ahmedfarazsyk/datasets-github-issues huggingface dataset:
ValueError: Invalid pattern: '**' can only be an entire path component [Colab]
Author: wkambale
State: closed

### 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

Comments: Could you please run the following code snippet in your environment and share the exact output? This will help check for any compatibility issues within the env itself. 

```
import datasets
import huggingface_hub
import fsspec

print("datasets version:", datasets.__version__)
print("huggingface_hub version:", huggingface_hub.__version__)
print("fsspec version:", fsspec.__version__)
```

```bash
datasets version: 2.14.4
huggingface_hub version: 0.31.4
fsspec version: 2025.3.2
```

Version 2.14.4 is not the latest version available, in fact it is from August 08, 2023 (you can check here: https://pypi.org/project/datasets/#history)

Use pip install datasets==3.6.0 to install a more recent version (from May 7, 2025)

I also had the same problem with Colab, after updating to the latest version it was solved.

I hope it helps

thank you @CleitonOERocha. it sure did help.

updating `datasets` to v3.6.0 and keeping `fsspec` on v2025.3.2 eliminates the issue.

Very helpful, thank you!
Add LMDB format support
Author: trotsky1997
State: open
Labels: name: enhancement

### 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

Comments: Hi ! Can you explain what's your use case ? Is it about converting LMDB to Dataset objects (i.e. converting to Arrow) ?
load_dataset type stubs reject List[str] for split parameter, but runtime supports it
Author: hierr
State: closed

### 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
Requesting a specific split (eg: test) still downloads all (train, test, val) data when streaming=False.
Author: s3pi
State: open

### 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

Comments: Hi ! There was a PR open to improve this: https://github.com/huggingface/datasets/pull/6832 
but it hasn't been continued so far.

It would be a cool improvement though !

Been having this problem with datasets and dataloader for a while.
arrow_schema is not compatible with list
Author: jonathanshen-upwork
State: closed

### 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

Comments: Thanks for reporting, I'll look into it

Actually it looks like you just forgot parenthesis:

```diff
- f = datasets.Features({'x': list[datasets.Value(dtype='int32')]})
+ f = datasets.Features({'x': list([datasets.Value(dtype='int32')])})
```

or simply using the `[ ]` syntax:

```python
f = datasets.Features({'x':[datasets.Value(dtype='int32')]})
```

I'm closing this issue if you don't mind

Ah is that what the syntax is? I don't think I was able to find an actual example of it so I assumed it was in the same way that you specify types eg. `list[int]`. This is good to know, thanks.
Missing multilingual directions in IWSLT2017 dataset's processing script
Author: andy-joy-25
State: open

### 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
- `fsspec` version: 2024.12.0

Comments: I have opened 2 PRs on the Hub: `https://huggingface.co/datasets/IWSLT/iwslt2017/discussions/7` and `https://huggingface.co/datasets/IWSLT/iwslt2017/discussions/8` to resolve this issue

cool ! I pinged the owners of the dataset on HF to merge your PRs :)
No Samsum dataset
Author: IgorKasianenko
State: closed

### 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
```

Comments: According to the following https://huggingface.co/posts/seawolf2357/424129432408590, as of now the dataset seems to be inaccessible.

@IgorKasianenko, would https://huggingface.co/datasets/knkarthick/samsum suffice for your purpose?


Thanks @SP1029 for the update!
That will work for now, using it as replacement. Is there a officially recommended way to maintain the CC licensed dataset under the organization account? 
Feel free to close this issue

> Is there an officially recommended way to maintain a CC-licensed dataset under an organizational account?

@IgorKasianenko, apologies, this is not my area of expertise.

> Please feel free to close this issue.

I have limited access and may not be able to do that. Since you opened it, you would be able to close it.

dataset_samsum = load_dataset("knkarthick/samsum")

is working
NotImplementedError: <class 'datasets.iterable_dataset.RepeatExamplesIterable'> doesn't implement num_shards yet
Author: cyanic-selkie
State: closed

### Describe the bug

When using `.repeat()` on an `IterableDataset`, this error gets thrown. There is [this thread](https://discuss.huggingface.co/t/making-an-infinite-iterabledataset/146192/5) that seems to imply the fix is trivial, but I don't know anything about this codebase, so I'm opening this issue rather than attempting to open a PR.

### Steps to reproduce the bug

1. Create an `IterableDataset`.
2. Call `.repeat(None)` on it.
3. Wrap it in a pytorch `DataLoader`
4. Iterate over it.

### Expected behavior

This should work normally.

### Environment info

datasets: 3.5.0
datasets downloads and generates all splits, even though a single split is requested (for dataset with loading script)
Author: sei-eschwartz
State: closed

### Describe the bug

`datasets` downloads and generates all splits, even though a single split is requested.  [This](https://huggingface.co/datasets/jordiae/exebench) is the dataset in question.  It uses a loading script.  I am not 100% sure that this is a bug, because maybe with loading scripts `datasets` must actually process all the splits?  But I thought loading scripts were designed to avoid this.

### Steps to reproduce the bug

See [this notebook](https://colab.research.google.com/drive/14kcXp_hgcdj-kIzK0bCG6taE-CLZPVvq?usp=sharing)

Or:
```python
from datasets import load_dataset
dataset = load_dataset('jordiae/exebench', split='test_synth', trust_remote_code=True)
```

### Expected behavior

I expected only the `test_synth` split to be downloaded and processed.

### Environment info

- `datasets` version: 3.5.1
- Platform: Linux-6.1.123+-x86_64-with-glibc2.35
- Python version: 3.11.12
- `huggingface_hub` version: 0.30.2
- PyArrow version: 18.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2025.3.0

Comments: Hi ! there has been some effort on allowing to download only a subset of splits in https://github.com/huggingface/datasets/pull/6832 but no one has been continuing this work so far. This would be a welcomed contribution though

Also note that loading script are often unoptimized, and we recommend using datasets in standard formats like Parquet instead.

Btw there is a CLI tool to convert a loading script to parquet:

```
datasets-cli convert_to_parquet <dataset-name> --trust_remote_code
```

Closing in favor of #6832
Issue with offline mode and partial dataset cached
Author: nrv
State: open

### Describe the bug

Hi, 

a issue related to #4760 here when loading a single file from a dataset, unable to access it in offline mode afterwards

### Steps to reproduce the bug

```python
import os
# os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["HF_TOKEN"] = "xxxxxxxxxxxxxx"

import datasets

dataset_name = "uonlp/CulturaX"
data_files = "fr/fr_part_00038.parquet"

ds = datasets.load_dataset(dataset_name, split='train', data_files=data_files)
print(f"Dataset loaded   : {ds}")
```
Once the file has been cached, I rerun with the HF_HUB_OFFLINE activated an get this error : 
```
ValueError: Couldn't find cache for uonlp/CulturaX for config 'default-1e725f978350254e'
Available configs in the cache: ['default-2935e8cdcc21c613']
```

### Expected behavior

Should be able to access the previously cached files

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-5.4.0-215-generic-x86_64-with-glibc2.31
- Python version: 3.12.0
- `huggingface_hub` version: 0.27.0
- PyArrow version: 19.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1


Comments: It seems the problem comes from builder.py / create_config_id()

On the first call, when the cache is empty we have
```
config_kwargs = {'data_files': {'train': ['hf://datasets/uonlp/CulturaX@6a8734bc69fefcbb7735f4f9250f43e4cd7a442e/fr/fr_part_00038.parquet']}}
```
leading to config_id beeing 'default-2935e8cdcc21c613'

then, on the second call, 
```
config_kwargs = {'data_files': 'fr/fr_part_00038.parquet'}
```
thus explaining why the hash is not the same, despite having the same parameter when calling load_dataset : data_files="fr/fr_part_00038.parquet"

Same behavior with version 3.5.1

Same issue when loading `google/IndicGenBench_flores_in` with `dataset==2.21.0` and `dataset==3.6.0` .




> It seems the problem comes from builder.py / create_config_id()
> 
> On the first call, when the cache is empty we have
> 
> ```
> config_kwargs = {'data_files': {'train': ['hf://datasets/uonlp/CulturaX@6a8734bc69fefcbb7735f4f9250f43e4cd7a442e/fr/fr_part_00038.parquet']}}
> ```
> 
> leading to config_id beeing 'default-2935e8cdcc21c613'
> 
> then, on the second call,
> 
> ```
> config_kwargs = {'data_files': 'fr/fr_part_00038.parquet'}
> ```
> 
> thus explaining why the hash is not the same, despite having the same parameter when calling load_dataset : data_files="fr/fr_part_00038.parquet"


I have identified that the issue indeed lies in the `data_files` within `config_kwargs`. 
The format and prefix of `data_files` differ depending on whether `HF_HUB_OFFLINE` is set, leading to different final `config_id` values. 
When I use other datasets without passing the `data_files` parameter, this issue does not occur.

A possible solution might be to standardize the formatting of `data_files` within the `create_config_id` function.
Large memory use when loading large datasets to a ZFS pool
Author: FredHaa
State: closed

### Describe the bug

When I load large parquet based datasets from the hub like `MLCommons/peoples_speech` using `load_dataset`, all my memory (500GB) is used and isn't released after loading, meaning that the process is terminated by the kernel if I try to load an additional dataset. This makes it impossible to train models using multiple large datasets.

### Steps to reproduce the bug

`uv run --with datasets==3.5.1 python`

```python
from datasets import load_dataset
load_dataset('MLCommons/peoples_speech', 'clean')
load_dataset('mozilla-foundation/common_voice_17_0', 'en')
```

### Expected behavior

I would expect that a lot less than 500GB of RAM would be required to load the dataset, or at least that the RAM usage would be cleared as soon as the dataset is loaded (and thus reside as a memory mapped file) such that other datasets can be loaded.

### Environment info

I am currently using the latest datasets==3.5.1 but I have had the same problem with multiple other versions.

Comments: Hi ! datasets are memory mapped from disk, so they don't fill out your RAM. Not sure what's the source of your memory issue.

What kind of system are you using ? and what kind of disk ?

Well, the fact of the matter is that my RAM is getting filled out by running the given example, as shown in [this video](https://streamable.com/usb0ql).

My system is a GPU server running Ubuntu. The disk is a SATA SSD attached to the server using a backplane. It is formatted with ZFS, mounted in /cache, and my HF_HOME is set to /cache/hf

I really need this fixed, so I am more than willing to test out various suggestions you might have, or write a PR if we can figure out what is going on.

I'm not super familiar with ZFS, but it looks like it loads the data in memory when the files are memory mapped, which is an issue.

Maybe it's a caching mechanism ? Since `datasets` accesses every memory mapped file to read a small part (the metadata of the arrow record batches), maybe ZFS brings the whole files in memory for quicker subsequent reads. This is an antipattern when it comes to lazy loading datasets of that size though

This is the answer.

I tried changing my HF_HOME to an NFS share, and no RAM is then consumed loading the dataset.

I will try to see if I can find a way to configure the ZFS pool to not cache the files (disabling the ARC/primary cache didn't work), and if I do write the solution in this issue. If I can't I guess I have to reformat my cache drive.
Networked Pull Through Cache
Author: wrmedford
State: open
Labels: name: enhancement

### Feature request

Introduce a HF_DATASET_CACHE_NETWORK_LOCATION configuration (e.g. an environment variable) together with a companion network cache service.

Enable a three-tier cache lookup for datasets:

1. Local on-disk cache
2. Configurable network cache proxy
3. Official Hugging Face Hub

### Motivation

- Distributed training & ephemeral jobs: In high-performance or containerized clusters, relying solely on a local disk cache either becomes a streaming bottleneck or incurs a heavy cold-start penalty as each job must re-download datasets.
- Traffic & cost reduction: A pull-through network cache lets multiple consumers share a common cache layer, reducing duplicate downloads from the Hub and lowering egress costs.
- Better streaming adoption: By offloading repeat dataset pulls to a locally managed cache proxy, streaming workloads can achieve higher throughput and more predictable latency.
- Proven pattern: Similar proxy-cache solutions (e.g. Harbor’s Proxy Cache for Docker images) have demonstrated reliability and performance at scale: https://goharbor.io/docs/2.1.0/administration/configure-proxy-cache/

### Your contribution

I’m happy to draft the initial PR for adding HF_DATASET_CACHE_NETWORK_LOCATION support in datasets and sketch out a minimal cache-service prototype.

I have limited bandwidth so I would be looking for collaborators if anyone else is interested.