ | 7,649 | Enable parallel shard upload in push_to_hub() using num_proc | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-27T05:59:03Z | 2025-06-27T06:03:46Z | null | CONTRIBUTOR | null | null | null | Fixes #7591
### Add num_proc support to `push_to_hub()` for parallel shard upload
This PR adds support for parallel upload of dataset shards via the `num_proc` argument in `Dataset.push_to_hub()`.
📌 While the `num_proc` parameter was already present in the `push_to_hub()` signature and correctly passed to `_push_parquet_shards_to_hub()`, it was not being used to parallelize the upload.
🔧 This PR updates the internal `_push_parquet_shards_to_hub()` function to:
- Use `multiprocessing.Pool` and `iflatmap_unordered()` for concurrent shard upload when `num_proc > 1`
- Preserve original serial upload behavior if `num_proc` is `None` or ≤ 1
- Keep tqdm progress and commit behavior unchanged
Let me know if any test coverage or further changes are needed!
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https://api.github.com/repos/huggingface/datasets/issues/7648 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7648/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7648/comments | https://api.github.com/repos/huggingface/datasets/issues/7648/events | https://github.com/huggingface/datasets/pull/7648 | 3,181,409,736 | PR_kwDODunzps6cWmSn | 7,648 | Fix misleading add_column() usage example in docstring | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-27T05:27:04Z | 2025-06-27T05:27:54Z | null | CONTRIBUTOR | null | null | null | Fixes #7611
This PR fixes the usage example in the Dataset.add_column() docstring, which previously implied that add_column() modifies the dataset in-place.
Why:
The method returns a new dataset with the additional column, and users must assign the result to a variable to preserve the change.
This should make the behavior clearer for users.
@lhoestq @davanstrien | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7647 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7647/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7647/comments | https://api.github.com/repos/huggingface/datasets/issues/7647/events | https://github.com/huggingface/datasets/issues/7647 | 3,178,952,517 | I_kwDODunzps69evdF | 7,647 | loading mozilla-foundation--common_voice_11_0 fails | {
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} | [] | open | false | null | [] | null | 1 | 2025-06-26T12:23:48Z | 2025-06-27T12:29:03Z | null | NONE | null | null | {
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} | ### Describe the bug
Hello everyone,
i am trying to load `mozilla-foundation--common_voice_11_0` and it fails. Reproducer
```
import datasets
datasets.load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test", streaming=True, trust_remote_code=True)
```
and it fails with
```
File ~/opt/envs/.../lib/python3.10/site-packages/datasets/utils/file_utils.py:827, in _add_retries_to_file_obj_read_method.<locals>.read_with_retries(*args, **kwargs)
825 for retry in range(1, max_retries + 1):
826 try:
--> 827 out = read(*args, **kwargs)
828 break
829 except (
830 _AiohttpClientError,
831 asyncio.TimeoutError,
832 requests.exceptions.ConnectionError,
833 requests.exceptions.Timeout,
834 ) as err:
File /usr/lib/python3.10/codecs.py:322, in BufferedIncrementalDecoder.decode(self, input, final)
319 def decode(self, input, final=False):
320 # decode input (taking the buffer into account)
321 data = self.buffer + input
--> 322 (result, consumed) = self._buffer_decode(data, self.errors, final)
323 # keep undecoded input until the next call
324 self.buffer = data[consumed:]
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte
```
When i remove streaming then everything is good but i need `streaming=True`
### Steps to reproduce the bug
```
import datasets
datasets.load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test", streaming=True, trust_remote_code=True)
```
### Expected behavior
Expected that it will download dataset
### Environment info
datasets==3.6.0
python3.10
on all platforms linux/win/mac | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7646 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7646/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7646/comments | https://api.github.com/repos/huggingface/datasets/issues/7646/events | https://github.com/huggingface/datasets/pull/7646 | 3,178,036,854 | PR_kwDODunzps6cLhrM | 7,646 | Introduces automatic subset-level grouping for folder-based dataset builders #7066 | {
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} | [] | open | false | null | [] | null | 3 | 2025-06-26T07:01:37Z | 2025-06-27T18:04:04Z | null | CONTRIBUTOR | null | null | null | Fixes #7066
This PR introduces automatic **subset-level grouping** for folder-based dataset builders by:
1. Adding a utility function `group_files_by_subset()` that clusters files by root name (ignoring digits and shard suffixes).
2. Integrating this logic into `FolderBasedBuilder._split_generators()` to yield one split per subset.
3. Adding unit tests for the grouping function.
4. Updating the documentation to describe this new behavior under `docs/source/repository_structure.mdx`.
---
### Motivation
Datasets with files like:
```
train0.jsonl
train1.jsonl
animals.jsonl
metadata.jsonl
```
will now be **automatically grouped** as:
- `"train"` subset → `train0.jsonl`, `train1.jsonl`
- `"animals"` subset → `animals.jsonl`
- `"metadata"` subset → `metadata.jsonl`
This enables structured multi-subset loading even when the dataset doesn't follow traditional `train/validation/test` split conventions.
---
### Files Changed
- `src/datasets/data_files.py`: added `group_files_by_subset()` utility
- `src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py`: grouped files before yielding splits
- `tests/test_data_files.py`: added unit test `test_group_files_by_subset`
- `docs/source/repository_structure.mdx`: documented subset grouping for maintainers and users
---
### Benefits
- More flexible and robust dataset split logic
- Enables logical grouping of user-uploaded files without nested folder structure
- Backward-compatible with all existing folder-based configs
---
Ready for review ✅ | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7645 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7645/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7645/comments | https://api.github.com/repos/huggingface/datasets/issues/7645/events | https://github.com/huggingface/datasets/pull/7645 | 3,176,810,164 | PR_kwDODunzps6cHkp- | 7,645 | `ClassLabel` docs: Correct value for unknown labels | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-25T20:01:35Z | 2025-06-25T20:01:35Z | null | NONE | null | null | null | This small change fixes the documentation to to be compliant with what happens in `encode_example`.
https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/features/features.py#L1126-L1129 | null | {
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} | [] | closed | false | null | [] | null | 1 | 2025-06-25T17:07:55Z | 2025-06-25T17:10:30Z | 2025-06-25T17:08:01Z | MEMBER | null | null | null | fix error from https://github.com/huggingface/datasets/pull/7643 | {
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https://api.github.com/repos/huggingface/datasets/issues/7643 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7643/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7643/comments | https://api.github.com/repos/huggingface/datasets/issues/7643/events | https://github.com/huggingface/datasets/pull/7643 | 3,176,354,431 | PR_kwDODunzps6cGEeK | 7,643 | Backward compat sequence instance | {
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} | [] | closed | false | null | [] | null | 1 | 2025-06-25T17:05:09Z | 2025-06-25T17:07:40Z | 2025-06-25T17:05:44Z | MEMBER | null | null | null | useful to still get `isinstance(Sequence(Value("int64")), Sequence)`for downstream libs like evaluate | {
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https://api.github.com/repos/huggingface/datasets/issues/7641 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7641/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7641/comments | https://api.github.com/repos/huggingface/datasets/issues/7641/events | https://github.com/huggingface/datasets/pull/7641 | 3,175,953,405 | PR_kwDODunzps6cEwUl | 7,641 | update docs and docstrings | {
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https://api.github.com/repos/huggingface/datasets/issues/7640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7640/comments | https://api.github.com/repos/huggingface/datasets/issues/7640/events | https://github.com/huggingface/datasets/pull/7640 | 3,175,914,924 | PR_kwDODunzps6cEofU | 7,640 | better features repr | {
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before:
```python
In [3]: ds.features
Out[3]:
{'json': {'id': Value(dtype='string', id=None),
'metadata:transcript': [{'end': Value(dtype='float64', id=None),
'start': Value(dtype='float64', id=None),
'transcript': Value(dtype='string', id=None),
'words': [{'end': Value(dtype='float64', id=None),
'score': Value(dtype='float64', id=None),
'start': Value(dtype='float64', id=None),
'word': Value(dtype='string', id=None)}]}],
'metadata:vad': [{'end': Value(dtype='float64', id=None),
'start': Value(dtype='float64', id=None)}]},
'mp4': Value(dtype='binary', id=None),
'npz': {'boxes_and_keypoints:box': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'boxes_and_keypoints:is_valid_box': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None),
'boxes_and_keypoints:keypoints': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'movement:EmotionArousalToken': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:EmotionValenceToken': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:FAUToken': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:FAUValue': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:alignment_head_rotation': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:alignment_translation': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'movement:emotion_arousal': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:emotion_scores': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:emotion_valence': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:expression': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:frame_latent': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:gaze_encodings': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:head_encodings': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:hypernet_features': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:is_valid': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'smplh:body_pose': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'smplh:global_orient': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'smplh:is_valid': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None),
'smplh:left_hand_pose': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'smplh:right_hand_pose': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'smplh:translation': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None)},
'wav': Audio(sampling_rate=None, mono=True, decode=True, id=None),
'__key__': Value(dtype='string', id=None),
'__url__': Value(dtype='string', id=None)}
```
after:
```python
In [3]: ds.features
Out[3]:
{'json': {'id': Value('string'),
'metadata:transcript': List({'end': Value('float64'), 'start': Value('float64'), 'transcript': Value('string'), 'words': List({'end': Value('float64'), 'score': Value('float64'), 'start': Value('float64'), 'word': Value('string')})}),
'metadata:vad': List({'end': Value('float64'), 'start': Value('float64')})},
'mp4': Value('binary'),
'npz': {'boxes_and_keypoints:box': List(List(Value('float32'))),
'boxes_and_keypoints:is_valid_box': List(Value('bool')),
'boxes_and_keypoints:keypoints': List(List(List(Value('float32')))),
'movement:EmotionArousalToken': List(List(Value('float32'))),
'movement:EmotionValenceToken': List(List(Value('float32'))),
'movement:FAUToken': List(List(Value('float32'))),
'movement:FAUValue': List(List(Value('float32'))),
'movement:alignment_head_rotation': List(List(Value('float32'))),
'movement:alignment_translation': List(List(List(Value('float32')))),
'movement:emotion_arousal': List(List(Value('float32'))),
'movement:emotion_scores': List(List(Value('float32'))),
'movement:emotion_valence': List(List(Value('float32'))),
'movement:expression': List(List(Value('float32'))),
'movement:frame_latent': List(List(Value('float32'))),
'movement:gaze_encodings': List(List(Value('float32'))),
'movement:head_encodings': List(List(Value('float32'))),
'movement:hypernet_features': List(List(Value('float32'))),
'movement:is_valid': List(List(Value('float32'))),
'smplh:body_pose': List(List(List(Value('float32')))),
'smplh:global_orient': List(List(Value('float32'))),
'smplh:is_valid': List(Value('bool')),
'smplh:left_hand_pose': List(List(List(Value('float32')))),
'smplh:right_hand_pose': List(List(List(Value('float32')))),
'smplh:translation': List(List(Value('float32')))},
'wav': Audio(sampling_rate=None, decode=True, stream_index=None),
'__key__': Value('string'),
'__url__': Value('string')}
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https://api.github.com/repos/huggingface/datasets/issues/7639 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7639/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7639/comments | https://api.github.com/repos/huggingface/datasets/issues/7639/events | https://github.com/huggingface/datasets/pull/7639 | 3,175,616,169 | PR_kwDODunzps6cDoAf | 7,639 | fix save_infos | {
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https://api.github.com/repos/huggingface/datasets/issues/7638 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7638/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7638/comments | https://api.github.com/repos/huggingface/datasets/issues/7638/events | https://github.com/huggingface/datasets/pull/7638 | 3,172,645,391 | PR_kwDODunzps6b5vpZ | 7,638 | Add ignore_decode_errors option to Image feature for robust decoding #7612 | {
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} | [] | open | false | null | [] | null | 3 | 2025-06-24T16:47:51Z | 2025-07-03T16:37:38Z | null | CONTRIBUTOR | null | null | null | This PR implements support for robust image decoding in the `Image` feature, as discussed in issue #7612.
## 🔧 What was added
- A new boolean field: `ignore_decode_errors` (default: `False`)
- If set to `True`, any exceptions during decoding will be caught, and `None` will be returned instead of raising an error
```python
features = Features({
"image": Image(decode=True, ignore_decode_errors=True),
})
````
This enables robust iteration over potentially corrupted datasets — especially useful when streaming datasets like WebDataset or image-heavy public sets where sample corruption is common.
## 🧪 Behavior
* If `ignore_decode_errors=False` (default), decoding behaves exactly as before
* If `True`, decoding errors are caught, and a warning is emitted:
```
[Image.decode_example] Skipped corrupted image: ...
```
## 🧵 Linked issue
Closes #7612
Let me know if you'd like a follow-up test PR. Happy to write one! | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7637 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7637/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7637/comments | https://api.github.com/repos/huggingface/datasets/issues/7637/events | https://github.com/huggingface/datasets/issues/7637 | 3,171,883,522 | I_kwDODunzps69DxoC | 7,637 | Introduce subset_name as an alias of config_name | {
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] | open | false | null | [] | null | 4 | 2025-06-24T12:49:01Z | 2025-07-01T16:08:33Z | null | MEMBER | null | null | {
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} | ### Feature request
Add support for `subset_name` as an alias for `config_name` in the datasets library and related tools (such as loading scripts, documentation, and metadata).
### Motivation
The Hugging Face Hub dataset viewer displays a column named **"Subset"**, which refers to what is currently technically called config_name in the datasets library. This inconsistency has caused confusion for many users, especially those unfamiliar with the internal terminology.
I have repeatedly received questions from users trying to understand what "config" means, and why it doesn’t match what they see as "subset" on the Hub. Renaming everything to `subset_name` might be too disruptive, but introducing subset_name as a clear alias for config_name could significantly improve user experience without breaking backward compatibility.
This change would:
- Align terminology across the Hub UI and datasets codebase
- Reduce user confusion, especially for newcomers
- Make documentation and examples more intuitive
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/7636 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7636/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7636/comments | https://api.github.com/repos/huggingface/datasets/issues/7636/events | https://github.com/huggingface/datasets/issues/7636 | 3,170,878,167 | I_kwDODunzps68_8LX | 7,636 | "open" in globals()["__builtins__"], an error occurs: "TypeError: argument of type 'module' is not iterable" | {
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} | [] | closed | false | null | [] | null | 3 | 2025-06-24T08:09:39Z | 2025-07-01T01:54:08Z | 2025-07-01T01:54:08Z | NONE | null | null | {
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} | When I run the following code, an error occurs: "TypeError: argument of type 'module' is not iterable"
```python
print("open" in globals()["__builtins__"])
```
Traceback (most recent call last):
File "./main.py", line 2, in <module>
print("open" in globals()["__builtins__"])
^^^^^^^^^^^^^^^^^^^^^^
TypeError: argument of type 'module' is not iterable
But this code runs fine in datasets, I don't understand why
[src/datasets/utils/patching.py#L96](https://github.com/huggingface/datasets/blob/3.6.0/src/datasets/utils/patching.py#L96) | {
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https://api.github.com/repos/huggingface/datasets/issues/7635 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7635/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7635/comments | https://api.github.com/repos/huggingface/datasets/issues/7635/events | https://github.com/huggingface/datasets/pull/7635 | 3,170,486,408 | PR_kwDODunzps6bybOe | 7,635 | Fix: Preserve float columns in JSON loader when values are integer-like (e.g. 0.0, 1.0) | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-24T06:16:48Z | 2025-06-24T06:16:48Z | null | CONTRIBUTOR | null | null | null | This PR fixes a bug in the JSON loader where columns containing float values like `[0.0, 1.0, 2.0]` were being implicitly coerced to `int`, due to pandas or Arrow type inference.
This caused issues downstream in statistics computation (e.g., dataset-viewer) where such columns were incorrectly labeled as `"int"` instead of `"float"`.
### 🔍 What was happening:
When the JSON loader falls back to `pandas_read_json()` (after `pa.read_json()` fails), pandas/Arrow can coerce float values to integers if all values are integer-like (e.g., `0.0 == 0`).
### ✅ What this PR does:
- Adds a check in the fallback path of `_generate_tables()`
- Ensures that columns made entirely of floats are preserved as `"float64"` even if they are integer-like (e.g. `0.0`, `1.0`)
- This prevents loss of float semantics when creating the Arrow table
### 🧪 Reproducible Example:
```json
[{"col": 0.0}, {"col": 1.0}, {"col": 2.0}]
````
Previously loaded as:
* `int`
Now correctly loaded as:
* `float`
Fixes #6937
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https://api.github.com/repos/huggingface/datasets/issues/7634 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7634/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7634/comments | https://api.github.com/repos/huggingface/datasets/issues/7634/events | https://github.com/huggingface/datasets/pull/7634 | 3,169,389,653 | PR_kwDODunzps6buyij | 7,634 | Replace Sequence by List | {
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} | [] | closed | false | null | [] | null | 1 | 2025-06-23T20:35:48Z | 2025-06-25T13:59:13Z | 2025-06-25T13:59:11Z | MEMBER | null | null | null | Sequence is just a utility that we need to keep for backward compatibility. And `[ ]` was used instead but doesn't allow passing the length of the list.
This PR removes most mentions of Sequence and usage of `[ ]` and defines a proper List type instead.
before: `Sequence(Value("int64"))` or `[Value("int64")]`
now: `List(Value("int64"))`
This PR conserves full backward compatibility. And it's a good occasion with the release of 4.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7633 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7633/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7633/comments | https://api.github.com/repos/huggingface/datasets/issues/7633/events | https://github.com/huggingface/datasets/issues/7633 | 3,168,399,637 | I_kwDODunzps682fEV | 7,633 | Proposal: Small Tamil Discourse Coherence Dataset. | {
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} | I’m a beginner from NIT Srinagar proposing a dataset of 50 Tamil text pairs for discourse coherence (coherent/incoherent labels) to support NLP research in low-resource languages.
- Size: 50 samples
- Format: CSV with columns (text1, text2, label)
- Use case: Training NLP models for coherence
I’ll use GitHub’s web editor and Google Colab. Please confirm if this fits. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7632 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7632/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7632/comments | https://api.github.com/repos/huggingface/datasets/issues/7632/events | https://github.com/huggingface/datasets/issues/7632 | 3,168,283,589 | I_kwDODunzps682CvF | 7,632 | Graceful Error Handling for cast_column("image", Image(decode=True)) in Hugging Face Datasets | {
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] | open | false | null | [] | null | 0 | 2025-06-23T13:49:24Z | 2025-06-23T16:26:53Z | null | NONE | null | null | {
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} | ### Feature request
Currently, when using dataset.cast_column("image", Image(decode=True)), the pipeline throws an error and halts if any image in the dataset is invalid or corrupted (e.g., truncated files, incorrect formats, unreachable URLs). This behavior disrupts large-scale processing where a few faulty samples are common.
reference : https://discuss.huggingface.co/t/handle-errors-when-loading-images-404-corrupted-etc/50318/5
https://discuss.huggingface.co/t/handling-non-existing-url-in-image-dataset-while-cast-column/69185
Proposed Feature
Introduce a mechanism (e.g., a continue_on_error=True flag or global error handling mode) in Image(decode=True) that:
Skips invalid images and sets them as None, or
Logs the error but allows the rest of the dataset to be processed without interruption.
Example Usage
from datasets import load_dataset, Image
dataset = load_dataset("my_dataset")
dataset = dataset.cast_column("image", Image(decode=True, continue_on_error=True))
Benefits
Ensures robust large-scale image dataset processing.
Improves developer productivity by avoiding custom retry/error-handling code.
Aligns with best practices in dataset preprocessing pipelines that tolerate minor data corruption.
Potential Implementation Options
Internally wrap the decoding in a try/except block.
Return None or a placeholder on failure.
Optionally allow custom error callbacks or logging.
### Motivation
Robustness: Large-scale image datasets often contain a small fraction of corrupt files or unreachable URLs. Halting on the first error forces users to write custom workarounds or preprocess externally.
Simplicity: A built-in flag removes boilerplate try/except logic around every decode step.
Performance: Skipping invalid samples inline is more efficient than a two-pass approach (filter then decode).
### Your contribution
1. API Change
Extend datasets.features.Image(decode=True) to accept continue_on_error: bool = False.
2. Behavior
If continue_on_error=False (default), maintain current behavior: any decode error raises an exception.
If continue_on_error=True, wrap decode logic in try/except:
On success: store the decoded image.
On failure: log a warning (e.g., via logging.warning) and set the field to None (or a sentinel value).
3. Optional Enhancements
Allow a callback hook:
Image(decode=True, continue_on_error=True, on_error=lambda idx, url, exc: ...)
Emit metrics or counts of skipped images. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7631 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7631/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7631/comments | https://api.github.com/repos/huggingface/datasets/issues/7631/events | https://github.com/huggingface/datasets/pull/7631 | 3,165,127,657 | PR_kwDODunzps6bgwOB | 7,631 | Pass user-agent from DownloadConfig into fsspec storage_options | {
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} | [] | open | false | null | [] | null | 1 | 2025-06-21T14:22:25Z | 2025-06-21T14:25:28Z | null | CONTRIBUTOR | null | null | null | Fixes part of issue #6046
### Problem
The `user-agent` defined in `DownloadConfig` was not passed down to fsspec-based filesystems like `HfFileSystem`, which prevents proper identification/tracking of client requests.
### Solution
Added support for injecting the `user-agent` into `storage_options["headers"]` within `_prepare_single_hop_path_and_storage_options()` based on the `protocol`.
Now, when using `hf://`, `http://`, or `https://`, the custom user-agent is passed automatically.
### Code Location
Modified:
- `src/datasets/utils/file_utils.py`
Used `get_datasets_user_agent(...)` to ensure proper formatting and fallback logic. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7630 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7630/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7630/comments | https://api.github.com/repos/huggingface/datasets/issues/7630/events | https://github.com/huggingface/datasets/issues/7630 | 3,164,650,900 | I_kwDODunzps68oL2U | 7,630 | [bug] resume from ckpt skips samples if .map is applied | {
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} | [] | open | false | null | [] | null | 2 | 2025-06-21T01:50:03Z | 2025-06-29T07:51:32Z | null | NONE | null | null | {
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} | ### Describe the bug
resume from ckpt skips samples if .map is applied
Maybe related: https://github.com/huggingface/datasets/issues/7538
### Steps to reproduce the bug
```python
from datasets import Dataset
from datasets.distributed import split_dataset_by_node
# Create dataset with map transformation
def create_dataset():
ds = Dataset.from_dict({"id": list(range(100))})
ds = ds.to_iterable_dataset(num_shards=4)
ds = ds.map(lambda x: x) #comment it out to get desired behavior
ds = split_dataset_by_node(ds, rank=0, world_size=2)
return ds
ds = create_dataset()
# Iterate and save checkpoint after 10 samples
it = iter(ds)
for idx, sample in enumerate(it):
if idx == 9: # Checkpoint after 10 samples
checkpoint = ds.state_dict()
print(f"Checkpoint saved at sample: {sample['id']}")
break
# Continue with original iterator
original_next_samples = []
for idx, sample in enumerate(it):
original_next_samples.append(sample["id"])
if idx >= 4:
break
# Resume from checkpoint
ds_new = create_dataset()
ds_new.load_state_dict(checkpoint)
# Get samples from resumed iterator
it_new = iter(ds_new)
resumed_next_samples = []
for idx, sample in enumerate(it_new):
resumed_next_samples.append(sample["id"])
if idx >= 4:
break
print(f"\nExpected next samples: {original_next_samples}")
print(f"Actual next samples: {resumed_next_samples}")
print(
f"\n❌ BUG: {resumed_next_samples[0] - original_next_samples[0]} samples were skipped!"
)
```
With map
```
Checkpoint saved at sample: 9
Expected next samples: [10, 11, 12, 13, 14]
Actual next samples: [50, 51, 52, 53, 54]
❌ BUG: 40 samples were skipped!
```
### Expected behavior
without map
```
Expected next samples: [10, 11, 12, 13, 14]
Actual next samples: [10, 11, 12, 13, 14]
❌ BUG: 0 samples were skipped!
```
### Environment info
datasets == 3.6.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7629 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7629/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7629/comments | https://api.github.com/repos/huggingface/datasets/issues/7629/events | https://github.com/huggingface/datasets/pull/7629 | 3,161,169,782 | PR_kwDODunzps6bTc7b | 7,629 | Add test for `as_iterable_dataset()` method in DatasetBuilder | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-19T19:23:55Z | 2025-06-19T19:23:55Z | null | CONTRIBUTOR | null | null | null | This PR adds a test for the new `as_iterable_dataset()` method introduced in PR #7628.
The test:
- Loads a builder using `load_dataset_builder("c4", "en")`
- Runs `download_and_prepare()`
- Streams examples using `builder.as_iterable_dataset(split="train[:100]")`
- Verifies streamed examples contain the "text" field
This ensures that the builder correctly streams data from cached Arrow files.
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https://api.github.com/repos/huggingface/datasets/issues/7628 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7628/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7628/comments | https://api.github.com/repos/huggingface/datasets/issues/7628/events | https://github.com/huggingface/datasets/pull/7628 | 3,161,156,461 | PR_kwDODunzps6bTaGk | 7,628 | Add `as_iterable_dataset()` method to DatasetBuilder for streaming from cached Arrow files | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-19T19:15:41Z | 2025-06-19T19:15:41Z | null | CONTRIBUTOR | null | null | null | This PR implements `builder.as_iterable_dataset(split=...)` as discussed in #5481.
It allows users to load an `IterableDataset` directly from cached Arrow files (using ArrowReader and ArrowExamplesIterable), without loading the full dataset into memory.
This is useful for large-scale training scenarios where memory is constrained. A test has also been added in `test_builder.py`.
Related to: #5481
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https://api.github.com/repos/huggingface/datasets/issues/7627 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7627/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7627/comments | https://api.github.com/repos/huggingface/datasets/issues/7627/events | https://github.com/huggingface/datasets/issues/7627 | 3,160,544,390 | I_kwDODunzps68YhSG | 7,627 | Creating a HF Dataset from lakeFS with S3 storage takes too much time! | {
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} | [] | closed | false | null | [] | null | 1 | 2025-06-19T14:28:41Z | 2025-06-23T12:39:10Z | 2025-06-23T12:39:10Z | NONE | null | null | {
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} | Hi,
I’m new to HF dataset and I tried to create datasets based on data versioned in **lakeFS** _(**MinIO** S3 bucket as storage backend)_
Here I’m using ±30000 PIL image from MNIST data however it is taking around 12min to execute, which is a lot!
From what I understand, it is loading the images into cache then building the dataset.
– Please find bellow the execution screenshot –
Is there a way to optimize this or am I doing something wrong?
Thanks!
 | {
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https://api.github.com/repos/huggingface/datasets/issues/7626 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7626/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7626/comments | https://api.github.com/repos/huggingface/datasets/issues/7626/events | https://github.com/huggingface/datasets/pull/7626 | 3,159,322,138 | PR_kwDODunzps6bNMuF | 7,626 | feat(map): reuse unchanged columns when input_columns specified to reduce disk usage (#6013) | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-19T07:41:45Z | 2025-06-26T06:43:16Z | null | CONTRIBUTOR | null | null | null | ## Summary
This PR addresses [#6013](https://github.com/huggingface/datasets/issues/6013) by reusing unchanged columns from the original dataset in the `map()` method when `input_columns` is specified.
## What’s Implemented
- Injected logic at the end of `Dataset.map()` to:
- Identify untouched columns not in `input_columns` or `remove_columns`
- Select those columns from the original dataset
- Concatenate them with the transformed result using `pyarrow.concat_tables`
## Example Behavior
```python
ds = Dataset.from_dict({"a": [1, 2], "b": [3, 4]})
ds2 = ds.map(lambda x: {"c": x["a"] + 10}, input_columns=["a"], remove_columns=["a"])
print(ds2.column_names) # Output: ['b', 'c']
````
Column `b` is reused from the original dataset.
## Notes
* This keeps disk usage and caching minimal by avoiding full dataset duplication.
* Only triggered when `input_columns` is set.
---
cc @lhoestq @mariosasko for review 🙂
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https://api.github.com/repos/huggingface/datasets/issues/7625 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7625/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7625/comments | https://api.github.com/repos/huggingface/datasets/issues/7625/events | https://github.com/huggingface/datasets/pull/7625 | 3,159,016,001 | PR_kwDODunzps6bMKof | 7,625 | feat: Add h5folder dataset loader for HDF5 support | {
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} | [] | open | false | null | [] | null | 3 | 2025-06-19T05:39:10Z | 2025-06-26T05:44:26Z | null | CONTRIBUTOR | null | null | null | ### Related Issue
Closes #3113
### What does this PR do?
This PR introduces a new dataset loader module called **`h5folder`** to support loading datasets stored in **HDF5 (.h5)** format.
It allows users to do:
```python
from datasets import load_dataset
dataset = load_dataset("h5folder", data_dir="path/to/")
````
### 🧩 Design Overview
* Implemented inside `datasets/packaged_modules/h5folder/h5folder.py`
* Based on the `GeneratorBasedBuilder` API
* Uses `h5py` to read HDF5 files and yield examples
* Expects datasets such as `id`, `data`, and `label` inside `data.h5`
* Converts numpy arrays to Python types before yielding
### 🧪 Example `.h5` Structure (for local testing)
```python
import h5py
import numpy as np
with h5py.File("data.h5", "w") as f:
f.create_dataset("id", data=np.arange(100))
f.create_dataset("data", data=np.random.randn(100, 10))
f.create_dataset("label", data=np.random.randint(0, 2, size=100))
```
### ✅ Testing
- The loader logic follows the structure of existing modules like `imagefolder`
- Will rely on Hugging Face CI to validate integration
- Manually testing planned once merged or during feedback
### 📁 Files Added
* `datasets/src/datasets/packaged_modules/h5folder/h5folder.py`
### 📌 Component(s) Affected
* `area/datasets`
* `area/load`
### 📦 Release Note Classification
* `rn/feature` – Adds support for loading `.h5` datasets via `load_dataset("h5folder", ...)`
---
Let me know if any changes or improvements are needed — happy to iterate. Thanks for reviewing!
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https://api.github.com/repos/huggingface/datasets/issues/7624 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7624/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7624/comments | https://api.github.com/repos/huggingface/datasets/issues/7624/events | https://github.com/huggingface/datasets/issues/7624 | 3,156,136,624 | I_kwDODunzps68HtKw | 7,624 | #Dataset Make "image" column appear first in dataset preview UI | {
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} | [] | closed | false | null | [] | null | 2 | 2025-06-18T09:25:19Z | 2025-06-20T07:46:43Z | 2025-06-20T07:46:43Z | NONE | null | null | {
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#Dataset
I’m currently uploading a dataset that includes an `"image"` column (PNG files), along with some metadata columns. The dataset is loaded from a .jsonl file. My goal is to have the "image" column appear as the first column in the dataset card preview UI on the :hugs: Hub.
However, at the moment, the `"image"` column is not the first—in fact, it appears last, which is not ideal for the presentation I’d like to achieve.
I have a couple of questions:
Is there a way to force the dataset card to display the `"image"` column first?
Is there currently any way to control or influence the column order in the dataset preview UI?
Does the order of keys in the .jsonl file or the features argument affect the display order?
Thanks again for your time and help! :blush: | {
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} | [] | closed | false | null | [] | null | 2 | 2025-06-17T19:16:34Z | 2025-06-18T14:18:41Z | 2025-06-18T14:18:41Z | CONTRIBUTOR | null | null | null | ### Related Issues/PRs
Fixes #6152
---
### What changes are proposed in this pull request?
This PR adds a dedicated validation check in the `_info()` method of the `FolderBasedBuilder` class to ensure that users provide either `data_dir` or `data_files` when loading folder-based datasets (such as `audiofolder`, `imagefolder`, etc.).
---
### Why this change?
Previously, when calling:
```python
load_dataset("audiofolder")
````
without specifying `data_dir` or `data_files`, the loader would silently fallback to the **current working directory**, leading to:
* Long loading times
* Unexpected behavior (e.g., scanning unrelated files)
This behavior was discussed in issue #6152. As suggested by maintainers, the fix has now been implemented directly inside the `FolderBasedBuilder._info()` method — keeping the logic localized to the specific builder instead of a generic loader function.
---
### How is this PR tested?
* ✅ Manually tested by calling `load_dataset("audiofolder")` with no `data_dir` or `data_files` → a `ValueError` is now raised early.
* ✅ Existing functionality (with valid input) remains unaffected.
---
### Does this PR require documentation update?
* [x] No
---
### Release Notes
#### Is this a user-facing change?
* [x] Yes
> Folder-based datasets now raise an explicit error if neither `data_dir` nor `data_files` are specified, preventing unintended fallback to the current working directory.
---
#### What component(s) does this PR affect?
* [x] `area/datasets`
* [x] `area/load`
---
<a name="release-note-category"></a>
#### How should the PR be classified?
* [x] `rn/bug-fix` - A user-facing bug fix
---
#### Should this be included in the next patch release?
* [x] Yes | {
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} | [] | open | false | null | [] | null | 1 | 2025-06-17T18:28:35Z | 2025-07-02T12:39:20Z | null | NONE | null | null | null | …builder (#4910 )
### What does this PR do?
Fixes edge case in `load_dataset_builder` by raising a `TypeError` if the same key exists in both `builder_kwargs` and `config_kwargs`.
### Implementation details
- Added a guard clause in `load_dataset_builder` to detect duplicate keys between `builder_kwargs` and `config_kwargs`
- Wrote a unit test in `tests/test_load_duplicate_keys.py` to verify the exception is raised correctly
### Fixes
Closes #4910
### Reviewers
@zach-huggingface
@SunMarc
Would appreciate your review if you have time - thanks! | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7620 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7620/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7620/comments | https://api.github.com/repos/huggingface/datasets/issues/7620/events | https://github.com/huggingface/datasets/pull/7620 | 3,153,565,183 | PR_kwDODunzps6a58TP | 7,620 | Fixes in docs | {
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} | [] | closed | false | null | [] | null | 1 | 2025-06-17T13:41:54Z | 2025-06-17T13:58:26Z | 2025-06-17T13:58:24Z | MEMBER | null | null | null | before release 4.0
(I also did minor improvements to `features` to not show their `id=None` in their `__repr__()`) | {
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https://api.github.com/repos/huggingface/datasets/issues/7619 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7619/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7619/comments | https://api.github.com/repos/huggingface/datasets/issues/7619/events | https://github.com/huggingface/datasets/issues/7619 | 3,153,058,517 | I_kwDODunzps6779rV | 7,619 | `from_list` fails while `from_generator` works for large datasets | {
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} | [] | open | false | null | [] | null | 4 | 2025-06-17T10:58:55Z | 2025-06-29T16:34:44Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I am constructing a large time series dataset and observed that first constructing a list of entries and then using `Dataset.from_list` led to a crash as the number of items became large. However, this is not a problem when using `Dataset.from_generator`.
### Steps to reproduce the bug
#### Snippet A (crashes)
```py
from tqdm.auto import tqdm
import numpy as np
import datasets
def data_generator():
for i in tqdm(range(10_000_000)):
length = np.random.randint(2048)
series = np.random.rand(length)
yield {"target": series, "item_id": str(i), "start": np.datetime64("2000", "ms")}
data_list = list(data_generator())
ds = datasets.Dataset.from_list(data_list)
```
The last line crashes with
```
ArrowInvalid: Value 2147483761 too large to fit in C integer type
```
#### Snippet B (works)
```py
from tqdm.auto import tqdm
import numpy as np
import datasets
def data_generator():
for i in tqdm(range(10_000_000)):
length = np.random.randint(2048)
series = np.random.rand(length)
yield {"target": series, "item_id": str(i), "start": np.datetime64("2000", "ms")}
ds = datasets.Dataset.from_generator(data_generator)
```
### Expected behavior
I expected both the approaches to work or to fail similarly.
### Environment info
```
- `datasets` version: 3.6.0
- Platform: Linux-6.8.0-1029-aws-x86_64-with-glibc2.35
- Python version: 3.11.11
- `huggingface_hub` version: 0.32.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7618/comments | https://api.github.com/repos/huggingface/datasets/issues/7618/events | https://github.com/huggingface/datasets/pull/7618 | 3,148,912,897 | PR_kwDODunzps6aqOnm | 7,618 | fix: raise error when folder-based datasets are loaded without data_dir or data_files | {
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### Related Issues/PRs
<!-- Uncomment 'Resolve' if this PR can close the linked items. -->
<!-- Resolve --> #6152
---
### What changes are proposed in this pull request?
This PR adds an early validation step for folder-based datasets (like `audiofolder`) to prevent silent fallback behavior.
**Before this fix**:
- When `data_dir` or `data_files` were not provided, the loader defaulted to the current working directory.
- This caused unexpected behavior like:
- Long loading times
- Scanning unintended local files
**Now**:
- If both `data_dir` and `data_files` are missing, a `ValueError` is raised early with a helpful message.
---
### How is this PR tested?
- [x] Manual test via `load_dataset("audiofolder")` with missing `data_dir`
- [ ] Existing unit tests (should not break any)
- [ ] New tests (if needed, maintainers can guide)
---
### Does this PR require documentation update?
- [x] No. You can skip the rest of this section.
---
### Release Notes
#### Is this a user-facing change?
- [x] Yes. Give a description of this change to be included in the release notes for users.
> Adds early error handling for folder-based datasets when neither `data_dir` nor `data_files` is specified, avoiding unintended resolution to the current directory.
#### What component(s), interfaces, languages, and integrations does this PR affect?
Components:
- [x] `area/datasets`
- [x] `area/load`
---
<a name="release-note-category"></a>
#### How should the PR be classified in the release notes? Choose one:
- [x] `rn/bug-fix` - A user-facing bug fix worth mentioning in the release notes
---
#### Should this PR be included in the next patch release?
- [x] Yes (this PR will be cherry-picked and included in the next patch release)
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https://api.github.com/repos/huggingface/datasets/issues/7617 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7617/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7617/comments | https://api.github.com/repos/huggingface/datasets/issues/7617/events | https://github.com/huggingface/datasets/issues/7617 | 3,148,102,085 | I_kwDODunzps67pDnF | 7,617 | Unwanted column padding in nested lists of dicts | {
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} | [] | closed | false | null | [] | null | 1 | 2025-06-15T22:06:17Z | 2025-06-16T13:43:31Z | 2025-06-16T13:43:31Z | MEMBER | null | null | {
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} | ```python
from datasets import Dataset
dataset = Dataset.from_dict({
"messages": [
[
{"a": "...",},
{"b": "...",},
],
]
})
print(dataset[0])
```
What I get:
```
{'messages': [{'a': '...', 'b': None}, {'a': None, 'b': '...'}]}
```
What I want:
```
{'messages': [{'a': '...'}, {'b': '...'}]}
```
Is there an easy way to automatically remove these auto-filled null/none values?
If not, I probably need a recursive none exclusion function, don't I?
Datasets 3.6.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7616 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7616/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7616/comments | https://api.github.com/repos/huggingface/datasets/issues/7616/events | https://github.com/huggingface/datasets/pull/7616 | 3,144,506,665 | PR_kwDODunzps6acSW7 | 7,616 | Torchcodec decoding | {
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} | [] | closed | false | null | [] | null | 5 | 2025-06-13T19:06:07Z | 2025-06-19T18:25:49Z | 2025-06-19T18:25:49Z | CONTRIBUTOR | null | null | null | Closes #7607
## New signatures
### Audio
```python
Audio(sampling_rate: Optional[int] = None, mono: bool = True, decode: bool = True, stream_index: Optional[int] = None)
Audio.encode_example(self, value: Union[str, bytes, bytearray, dict, "AudioDecoder"]) -> dict
Audio.decode_example(self, value: dict, token_per_repo_id: Optional[dict[str, Union[str, bool, None]]] = None) -> "AudioDecoder":
```
### Video
```python
Video(decode: bool = True, stream_index: Optional[int] = None, dimension_order: Literal['NCHW', 'NHWC'] = 'NCHW', num_ffmpeg_threads: int = 1, device: Optional[Union[str, "torch.device"]] = 'cpu', seek_mode: Literal['exact', 'approximate'] = 'exact')
Video.encode_example(self, value: Union[str, bytes, bytearray, Example, np.ndarray, "VideoDecoder"]) -> Example:
Video.decode_example(self, value: Union[str, Example], token_per_repo_id: Optional[dict[str, Union[bool, str]]] = None, ) -> "VideoDecoder":
```
## Notes
Audio features constructor takes in 1 new optional param stream_index which is passed to the AudioDecoder constructor to select the stream index of a file.
Audio feature can now take in torchcodec.decoders.AudioDecoder as input to encode_example()
Audio feature decode_example() returns torchcodec.decoders.AudioDecoder
Video feature constructor takes in 5 new optional params stream_index, dimension_order, num_ffmpeg_threads, device, seek_mode all of which are passed to VideoDecoder constructor
Video feature decode_example() returns torchcodec.decoders.VideoDecoder
Video feature can now take in torchcodec.decoders.VideoDecoder as input to encode_example()
All test cases have been updated to reflect these changes
All documentation has also been updated to reflect these changes.
Both VideoDecoder and AudioDecoder when formatted with (np_formatter, tf_formatter, etc) will ignore the type and return themselves. Formatting test cases were updated accordingly to reflect this. (Pretty simple to make this not the case if we want though)
## Errors
This test case from `tests/packaged_modules/test_audiofolder.py`
```python
@require_librosa
@require_sndfile
@pytest.mark.parametrize("streaming", [False, True])
def test_data_files_with_metadata_and_archives(streaming, cache_dir, data_files_with_zip_archives):
audiofolder = AudioFolder(data_files=data_files_with_zip_archives, cache_dir=cache_dir)
audiofolder.download_and_prepare()
datasets = audiofolder.as_streaming_dataset() if streaming else audiofolder.as_dataset()
for split, data_files in data_files_with_zip_archives.items():
num_of_archives = len(data_files) # the metadata file is inside the archive
expected_num_of_audios = 2 * num_of_archives
assert split in datasets
dataset = list(datasets[split])
assert len(dataset) == expected_num_of_audios
# make sure each sample has its own audio (all arrays are different) and metadata
assert (
sum(np.array_equal(dataset[0]["audio"].get_all_samples().data.numpy(), example["audio"].get_all_samples().data.numpy()) for example in dataset[1:])
== 0
)
assert len({example["text"] for example in dataset}) == expected_num_of_audios
assert all(example["text"] is not None for example in dataset)
```
Fails now because AudioDecoder needs to access the files after the lines below are run, but there seems to be some context issues. The file the decoder is trying to read is closed before the decoder gets the chance to decode it.
```python
audiofolder.download_and_prepare()
datasets = audiofolder.as_streaming_dataset() if streaming else audiofolder.as_dataset()
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7615/comments | https://api.github.com/repos/huggingface/datasets/issues/7615/events | https://github.com/huggingface/datasets/pull/7615 | 3,143,443,498 | PR_kwDODunzps6aYp18 | 7,615 | remove unused code | {
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https://api.github.com/repos/huggingface/datasets/issues/7614 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7614/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7614/comments | https://api.github.com/repos/huggingface/datasets/issues/7614/events | https://github.com/huggingface/datasets/pull/7614 | 3,143,381,638 | PR_kwDODunzps6aYcbH | 7,614 | Lazy column | {
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e.g. `ds[col]` now returns a lazy Column instead of a list
This way calling `ds[col][idx]` only loads the required data in memory
(bonus: also supports subfields access with `ds[col][subcol][idx]`)
the breaking change will be for the next major release, which also includes removal of dataset scripts support
close https://github.com/huggingface/datasets/issues/4180 | {
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https://api.github.com/repos/huggingface/datasets/issues/7613 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7613/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7613/comments | https://api.github.com/repos/huggingface/datasets/issues/7613/events | https://github.com/huggingface/datasets/pull/7613 | 3,142,819,991 | PR_kwDODunzps6aWgr3 | 7,613 | fix parallel push_to_hub in dataset_dict | {
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https://api.github.com/repos/huggingface/datasets/issues/7612 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7612/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7612/comments | https://api.github.com/repos/huggingface/datasets/issues/7612/events | https://github.com/huggingface/datasets/issues/7612 | 3,141,905,049 | I_kwDODunzps67RaqZ | 7,612 | Provide an option of robust dataset iterator with error handling | {
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} | ### Feature request
Adding an option to skip corrupted data samples. Currently the datasets behavior is throwing errors if the data sample if corrupted and let user aware and handle the data corruption. When I tried to try-catch the error at user level, the iterator will raise StopIteration when I called next() again.
The way I try to do error handling is: (This doesn't work, unfortunately)
```
# Load the dataset with streaming enabled
dataset = load_dataset(
"pixparse/cc12m-wds", split="train", streaming=True
)
# Get an iterator from the dataset
iterator = iter(dataset)
while True:
try:
# Try to get the next example
example = next(iterator)
# Try to access and process the image
image = example["jpg"]
pil_image = Image.fromarray(np.array(image))
pil_image.verify() # Verify it's a valid image file
except StopIteration: # Code path 1
print("\nStopIteration was raised! Reach the end of dataset")
raise StopIteration
except Exception as e: # Code path 2
errors += 1
print("Error! Skip this sample")
cotinue
else:
successful += 1
```
This is because the `IterableDataset` already throws an error (reaches Code path 2). And if I continue call next(), it will hit Code path 1. This is because the inner iterator of `IterableDataset`([code](https://github.com/huggingface/datasets/blob/89bd1f971402acb62805ef110bc1059c38b1c8c6/src/datasets/iterable_dataset.py#L2242)) as been stopped, so calling next() on it will raise StopIteration.
So I can not skip the corrupted data sample in this way. Would also love to hear any suggestions about creating a robust dataloader.
Thanks for your help in advance!
### Motivation
## Public dataset corruption might be common
A lot of users would use public dataset, and the public dataset might contains some corrupted data, especially for dataset with image / video etc. I totally understand it's dataset owner and user's responsibility to ensure the data integrity / run data cleaning or preprocessing, but it would be easier for developers who would use the dataset
## Use cases
For example, a robust dataloader would be easy for users who want to try quick tests on different dataset, and chose one dataset which fits their needs. So user could use IterableDataloader with `stream=True` to use the dataset easily without downloading and removing corrupted data samples from the dataset.
### Your contribution
The error handling might not trivial and might need more careful design. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7611/comments | https://api.github.com/repos/huggingface/datasets/issues/7611/events | https://github.com/huggingface/datasets/issues/7611 | 3,141,383,940 | I_kwDODunzps67PbcE | 7,611 | Code example for dataset.add_column() does not reflect correct way to use function | {
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The example seems to suggest that dataset.add_column() can add column inplace, however, this is wrong -- it cannot. It returns a new dataset with the column added to it. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7610 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7610/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7610/comments | https://api.github.com/repos/huggingface/datasets/issues/7610/events | https://github.com/huggingface/datasets/issues/7610 | 3,141,281,560 | I_kwDODunzps67PCcY | 7,610 | i cant confirm email | {
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} | ### Describe the bug
This is dificult, I cant confirm email because I'm not get any email!
I cant post forum because I cant confirm email!
I can send help desk because... no exist on web page.
paragraph 44
### Steps to reproduce the bug
rthjrtrt
### Expected behavior
ewtgfwetgf
### Environment info
sdgfswdegfwe | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7609 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7609/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7609/comments | https://api.github.com/repos/huggingface/datasets/issues/7609/events | https://github.com/huggingface/datasets/pull/7609 | 3,140,373,128 | PR_kwDODunzps6aOQ_g | 7,609 | Update `_dill.py` to use `co_linetable` for Python 3.10+ in place of `co_lnotab` | {
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```
/fsx/qgallouedec/miniconda3/envs/trl/lib/python3.12/site-packages/datasets/utils/_dill.py:385: DeprecationWarning: co_lnotab is deprecated, use co_lines instead.
```
Tests pass locally. And the warning is gone with this change.
https://peps.python.org/pep-0626/#backwards-compatibility | {
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https://api.github.com/repos/huggingface/datasets/issues/7608 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7608/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7608/comments | https://api.github.com/repos/huggingface/datasets/issues/7608/events | https://github.com/huggingface/datasets/pull/7608 | 3,137,564,259 | PR_kwDODunzps6aEr6b | 7,608 | Tests typing and fixes for push_to_hub | {
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- [x] fix TestPushToHub.test_push_dataset_dict_to_hub_iterable_num_proc | {
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https://api.github.com/repos/huggingface/datasets/issues/7607 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7607/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7607/comments | https://api.github.com/repos/huggingface/datasets/issues/7607/events | https://github.com/huggingface/datasets/issues/7607 | 3,135,722,560 | I_kwDODunzps6651RA | 7,607 | Video and audio decoding with torchcodec | {
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"completed": 0,
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} | ### Feature request
Pytorch is migrating video processing to torchcodec and it's pretty cool. It would be nice to migrate both the audio and video features to use torchcodec instead of torchaudio/video.
### Motivation
My use case is I'm working on a multimodal AV model, and what's nice about torchcodec is I can extract the audio tensors directly from MP4 files. Also, I can easily resample video data to whatever fps I like on the fly. I haven't found an easy/efficient way to do this with torchvision.
### Your contribution
I’m modifying the Video dataclass to use torchcodec in place of the current backend, starting from a stable commit for a project I’m working on. If it ends up working well, I’m happy to open a PR on main. | {
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https://api.github.com/repos/huggingface/datasets/issues/7605 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7605/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7605/comments | https://api.github.com/repos/huggingface/datasets/issues/7605/events | https://github.com/huggingface/datasets/pull/7605 | 3,131,636,882 | PR_kwDODunzps6ZwcPp | 7,605 | Make `push_to_hub` atomic (#7600) | {
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https://api.github.com/repos/huggingface/datasets/issues/7604 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7604/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7604/comments | https://api.github.com/repos/huggingface/datasets/issues/7604/events | https://github.com/huggingface/datasets/pull/7604 | 3,130,837,169 | PR_kwDODunzps6Ztrm_ | 7,604 | Docs and more methods for IterableDataset: push_to_hub, to_parquet... | {
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https://api.github.com/repos/huggingface/datasets/issues/7602 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7602/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7602/comments | https://api.github.com/repos/huggingface/datasets/issues/7602/events | https://github.com/huggingface/datasets/pull/7602 | 3,128,758,924 | PR_kwDODunzps6Zmk99 | 7,602 | Enhance error handling and input validation across multiple modules | {
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} | [] | open | false | null | [] | null | 0 | 2025-06-08T23:01:06Z | 2025-06-08T23:01:06Z | null | NONE | null | null | null | This PR improves the robustness and user experience by:
1. **Audio Module**:
- Added clear error messages when required fields ('path' or 'bytes') are missing in audio encoding
2. **DatasetDict**:
- Enhanced key access error messages to show available splits when an invalid key is accessed
3. **NonMutableDict**:
- Added input validation for the update() method to ensure proper mapping types
4. **Arrow Reader**:
- Improved error messages for small dataset percentage splits with suggestions for alternatives
5. **FaissIndex**:
- Strengthened input validation with descriptive error messages
- Added proper type checking and shape validation for search queries
These changes make the code more maintainable and user-friendly by providing actionable feedback when issues arise. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7600 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7600/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7600/comments | https://api.github.com/repos/huggingface/datasets/issues/7600/events | https://github.com/huggingface/datasets/issues/7600 | 3,127,296,182 | I_kwDODunzps66ZsC2 | 7,600 | `push_to_hub` is not concurrency safe (dataset schema corruption) | {
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} | ### Describe the bug
Concurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.
Consider this scenario:
- we have an Arrow dataset
- there are `N` configs of the dataset
- there are `N` independent processes operating on each of the individual configs (e.g. adding a column, `new_col`)
- each process calls `push_to_hub` on their particular config when they're done processing
- all calls to `push_to_hub` succeed
- the `README.md` now has some configs with `new_col` added and some with `new_col` missing
Any attempt to load a config (using `load_dataset`) where `new_col` is missing will fail because of a schema mismatch between `README.md` and the Arrow files. Fixing the dataset requires updating `README.md` by hand with the correct schema for the affected config. In effect, `push_to_hub` is doing a `git push --force` (I found this behavior quite surprising).
We have hit this issue every time we run processing jobs over our datasets and have to fix corrupted schemas by hand.
Reading through the code, it seems that specifying a [`parent_commit`](https://github.com/huggingface/huggingface_hub/blob/v0.32.4/src/huggingface_hub/hf_api.py#L4587) hash around here https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L5794 would get us to a normal, non-forced git push, and avoid schema corruption. I'm not familiar enough with the code to know how to determine the commit hash from which the in-memory dataset card was loaded.
### Steps to reproduce the bug
See above.
### Expected behavior
Concurrent edits to disjoint configs of a dataset should never corrupt the dataset schema.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-118-generic-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.2
- `fsspec` version: 2023.9.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7599 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7599/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7599/comments | https://api.github.com/repos/huggingface/datasets/issues/7599/events | https://github.com/huggingface/datasets/issues/7599 | 3,125,620,119 | I_kwDODunzps66TS2X | 7,599 | My already working dataset (when uploaded few months ago) now is ignoring metadata.jsonl | {
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} | [] | closed | false | null | [] | null | 3 | 2025-06-06T18:59:00Z | 2025-06-16T15:18:00Z | 2025-06-16T15:18:00Z | NONE | null | null | {
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} | ### Describe the bug
Hi everyone, I uploaded my dataset https://huggingface.co/datasets/PRAIG/SMB a few months ago while I was waiting for a conference acceptance response. Without modifying anything in the dataset repository now the Dataset viewer is not rendering the metadata.jsonl annotations, neither it is being downloaded when using load_dataset. Can you please help? Thank you in advance.
### Steps to reproduce the bug
from datasets import load_dataset
ds = load_dataset("PRAIG/SMB")
ds = ds["train"]
### Expected behavior
It is expected to have all the metadata available in the jsonl file. Fields like: "score_id", "original_width", "original_height", "regions"... among others.
### Environment info
datasets==3.6.0, python 3.13.3 (but he problem is already in the huggingface dataset page) | {
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https://api.github.com/repos/huggingface/datasets/issues/7597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7597/comments | https://api.github.com/repos/huggingface/datasets/issues/7597/events | https://github.com/huggingface/datasets/issues/7597 | 3,123,962,709 | I_kwDODunzps66M-NV | 7,597 | Download datasets from a private hub in 2025 | {
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} | ### Feature request
In the context of a private hub deployment, customers would like to use load_dataset() to load datasets from their hub, not from the public hub. This doesn't seem to be configurable at the moment and it would be nice to add this feature.
The obvious workaround is to clone the repo first and then load it from local storage, but this adds an extra step. It'd be great to have the same experience regardless of where the hub is hosted.
This issue was raised before here: https://github.com/huggingface/datasets/issues/3679
@juliensimon
### Motivation
none
### Your contribution
none | {
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2. Extended section about video image augmentations with an example from Albumentations. | {
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This is to be improved later.
Close https://github.com/huggingface/datasets/issues/5665
PS: for image/audio datasets structured as actual image/audio files (not parquet), you can sometimes speed it up with `ds.decode(num_threads=...).push_to_hub(...)` | {
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https://api.github.com/repos/huggingface/datasets/issues/7594 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7594/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7594/comments | https://api.github.com/repos/huggingface/datasets/issues/7594/events | https://github.com/huggingface/datasets/issues/7594 | 3,120,799,626 | I_kwDODunzps66A5-K | 7,594 | Add option to ignore keys/columns when loading a dataset from jsonl(or any other data format) | {
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} | ### Feature request
Hi, I would like the option to ignore keys/columns when loading a dataset from files (e.g. jsonl).
### Motivation
I am working on a dataset which is built on jsonl. It seems the dataset is unclean and a column has different types in each row. I can't clean this or remove the column (It is not my data and it is too big for me to clean and save on my own hardware).
I would like the option to just ignore this column when using `load_dataset`, since i don't need it.
I tried to look if this is already possible but couldn't find a solution. if there is I would love some help. If it is not currently possible, I would love this feature
### Your contribution
I don't think I can help this time, unfortunately. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7593 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7593/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7593/comments | https://api.github.com/repos/huggingface/datasets/issues/7593/events | https://github.com/huggingface/datasets/pull/7593 | 3,118,812,368 | PR_kwDODunzps6ZE34G | 7,593 | Fix broken link to albumentations | {
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In this PR fixed link to the most recent doc in Albumentations about bounding boxes and it's format.
Fix a few typos in the doc as well. | {
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- [x] remplace fixtures based on script with no-script fixtures
- [x] windaube | {
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} | ### Feature request
A number of processes parameter to the dataset.push_to_hub method
### Motivation
Shards are currently uploaded serially which makes it slow for many shards, uploading can be done in parallel and much faster
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} | ### Description
When loading a dataset with a field declared as a list of structs using `Sequence(Features(...))`, `load_dataset` incorrectly infers the field as a plain `struct<...>` instead of a `list<struct<...>>`. This leads to the following error:
```
ArrowNotImplementedError: Unsupported cast from list<item: struct<id: string, data: string>> to struct using function cast_struct
```
This occurs even when the `features` schema is explicitly provided and the dataset format supports nested structures natively (e.g., JSON, JSONL).
---
### Minimal Reproduction
[Colab Link.](https://colab.research.google.com/drive/1FZPQy6TP3jVd4B3mYKyfQaWNuOAvljUq?usp=sharing)
#### Dataset
```python
data = [
{
"list": [
{"id": "example1", "data": "text"},
]
},
]
```
#### Schema
```python
from datasets import Features, Sequence, Value
item = Features({
"id": Value("string"),
"data": Value("string"),
})
features = Features({
"list": Sequence(item),
})
```
---
### Tested File Formats
The same schema was tested across different formats:
| Format | Method | Result |
| --------- | --------------------------- | ------------------- |
| JSONL | `load_dataset("json", ...)` | Arrow cast error |
| JSON | `load_dataset("json", ...)` | Arrow cast error |
| In-memory | `Dataset.from_list(...)` | Works as expected |
The issue seems not to be in the schema or the data, but in how `load_dataset()` handles the `Sequence(Features(...))` pattern when parsing from files (specifically JSON and JSONL).
---
### Expected Behavior
If `features` is explicitly defined as:
```python
Features({"list": Sequence(Features({...}))})
```
Then the data should load correctly across all backends — including from JSON and JSONL — without any Arrow casting errors. This works correctly when loading from memory via `Dataset.from_list`.
---
### Environment
* `datasets`: 3.6.0
* `pyarrow`: 20.0.0
* Python: 3.12.10
* OS: Ubuntu 24.04.2 LTS
* Notebook: \[Colab test notebook available]
---
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https://api.github.com/repos/huggingface/datasets/issues/7589 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7589/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7589/comments | https://api.github.com/repos/huggingface/datasets/issues/7589/events | https://github.com/huggingface/datasets/pull/7589 | 3,101,119,704 | PR_kwDODunzps6YKiyL | 7,589 | feat: use content defined chunking | {
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} | [] | open | false | null | [] | null | 2 | 2025-05-29T18:19:41Z | 2025-06-17T15:04:07Z | null | COLLABORATOR | null | null | null | WIP:
- [x] set the parameters in `io.parquet.ParquetDatasetReader`
- [x] set the parameters in `arrow_writer.ParquetWriter`
It requires a new pyarrow pin ">=21.0.0" which is not yet released. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7588 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7588/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7588/comments | https://api.github.com/repos/huggingface/datasets/issues/7588/events | https://github.com/huggingface/datasets/issues/7588 | 3,094,012,025 | I_kwDODunzps64auB5 | 7,588 | ValueError: Invalid pattern: '**' can only be an entire path component [Colab] | {
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} | ### Describe the bug
I have a dataset on HF [here](https://huggingface.co/datasets/kambale/luganda-english-parallel-corpus) that i've previously used to train a translation model [here](https://huggingface.co/kambale/pearl-11m-translate).
now i changed a few hyperparameters to increase number of tokens for the model, increase Transformer layers, and all
however, when i try to load the dataset, this error keeps coming up.. i have tried everything.. i have re-written the code a hundred times, and this keep coming up
### Steps to reproduce the bug
Imports:
```bash
!pip install datasets huggingface_hub fsspec
```
Python code:
```python
from datasets import load_dataset
HF_DATASET_NAME = "kambale/luganda-english-parallel-corpus"
# Load the dataset
try:
if not HF_DATASET_NAME or HF_DATASET_NAME == "YOUR_HF_DATASET_NAME":
raise ValueError(
"Please provide a valid Hugging Face dataset name."
)
dataset = load_dataset(HF_DATASET_NAME)
# Omitted code as the error happens on the line above
except ValueError as ve:
print(f"Configuration Error: {ve}")
raise
except Exception as e:
print(f"An error occurred while loading the dataset '{HF_DATASET_NAME}': {e}")
raise e
```
now, i have tried going through this [issue](https://github.com/huggingface/datasets/issues/6737) and nothing helps
### Expected behavior
loading the dataset successfully and perform splits (train, test, validation)
### Environment info
from the imports, i do not install specific versions of these libraries, so the latest or available version is installed
* `datasets` version: latest
* `Platform`: Google Colab
* `Hardware`: NVIDIA A100 GPU
* `Python` version: latest
* `huggingface_hub` version: latest
* `fsspec` version: latest | {
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] | open | false | null | [] | null | 1 | 2025-05-26T14:00:42Z | 2025-05-26T18:21:57Z | null | NONE | null | null | {
"completed": 0,
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} | ### Feature request
https://github.com/rajasekarnp1/neural-audio-upscaler/tree/main
### Motivation
ai model develpment and audio
### Your contribution
ai model develpment and audio | null | {
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Currently, when calling `push_to_hub(set_default=True` with 2 different config names, both are set as default.
Moreover, this will generate an error next time we try to push another default config name, raised by `MetadataConfigs.get_default_config_name`:
https://github.com/huggingface/datasets/blob/da1db8a5b89fc0badaa0f571b36e122e52ae8c61/src/datasets/arrow_dataset.py#L5757
https://github.com/huggingface/datasets/blob/da1db8a5b89fc0badaa0f571b36e122e52ae8c61/src/datasets/utils/metadata.py#L186-L188 | {
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https://api.github.com/repos/huggingface/datasets/issues/7584 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7584/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7584/comments | https://api.github.com/repos/huggingface/datasets/issues/7584/events | https://github.com/huggingface/datasets/issues/7584 | 3,090,255,023 | I_kwDODunzps64MYyv | 7,584 | Add LMDB format support | {
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"completed": 0,
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} | ### Feature request
Add LMDB format support for large memory-mapping files
### Motivation
Add LMDB format support for large memory-mapping files
### Your contribution
I'm trying to add it | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7583 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7583/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7583/comments | https://api.github.com/repos/huggingface/datasets/issues/7583/events | https://github.com/huggingface/datasets/issues/7583 | 3,088,987,757 | I_kwDODunzps64HjZt | 7,583 | load_dataset type stubs reject List[str] for split parameter, but runtime supports it | {
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} | ### Describe the bug
The [load_dataset](https://huggingface.co/docs/datasets/v3.6.0/en/package_reference/loading_methods#datasets.load_dataset) method accepts a `List[str]` as the split parameter at runtime, however, the current type stubs restrict the split parameter to `Union[str, Split, None]`. This causes type checkers like Pylance to raise `reportArgumentType` errors when passing a list of strings, even though it works as intended at runtime.
### Steps to reproduce the bug
1. Use load_dataset with multiple splits e.g.:
```
from datasets import load_dataset
ds_train, ds_val, ds_test = load_dataset(
"Silly-Machine/TuPyE-Dataset",
"binary",
split=["train[:75%]", "train[75%:]", "test"]
)
```
2. Observe that code executes correctly at runtime and Pylance raises `Argument of type "List[str]" cannot be assigned to parameter "split" of type "str | Split | None"`
### Expected behavior
The type stubs for [load_dataset](https://huggingface.co/docs/datasets/v3.6.0/en/package_reference/loading_methods#datasets.load_dataset) should accept `Union[str, Split, List[str], None]` or more specific overloads for the split parameter to correctly represent runtime behavior.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39
- Python version: 3.12.7
- `huggingface_hub` version: 0.32.0
- PyArrow version: 20.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7582 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7582/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7582/comments | https://api.github.com/repos/huggingface/datasets/issues/7582/events | https://github.com/huggingface/datasets/pull/7582 | 3,083,515,643 | PR_kwDODunzps6XPIt7 | 7,582 | fix: Add embed_storage in Pdf feature | {
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} | [] | closed | false | null | [] | null | 1 | 2025-05-22T14:06:29Z | 2025-05-22T14:17:38Z | 2025-05-22T14:17:36Z | CONTRIBUTOR | null | null | null | Add missing `embed_storage` method in Pdf feature (Same as in Audio and Image) | {
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https://api.github.com/repos/huggingface/datasets/issues/7581 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7581/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7581/comments | https://api.github.com/repos/huggingface/datasets/issues/7581/events | https://github.com/huggingface/datasets/pull/7581 | 3,083,080,413 | PR_kwDODunzps6XNpm0 | 7,581 | Add missing property on `RepeatExamplesIterable` | {
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} | [] | closed | false | null | [] | null | 0 | 2025-05-22T11:41:07Z | 2025-06-05T12:41:30Z | 2025-06-05T12:41:29Z | CONTRIBUTOR | null | null | null | Fixes #7561 | {
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https://api.github.com/repos/huggingface/datasets/issues/7580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7580/comments | https://api.github.com/repos/huggingface/datasets/issues/7580/events | https://github.com/huggingface/datasets/issues/7580 | 3,082,993,027 | I_kwDODunzps63wr2D | 7,580 | Requesting a specific split (eg: test) still downloads all (train, test, val) data when streaming=False. | {
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} | ### Describe the bug
When using load_dataset() from the datasets library (in load.py), specifying a particular split (e.g., split="train") still results in downloading data for all splits when streaming=False. This happens during the builder_instance.download_and_prepare() call.
This behavior leads to unnecessary bandwidth usage and longer download times, especially for large datasets, even if the user only intends to use a single split.
### Steps to reproduce the bug
dataset_name = "skbose/indian-english-nptel-v0"
dataset = load_dataset(dataset_name, token=hf_token, split="test")
### Expected behavior
Optimize the download logic so that only the required split is downloaded when streaming=False when a specific split is provided.
### Environment info
Dataset: skbose/indian-english-nptel-v0
Platform: M1 Apple Silicon
Python verison: 3.12.9
datasets>=3.5.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7579 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7579/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7579/comments | https://api.github.com/repos/huggingface/datasets/issues/7579/events | https://github.com/huggingface/datasets/pull/7579 | 3,081,849,022 | PR_kwDODunzps6XJerX | 7,579 | Fix typos in PDF and Video documentation | {
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https://api.github.com/repos/huggingface/datasets/issues/7577 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7577/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7577/comments | https://api.github.com/repos/huggingface/datasets/issues/7577/events | https://github.com/huggingface/datasets/issues/7577 | 3,080,833,740 | I_kwDODunzps63ocrM | 7,577 | arrow_schema is not compatible with list | {
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} | [] | closed | false | null | [] | null | 3 | 2025-05-21T16:37:01Z | 2025-05-26T18:49:51Z | 2025-05-26T18:32:55Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
```
import datasets
f = datasets.Features({'x': list[datasets.Value(dtype='int32')]})
f.arrow_schema
Traceback (most recent call last):
File "datasets/features/features.py", line 1826, in arrow_schema
return pa.schema(self.type).with_metadata({"huggingface": json.dumps(hf_metadata)})
^^^^^^^^^
File "datasets/features/features.py", line 1815, in type
return get_nested_type(self)
^^^^^^^^^^^^^^^^^^^^^
File "datasets/features/features.py", line 1252, in get_nested_type
return pa.struct(
^^^^^^^^^^
File "pyarrow/types.pxi", line 5406, in pyarrow.lib.struct
File "pyarrow/types.pxi", line 3890, in pyarrow.lib.field
File "pyarrow/types.pxi", line 5918, in pyarrow.lib.ensure_type
TypeError: DataType expected, got <class 'list'>
```
The following works
```
f = datasets.Features({'x': datasets.LargeList(datasets.Value(dtype='int32'))})
```
### Expected behavior
according to https://github.com/huggingface/datasets/blob/458f45a22c3cc9aea5f442f6f519333dcfeae9b9/src/datasets/features/features.py#L1765 python list should be a valid type specification for features
### Environment info
- `datasets` version: 3.5.1
- Platform: macOS-15.5-arm64-arm-64bit
- Python version: 3.12.9
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7576 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7576/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7576/comments | https://api.github.com/repos/huggingface/datasets/issues/7576/events | https://github.com/huggingface/datasets/pull/7576 | 3,080,450,538 | PR_kwDODunzps6XEuMz | 7,576 | Fix regex library warnings | {
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} | [] | closed | false | null | [] | null | 1 | 2025-05-21T14:31:58Z | 2025-06-05T13:35:16Z | 2025-06-05T12:37:55Z | CONTRIBUTOR | null | null | null | # PR Summary
This small PR resolves the regex library warnings showing starting Python3.11:
```python
DeprecationWarning: 'count' is passed as positional argument
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7575 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7575/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7575/comments | https://api.github.com/repos/huggingface/datasets/issues/7575/events | https://github.com/huggingface/datasets/pull/7575 | 3,080,228,718 | PR_kwDODunzps6XD9gM | 7,575 | [MINOR:TYPO] Update save_to_disk docstring | {
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} | [] | closed | false | null | [] | null | 0 | 2025-05-21T13:22:24Z | 2025-06-05T12:39:13Z | 2025-06-05T12:39:13Z | CONTRIBUTOR | null | null | null | r/hub/filesystem in save_to_disk | {
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https://api.github.com/repos/huggingface/datasets/issues/7574 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7574/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7574/comments | https://api.github.com/repos/huggingface/datasets/issues/7574/events | https://github.com/huggingface/datasets/issues/7574 | 3,079,641,072 | I_kwDODunzps63j5fw | 7,574 | Missing multilingual directions in IWSLT2017 dataset's processing script | {
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} | [] | open | false | null | [] | null | 2 | 2025-05-21T09:53:17Z | 2025-05-26T18:36:38Z | null | NONE | null | null | {
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"total": 0
} | ### Describe the bug
Hi,
Upon using `iwslt2017.py` in `IWSLT/iwslt2017` on the Hub for loading the datasets, I am unable to obtain the datasets for the language pairs `de-it`, `de-ro`, `de-nl`, `it-de`, `nl-de`, and `ro-de` using it. These 6 pairs do not show up when using `get_dataset_config_names()` to obtain the list of all the configs present in `IWSLT/iwslt2017`. This should not be the case since as mentioned in their original paper (please see https://aclanthology.org/2017.iwslt-1.1.pdf), the authors specify that "_this year we proposed the multilingual translation between any pair of languages from {Dutch, English, German, Italian, Romanian}..._" and because these datasets are indeed present in `data/2017-01-trnmted/texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.zip`.
Best Regards,
Anand
### Steps to reproduce the bug
Check the output of `get_dataset_config_names("IWSLT/iwslt2017", trust_remote_code=True)`: only 24 language pairs are present and the following 6 config names are absent: `iwslt2017-de-it`, `iwslt2017-de-ro`, `iwslt2017-de-nl`, `iwslt2017-it-de`, `iwslt2017-nl-de`, and `iwslt2017-ro-de`.
### Expected behavior
The aforementioned 6 language pairs should also be present and hence, all these 6 language pairs' IWSLT2017 datasets must also be available for further use.
I would suggest removing `de` from the `BI_LANGUAGES` list and moving it over to the `MULTI_LANGUAGES` list instead in `iwslt2017.py` to account for all the 6 missing language pairs (the same `de-en` dataset is present in both `data/2017-01-trnmted/texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.zip` and `data/2017-01-trnted/texts/de/en/de-en.zip` but the `de-ro`, `de-nl`, `it-de`, `nl-de`, and `ro-de` datasets are only present in `data/2017-01-trnmted/texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.zip`: so, its unclear why the following comment: _`# XXX: Artificially removed DE from here, as it also exists within bilingual data`_ has been added as `L71` in `iwslt2017.py`). The `README.md` file in `IWSLT/iwslt2017`must then be re-created using `datasets-cli test path/to/iwslt2017.py --save_info --all_configs` to pass all split size verification checks for the 6 new language pairs which were previously non-existent.
### Environment info
- `datasets` version: 3.5.0
- Platform: Linux-6.8.0-56-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.30.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
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https://api.github.com/repos/huggingface/datasets/issues/7573 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7573/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7573/comments | https://api.github.com/repos/huggingface/datasets/issues/7573/events | https://github.com/huggingface/datasets/issues/7573 | 3,076,415,382 | I_kwDODunzps63Xl-W | 7,573 | No Samsum dataset | {
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} | [] | closed | false | null | [] | null | 3 | 2025-05-20T09:54:35Z | 2025-06-18T12:52:23Z | 2025-06-18T12:52:23Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
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} | ### Describe the bug
https://huggingface.co/datasets/Samsung/samsum dataset not found error 404
Originated from https://github.com/meta-llama/llama-cookbook/issues/948
### Steps to reproduce the bug
go to website https://huggingface.co/datasets/Samsung/samsum
see the error
also downloading it with python throws
```
Couldn't find 'Samsung/samsum' on the Hugging Face Hub either: FileNotFoundError: Samsung/samsum@f00baf5a7d4abfec6820415493bcb52c587788e6/samsum.py (repository not found)
```
### Expected behavior
Dataset exists
### Environment info
```
- `datasets` version: 3.2.0
- Platform: macOS-15.4.1-arm64-arm-64bit
- Python version: 3.12.2
- `huggingface_hub` version: 0.26.5
- PyArrow version: 16.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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} | [] | closed | false | null | [] | null | 1 | 2025-05-19T17:16:59Z | 2025-06-05T12:25:42Z | 2025-06-05T12:25:41Z | CONTRIBUTOR | null | null | null | More info: [comment](https://github.com/huggingface/datasets/pull/7564#issuecomment-2863391781). | {
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} | [] | closed | false | null | [] | null | 1 | 2025-05-19T14:49:23Z | 2025-05-19T14:52:24Z | 2025-05-19T14:49:28Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7570 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7570/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7570/comments | https://api.github.com/repos/huggingface/datasets/issues/7570/events | https://github.com/huggingface/datasets/issues/7570 | 3,065,966,529 | I_kwDODunzps62vu_B | 7,570 | Dataset lib seems to broke after fssec lib update | {
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} | [] | closed | false | null | [] | null | 3 | 2025-05-15T11:45:06Z | 2025-06-13T00:44:27Z | 2025-06-13T00:44:27Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I am facing an issue since today where HF's dataset is acting weird and in some instances failure to recognise a valid dataset entirely, I think it is happening due to recent change in `fsspec` lib as using this command fixed it for me in one-time: `!pip install -U datasets huggingface_hub fsspec`
### Steps to reproduce the bug
from datasets import load_dataset
def download_hf():
dataset_name = input("Enter the dataset name: ")
subset_name = input("Enter subset name: ")
ds = load_dataset(dataset_name, name=subset_name)
for split in ds:
ds[split].to_pandas().to_csv(f"{subset_name}.csv", index=False)
download_hf()
### Expected behavior
```
Downloading readme: 100%
1.55k/1.55k [00:00<00:00, 121kB/s]
Downloading data files: 100%
1/1 [00:00<00:00, 2.06it/s]
Downloading data: 0%| | 0.00/54.2k [00:00<?, ?B/s]
Downloading data: 100%|██████████| 54.2k/54.2k [00:00<00:00, 121kB/s]
Extracting data files: 100%
1/1 [00:00<00:00, 35.17it/s]
Generating test split:
140/0 [00:00<00:00, 2628.62 examples/s]
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
[<ipython-input-2-12ab305b0e77>](https://localhost:8080/#) in <cell line: 0>()
8 ds[split].to_pandas().to_csv(f"{subset_name}.csv", index=False)
9
---> 10 download_hf()
2 frames
[/usr/local/lib/python3.11/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1171 is_local = not is_remote_filesystem(self._fs)
1172 if not is_local:
-> 1173 raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
1174 if not os.path.exists(self._output_dir):
1175 raise FileNotFoundError(
NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.
```
OR
```
Traceback (most recent call last):
File "e:\Fuck\download-data\mcq_dataset.py", line 10, in <module>
download_hf()
File "e:\Fuck\download-data\mcq_dataset.py", line 6, in download_hf
ds = load_dataset(dataset_name, name=subset_name)
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py", line 2606, in load_dataset
builder_instance = load_dataset_builder(
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py", line 2277, in load_dataset_builder
dataset_module = dataset_module_factory(
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py", line 1917, in dataset_module_factory
raise e1 from None
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py", line 1867, in dataset_module_factory
raise DatasetNotFoundError(f"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.") from e
datasets.exceptions.DatasetNotFoundError: Dataset 'dataset repo_id' doesn't exist on the Hub or cannot be accessed.
```
### Environment info
colab and 3.10 local system | {
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https://api.github.com/repos/huggingface/datasets/issues/7569 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7569/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7569/comments | https://api.github.com/repos/huggingface/datasets/issues/7569/events | https://github.com/huggingface/datasets/issues/7569 | 3,061,234,054 | I_kwDODunzps62drmG | 7,569 | Dataset creation is broken if nesting a dict inside a dict inside a list | {
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} | [] | open | false | null | [] | null | 2 | 2025-05-13T21:06:45Z | 2025-05-20T19:25:15Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Hey,
I noticed that the creation of datasets with `Dataset.from_generator` is broken if dicts and lists are nested in a certain way and a schema is being passed. See below for details.
Best,
Tim
### Steps to reproduce the bug
Runing this code:
```python
from datasets import Dataset, Features, Sequence, Value
def generator():
yield {
"a": [{"b": {"c": 0}}],
}
features = Features(
{
"a": Sequence(
feature={
"b": {
"c": Value("int32"),
},
},
length=1,
)
}
)
dataset = Dataset.from_generator(generator, features=features)
```
leads to
```
Generating train split: 1 examples [00:00, 540.85 examples/s]
Traceback (most recent call last):
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1635, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
^^^^^^^^^^^^^^^^^
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/arrow_writer.py", line 657, in finalize
self.write_examples_on_file()
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/arrow_writer.py", line 510, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/arrow_writer.py", line 629, in write_batch
pa_table = pa.Table.from_arrays(arrays, schema=schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 4851, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 1608, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 399, in pyarrow.lib.asarray
File "pyarrow/array.pxi", line 1004, in pyarrow.lib.Array.cast
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/pyarrow/compute.py", line 405, in cast
return call_function("cast", [arr], options, memory_pool)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/_compute.pyx", line 598, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 393, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from fixed_size_list<item: struct<c: int32>>[1] to struct using function cast_struct
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/user/test/tools/hf_test2.py", line 23, in <module>
dataset = Dataset.from_generator(generator, features=features)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 1114, in from_generator
).read()
^^^^^^
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/io/generator.py", line 49, in read
self.builder.download_and_prepare(
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1649, in _download_and_prepare
super()._download_and_prepare(
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1487, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/user/miniconda3/envs/test/lib/python3.11/site-packages/datasets/builder.py", line 1644, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
Process finished with exit code 1
```
### Expected behavior
I expected this code not to lead to an error.
I have done some digging and figured out that the problem seems to be the `get_nested_type` function in `features.py`, which, for whatever reason, flips Sequences and dicts whenever it encounters a dict inside of a sequence. This seems to be necessary, as disabling that flip leads to another error. However, by keeping that flip enabled for the highest level and disabling it for all subsequent levels, I was able to work around this problem. Specifically, by patching `get_nested_type` as follows, it works on the given example (emphasis on the `level` parameter I added):
```python
def get_nested_type(schema: FeatureType, level=0) -> pa.DataType:
"""
get_nested_type() converts a datasets.FeatureType into a pyarrow.DataType, and acts as the inverse of
generate_from_arrow_type().
It performs double-duty as the implementation of Features.type and handles the conversion of
datasets.Feature->pa.struct
"""
# Nested structures: we allow dict, list/tuples, sequences
if isinstance(schema, Features):
return pa.struct(
{key: get_nested_type(schema[key], level = level + 1) for key in schema}
) # Features is subclass of dict, and dict order is deterministic since Python 3.6
elif isinstance(schema, dict):
return pa.struct(
{key: get_nested_type(schema[key], level = level + 1) for key in schema}
) # however don't sort on struct types since the order matters
elif isinstance(schema, (list, tuple)):
if len(schema) != 1:
raise ValueError("When defining list feature, you should just provide one example of the inner type")
value_type = get_nested_type(schema[0], level = level + 1)
return pa.list_(value_type)
elif isinstance(schema, LargeList):
value_type = get_nested_type(schema.feature, level = level + 1)
return pa.large_list(value_type)
elif isinstance(schema, Sequence):
value_type = get_nested_type(schema.feature, level = level + 1)
# We allow to reverse list of dict => dict of list for compatibility with tfds
if isinstance(schema.feature, dict) and level == 1:
data_type = pa.struct({f.name: pa.list_(f.type, schema.length) for f in value_type})
else:
data_type = pa.list_(value_type, schema.length)
return data_type
# Other objects are callable which returns their data type (ClassLabel, Array2D, Translation, Arrow datatype creation methods)
return schema()
```
I have honestly no idea what I am doing here, so this might produce other issues for different inputs.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-6.8.0-59-generic-x86_64-with-glibc2.35
- Python version: 3.11.11
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
Also tested it with 3.5.0, same result. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7568 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7568/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7568/comments | https://api.github.com/repos/huggingface/datasets/issues/7568/events | https://github.com/huggingface/datasets/issues/7568 | 3,060,515,257 | I_kwDODunzps62a8G5 | 7,568 | `IterableDatasetDict.map()` call removes `column_names` (in fact info.features) | {
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} | When calling `IterableDatasetDict.map()`, each split’s `IterableDataset.map()` is invoked without a `features` argument. While omitting the argument isn’t itself incorrect, the implementation then sets `info.features = features`, which destroys the original `features` content. Since `IterableDataset.column_names` relies on `info.features`, it ends up broken (`None`).
**Reproduction**
1. Define an IterableDatasetDict with a non-None features schema.
2. my_iterable_dataset_dict contains "text" column.
3. Call:
```Python
new_dict = my_iterable_dataset_dict.map(
function=my_fn,
with_indices=False,
batched=True,
batch_size=16,
)
```
4. Observe
```Python
new_dict["train"].info.features # {'text': Value(dtype='string', id=None)}
new_dict["train"].column_names # ['text']
```
5. Call:
```Python
new_dict = my_iterable_dataset_dict.map(
function=my_fn,
with_indices=False,
batched=True,
batch_size=16,
remove_columns=["foo"]
)
```
6. Observe:
```Python
new_dict["train"].info.features # → None
new_dict["train"].column_names # → None
```
5. Internally, in dataset_dict.py this loop omits features ([code](https://github.com/huggingface/datasets/blob/b9efdc64c3bfb8f21f8a4a22b21bddd31ecd5a31/src/datasets/dataset_dict.py#L2047C5-L2056C14)):
```Python
for split, dataset in self.items():
dataset_dict[split] = dataset.map(
function=function,
with_indices=with_indices,
input_columns=input_columns,
batched=batched,
batch_size=batch_size,
drop_last_batch=drop_last_batch,
remove_columns=remove_columns,
fn_kwargs=fn_kwargs,
# features omitted → defaults to None
)
```
7. Then inside IterableDataset.map() ([code](https://github.com/huggingface/datasets/blob/b9efdc64c3bfb8f21f8a4a22b21bddd31ecd5a31/src/datasets/iterable_dataset.py#L2619C1-L2622C37)) correct `info.features` is replaced by features which is None:
```Python
info = self.info.copy()
info.features = features # features is None here
return IterableDataset(..., info=info, ...)
```
**Suggestion**
It looks like this replacement was added intentionally but maybe should be done only if `features` is `not None`.
**Workarround:**
`SFTTrainer` calls `dataset.map()` several times and then fails on `NoneType` when iterating `dataset.column_names`.
I decided to write this patch - works form me.
```python
def patch_iterable_dataset_map():
_orig_map = IterableDataset.map
def _patched_map(self, *args, **kwargs):
if "features" not in kwargs or kwargs["features"] is None:
kwargs["features"] = self.info.features
return _orig_map(self, *args, **kwargs)
IterableDataset.map = _patched_map
```
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https://api.github.com/repos/huggingface/datasets/issues/7567 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7567/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7567/comments | https://api.github.com/repos/huggingface/datasets/issues/7567/events | https://github.com/huggingface/datasets/issues/7567 | 3,058,308,538 | I_kwDODunzps62ShW6 | 7,567 | interleave_datasets seed with multiple workers | {
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} | [] | open | false | null | [] | null | 7 | 2025-05-12T22:38:27Z | 2025-06-29T06:53:59Z | null | NONE | null | null | {
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} | ### Describe the bug
Using interleave_datasets with multiple dataloader workers and a seed set causes the same dataset sampling order across all workers.
Should the seed be modulated with the worker id?
### Steps to reproduce the bug
See above
### Expected behavior
See above
### Environment info
- `datasets` version: 3.5.1
- Platform: macOS-15.4.1-arm64-arm-64bit
- Python version: 3.12.9
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0 | null | {
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