AniketTec/github-issues huggingface dataset:
Title: Throw an error when dataset improperly indexed
State: closed

### Describe the bug

Pandas-style subset indexing on dataset does not throw an error, when maybe it should. Instead returns the first instance of the dataset regardless of index condition.

### Steps to reproduce the bug

Steps to reproduce the behavior:

1. `squad = datasets.load_dataset("squad_v2", split="validation")`
2. `item = squad[squad['question'] ==  "Who was the Norse leader?"]`
or `it = squad[squad['id'] == '56ddde6b9a695914005b962b']`
3.  returns the first item in the dataset, which does not satisfy the above conditions:

`{'id': '56ddde6b9a695914005b9628', 'title': 'Normans', 'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse ("Norman" comes from "Norseman") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries.', 'question': 'In what country is Normandy located?', 'answers': {'text': ['France', 'France', 'France', 'France'], 'answer_start': [159, 159, 159, 159]}}`

### Expected behavior

Should either throw an error message, or return the dataset item that satisfies the condition.

### Environment info

- `datasets` version: 2.9.0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.3

Comments:
Thanks for reporting, @sarahwie.

Please note that in `datasets` we do not have vectorized operation like `pandas`. Therefore, your equality comparisons above are `False`:
- For example: `squad['question']` returns a `list`, and this list is not equal to `"Who was the Norse leader?"`

The `False` value is equivalent to `0` when indexing a dataset, thus the reason why you get the first element (with index 0): 
- For example: `squad[False]` is equivalent to `squad[0]`

Maybe we should an exception instead of assuming that `False` is equivalent to `0` (and `True` is equivalent to `1`) in the context of indexing.
Title: Adding chemistry dataset/models in huggingface
State: closed
Labels: description: New feature or request
name: enhancement

### Feature request

Huggingface is really amazing platform for open science.

In addition to computer vision, video and NLP, would it be of interest to add chemistry/materials science dataset/models in Huggingface? Or, if its already done, can you provide some pointers.

We have been working on a comprehensive benchmark on this topic: [JARVIS-Leaderboard](https://pages.nist.gov/jarvis_leaderboard/) and I am wondering if we could contribute/integrate this project as a part of huggingface. 

### Motivation

Similar to the main stream AI field, there is need of large scale benchmarks/models/infrastructure for chemistry/materials data.

### Your contribution

We can start adding datasets as our [benchmarks](https://github.com/usnistgov/jarvis_leaderboard/tree/main/jarvis_leaderboard/benchmarks) should be easily convertible to the dataset format.

Comments:
Hi! 

This would be a nice addition to the Hub! You can find the existing chemistry datasets/models on the Hub (using the `chemistry` tag) [here](https://huggingface.co/search/full-text?q=chemistry&type=model&type=dataset).

Feel free to ping us here on the Hub if you need help adding the datasets.
Title: Error loading natural_questions
State: closed

### Describe the bug

When try to load natural_questions through datasets == 2.12.0 with python == 3.8.9:

```python
import datasets
datasets.load_dataset('natural_questions',beam_runner='DirectRunner')
```

It failed with following info:

`pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs`

### Steps to reproduce the bug

In python console:

```python
import datasets
datasets.load_dataset('natural_questions',beam_runner='DirectRunner')
```

Then the trace is:

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/load.py", line 1797, in load_dataset
    builder_instance.download_and_prepare(
  File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/builder.py", line 890, in download_and_prepare
    self._download_and_prepare(
  File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/builder.py", line 2019, in _download_and_prepare
    num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter))
  File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/arrow_writer.py", line 694, in finalize
    shard_num_bytes, _ = parquet_to_arrow(source, destination)
  File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/arrow_writer.py", line 737, in parquet_to_arrow
    for record_batch in parquet_file.iter_batches():
  File "pyarrow/_parquet.pyx", line 1323, in iter_batches
  File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```

### Expected behavior

load natural_question questions

### Environment info

```
- `datasets` version: 2.12.0
- Platform: Linux-3.10.0-1160.42.2.el7.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.8.9
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.1
```

Comments:
Hi! You can avoid this error by using the preprocessed version:
```python
import datasets
ds = datasets.load_dataset('natural_questions')
```

PS: Once we finish https://github.com/huggingface/datasets/pull/5364, this error will no longer be a problem.

> Hi! You can avoid this error by using the preprocessed version:
> 
> ```python
> import datasets
> ds = datasets.load_dataset('natural_questions')
> ```
> 
> PS: Once we finish #5364, this error will no longer be a problem.

Thanks, wish #5364  finish early
Title: Can not load audiofolder dataset on kaggle
State: closed

### Describe the bug
It's crash log:
FileNotFoundError: Couldn't find a dataset script at /kaggle/working/audiofolder/audiofolder.py or any data file in the same directory. Couldn't find 'audiofolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/audiofolder/audiofolder.py


### Steps to reproduce the bug

![image](https://github.com/huggingface/datasets/assets/93691919/a2829d27-d15c-4acc-86fb-d1987c760468)
common_voice = load_dataset("audiofolder", data_dir="/kaggle/working/data")

### Expected behavior

load dataset without error. It works ok on colab, but on kaggle it happends.

### Environment info

- `datasets` version: 2.1.0
- Platform: Linux-5.15.109+-x86_64-with-glibc2.31
- Python version: 3.10.10
- PyArrow version: 9.0.0
- Pandas version: 1.5.3

Comments:
Hi! `audiofolder` requires `datasets>=2.5.0`, so please update the `datasets`' installation (`pip install -U datasets`) in the environment (and restart the env for the update to take effect) to resolve the issue.

> Hi! `audiofolder` requires `datasets>=2.5.0`, so please update the `datasets`' installation (`pip install -U datasets`) in the environment to resolve the issue.

I don't think it is a problem of the version. It runs ok on colab or local machine. Only on kaggle will has this bug.

Based on your dataset info, the installed version is `2.1.0`, which does not include `audiofolder`.

By default, Kaggle preinstalls `datasets` into a new env, but the version it installs is outdated and does not contain newer features such as `audiofolder`
Title: CSV datasets should only read the CSV data files in the repo
State: closed
Labels: description: Something isn't working
name: bug

When a no-script dataset has many CSV files and a JPG file, the library infers to use the Csv builder, but tries to read as CSV all files in the repo, also the JPG file.

I think the Csv builder should filter out non-CSV files when reading.

An analogue solution should be implemented for other packaged builders.

Related to:
- https://huggingface.co/datasets/abidlabs/img2text/discussions/1
- https://github.com/gradio-app/gradio/pull/3973#issuecomment-1545409061

CC: @abidlabs @severo 

Comments:
Title: Error message not clear in interleaving datasets
State: closed

### System Info

standard env

### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

I'm trying to interleave 'sciq', 'wiki' and the 'pile-enron' dataset. I think the error I made was that I loaded the train split of one, but for the other but the error is not too helpful- 

```
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[/home/suryahari/Vornoi/save_model_ops.py](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/Vornoi/save_model_ops.py) in line 3
      [41](file:///home/suryahari/Vornoi/save_model_ops.py?line=40) # %%
----> [43](file:///home/suryahari/Vornoi/save_model_ops.py?line=42) dataset = interleave_datasets(datasets, stopping_strategy="all_exhausted")

File [~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124), in interleave_datasets(datasets, probabilities, seed, info, split, stopping_strategy)
    [122](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=121) for dataset in datasets[1:]:
    [123](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=122)     if (map_style and not isinstance(dataset, Dataset)) or (iterable and not isinstance(dataset, IterableDataset)):
--> [124](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=123)         raise ValueError(
    [125](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=124)             f"Unable to interleave a {type(datasets[0])} with a {type(dataset)}. Expected a list of Dataset objects or a list of IterableDataset objects."
    [126](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=125)         )
    [127](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=126) if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
    [128](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=127)     raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy.")

ValueError: Unable to interleave a  with a . Expected a list of Dataset objects or a list of IterableDataset objects.
```

### Expected behavior

the error message should hopefully be more clear

Comments:
Title: load_dataset('bigcode/the-stack-dedup', streaming=True) very slow!
State: closed

### Describe the bug

Running

```
import datasets
ds = datasets.load_dataset('bigcode/the-stack-dedup', streaming=True)
```

takes about 2.5 minutes! 

I would expect this to be near instantaneous. With other datasets, the runtime is one or two seconds.


### Environment info

- `datasets` version: 2.11.0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.10.10
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0

Comments:
This is due to the slow resolution of the data files: https://github.com/huggingface/datasets/issues/5537.

We plan to switch to `huggingface_hub`'s `HfFileSystem` soon to make the resolution faster (will be up to 20x faster once we merge https://github.com/huggingface/huggingface_hub/pull/1443)



You're right, when I try to parse more than 50GB of text data, I also get very slow, usually taking hours or even tens of hours.

> You're right, when I try to parse more than 50GB of text data, I also get very slow, usually taking hours or even tens of hours.

That's unrelated to the problem discussed in this issue. 

> > You're right, when I try to parse more than 50GB of text data, I also get very slow, usually taking hours or even tens of hours.
> 
> That's unrelated to the problem discussed in this issue.

Sorry, I misunderstood it.

Closing this issue as it has been addressed in `huggingface_hub`!

(This now takes 25s to execute on my machine.)

Thanks for the improvements! 🎉🎉

25 seconds is better but still about 2500x slower than this _should_ be! Loading a tiny 1-2KB metadata file is all that would be necessary with a better design.

Once we merge https://github.com/huggingface/huggingface_hub/pull/2103, this should only take a few seconds. 

For the 2500x speed-up (without metadata files with pre-cached results), we wouldn't even be allowed to use `os.path` functions or `requests`/`aiohttp` for HTTP requests, so I don't think this is feasible for us as it would make the code unreadable.

The HF Datasets Hub is (almost) platform-agnostic, so you are free to implement your own library (in a faster language than Python) to achieve this kind of performance, and we would be happy to support it 🙂.
Title: load model error.
State: closed

### Describe the bug

I had trained one model use deepspeed, when I load the final load I get the follow error:
OSError: Can't load tokenizer for '/XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/home/fm001/hzl/Project/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor' is the correct path to a directory containing all relevant files for a BloomTokenizerFast tokenizer.


my load code is : python chat.py --path /XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor/



### Steps to reproduce the bug

。。。

### Expected behavior

。。。

### Environment info

。。。

Comments:
Please report this in the `transformers` repo, as it's not related to `datasets`
Title: Remove columns in interable dataset
State: closed

### Feature request

Right now, remove_columns() produces a NotImplementedError for iterable style datasets

### Motivation

It would be great to have the same functionality irrespective of whether one is using an iterable or a map-style dataset

### Your contribution

hope and courage.

Comments:
Transferring this issue as it's related to the 🤗 Datasets library 

Hi @surya-narayanan! Could you provide some code snippet?

This method has been recently added to the `IterableDataset`, so you need to update the `datasets`' installation (`pip install -U datasets`) to use it.
Title: Can't add iterable datasets to a Dataset Dict.
State: closed

### System Info

standard env

### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Get the following error:

TypeError: Values in `DatasetDict` should be of type `Dataset` but got type '<class 'datasets.iterable_dataset.IterableDataset'>'


### Expected behavior

should be able to add iterable datasets to a dataset dict

Comments:
Transferring as this is relating to the 🤗 Datasets library

You need to use `IterableDatasetDict` instead of `DatasetDict` for iterable datasets.
Title: Make models/functions optimized with `torch.compile` hashable
State: closed
Labels: description: New feature or request
name: enhancement

As reported in https://github.com/huggingface/datasets/issues/5819, hashing functions/transforms that reference a model, or a function, optimized with `torch.compile` currently fails due to them not being picklable (the concrete error can be found in the linked issue).

The solutions to consider:
1. hashing/pickling the original, uncompiled version of a compiled model/function (attributes `_orig_mod`/`_torchdynamo_orig_callable`) (less precise than the 2nd option as it ignores the other params of `torch.compute`)
2. wait for https://github.com/pytorch/pytorch/issues/101107 to be resolved


Comments:
Title: Is uint8 supported?
State: closed

### Describe the bug

I expect the dataset to store the data in the `uint8` data type, but it's returning `int64` instead.
While I've found that `datasets` doesn't yet support float16 (https://github.com/huggingface/datasets/issues/4981), I'm wondering if this is the case for other data types as well.
Is there a way to store vector data as `uint8` and then upload it to the hub?

### Steps to reproduce the bug

```python
from datasets import Features, Dataset, Sequence, Value
import numpy as np

dataset = Dataset.from_dict(
    {"vector": [np.array([0, 1, 2], dtype=np.uint8)]}, features=Features({"vector": Sequence(Value("uint8"))})
).with_format("numpy")

print(dataset[0]["vector"].dtype)
```

### Expected behavior

Expected: `uint8`
Actual: `int64`

### Environment info

- `datasets` version: 2.12.0
- Platform: macOS-12.1-x86_64-i386-64bit
- Python version: 3.8.12
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3

Comments:
Hi ! The numpy formatting detaults to int64 and float32 - but you can use uint8 using
```python
ds = ds.with_format("numpy", dtype=np.uint8)
```

Related to https://github.com/huggingface/datasets/issues/5517.

Thank you!
By setting `ds.with_format("numpy", dtype=np.uint8)`, the dataset returns the data in `uint8`.

However, `with_format` and `set_format` seem to cast the data on-the-fly.
I want to reduce the dataset size by using `uint8` instead of `int64` and I observe no difference between using `int64` and `uint8` for the vector.
Is there any way to actually store the data in `uint8` and save the disk space and the downloading time when loaded from the hub?


If the feature type is `Value("uint8")` then it's written an uint8 on disk using the uint8 Arrow dtype.

e.g.
```python
ds = Dataset.from_dict({"a": range(10)}, features=Features({"a": Value("uint8")}))
ds.data.nbytes
# 10
```

Oh, I understand now.
The data was stored in `uint8` from the beginning (when the dataset returns `int64`).

Thank you for your time!
My question is fully resolved.
Title: (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64'))
State: closed

### Describe the bug

M2 MBP can't run
```python
from datasets import load_dataset

jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```

### Steps to reproduce the bug

1. Use M2 MBP
2. Python 3.10.10 from pyenv
3. Run 
```
from datasets import load_dataset

jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```

### Expected behavior

Be able to run normally

### Environment info

```
from datasets import load_dataset

jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
OSX: 13.2
CPU: M2


Comments:
Can you paste the error stack trace?

That is weird. I can't reproduce it again after reboot.
```python
In [2]: import platform

In [3]: platform.platform()
Out[3]: 'macOS-13.2-arm64-arm-64bit'

In [4]: from datasets import load_dataset
   ...:
   ...: jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
Found cached dataset parquet (/Users/sarit/.cache/huggingface/datasets/nomic-ai___parquet/nomic-ai--gpt4all-j-prompt-generations-a3b62015e2e52043/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 63.25it/s]
```
Title: Stream data concatenation issue
State: closed

### Describe the bug

I am not able to concatenate the augmentation of the stream data. I am using the latest version of dataset.

ValueError: The features can't be aligned because the key audio of features {'audio_id': Value(dtype='string', 
id=None), 'audio': {'array': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'path': 
Value(dtype='null', id=None), 'sampling_rate': Value(dtype='int64', id=None)}, 'transcript': Value(dtype='string', 
id=None)} has unexpected type - {'array': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 
'path': Value(dtype='null', id=None), 'sampling_rate': Value(dtype='int64', id=None)} (expected either 
Audio(sampling_rate=16000, mono=True, decode=True, id=None) or Value("null").

### Steps to reproduce the bug

dataset = load_dataset("tobiolatunji/afrispeech-200", "all", streaming=True).shuffle(seed=42)
dataset_cln = dataset.remove_columns(['speaker_id', 'path', 'age_group', 'gender', 'accent', 'domain', 'country', 'duration'])
dataset_cln = dataset_cln.cast_column("audio", Audio(sampling_rate=16000))
from audiomentations import AddGaussianNoise,Compose,Gain,OneOf,PitchShift,PolarityInversion,TimeStretch

augmentation = Compose([
    AddGaussianNoise(min_amplitude=0.005, max_amplitude=0.015, p=0.2)
])

def augment_dataset(batch):
    audio = batch["audio"]
    audio["array"] = augmentation(audio["array"], sample_rate=audio["sampling_rate"])    
    return batch

augmented_dataset_cln = dataset_cln['train'].map(augment_dataset)
dataset_cln['train'] = interleave_datasets([dataset_cln['train'], augmented_dataset_cln])
dataset_cln['train'] = dataset_cln['train'].shuffle(seed=42)

### Expected behavior

I should be able to merge as sampling rate is same.

### Environment info

import datasets
import transformers
import accelerate
print(datasets.__version__)
print(transformers.__version__)
print(torch.__version__)
print(evaluate.__version__)
print(accelerate.__version__)

2.12.0
4.28.1
2.0.0
0.4.0
0.18.0

Comments:
Hi! 

You can call `map` as follows to avoid the error:
```python
augmented_dataset_cln = dataset_cln['train'].map(augment_dataset, features=dataset_cln['train'].features)
```

Thanks it is solved

Hi! 
I have run into the same problem with you. Could you please let me know how you solve it? Thanks!