josteece/github-issues huggingface dataset:
Import error
Opened by feketedavid1012 (closed)

https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28

Hy, 
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong)

Comments:
Hi ! Can you 
```python
import platform
print(platform.python_version())
```
to see that it returns ?

Hi,

3.8.13

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Subject: Re: [huggingface/datasets] Import error (Issue #5280)


Hi ! Can you

import platform
print(platform.python_version())

to see that it returns ?

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Then it should work as expected if you use the same python when using `datasets`

Please make sure you're running your code in the right environment

It's the right environment. But in if statement I have
"3.8.13" < 3.7
And in the error message is Python>=3.7 which is true in my case (3.8.13 is greater then 3.7), so I don't understand my python should be below the 3.7 which case the if statement is right, but the message is wrong, or above 3.7 which case if statement is wrong, error message is right.

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Cc: feketedavid1012 ***@***.***>; Author ***@***.***>
Subject: Re: [huggingface/datasets] Import error (Issue #5280)


Then it should work as expected if you use the same python when using datasets

Please make sure you're running your code in the right environment

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If you're having an error then you're not running your code in the right environment.
Warn about checksums
Opened by lhoestq (closed)

It takes a lot of time on big datasets to compute the checksums, we should at least add a warning to notify the user about this step. I also mentioned how to disable it, and added a tqdm bar (delay=5 seconds)

cc @ola13 

Comments:
_The documentation is not available anymore as the PR was closed or merged._

I'm also in favor of disabling this by default - it's kinda impractical

Great, thanks for the quick turnaround on this!
load_dataset does not read jsonl metadata file properly
Opened by 065294847 (closed)

### Describe the bug

Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features. 

Below is code to reproduce my exact example/problem.

### Steps to reproduce the bug

```ruby 
dataset_link="19Unu89Ih_kP6zsE7f9Mkw8dy3NwHopRF"
id = dataset_link
output = 'Godardv01.zip'
gdown.download(id=id, output=output, quiet=False)

ds = load_dataset("imagefolder", data_dir="/kaggle/working/Volumes/TOSHIBA/Godard_imgs/Volumes/TOSHIBA/Godard_imgs/Full/train", split="train", drop_labels=False)
print(ds)
```

### Expected behavior

I would expect that it returned "image" and "text" columns from the code above.

### Environment info

- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5

Comments:
Can you try to remove "drop_labels=false" ? It may force the loader to infer the labels instead of reading the metadata

Hi, thanks for responding. I tried that, but it does not change anything.

Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4

Probably the issue, will report back asap!

Okay, now it seems to actually load the metadata and create the train_split, but it still says only returns "image" and "label", which is always 0 since all images are from same folder

> Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4

Update: This was the issue.
load_dataset possibly broken for gated datasets?
Opened by TristanThrush (closed)

### Describe the bug

When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:

```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
    165     if repo_id.count("/") > 1:
    166         raise HFValidationError(
--> 167             "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
    168             f" '{repo_id}'. Use `repo_type` argument if needed."
    169         )

HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```

### Steps to reproduce the bug

Install requirements:

```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```

Then:

```
from datasets import load_dataset
auth_token = ""  # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```





### Expected behavior

Downloading of the datset

### Environment info

Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing

Comments:
@BradleyHsu

Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!

I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` and `huggingface_hub==0.10.1`.

https://github.com/poloclub/diffusiondb/issues/7

I fixed my issue by specifying `repo_type` in `hf_hub_url()`. https://github.com/poloclub/diffusiondb/commit/9eb91c79aaca98b0515a0ce45778b8af65b84652

I opened a PR on the Winoground's repo: https://huggingface.co/datasets/facebook/winoground/discussions/2

This is a bug in the script, indeed. The most robust fix is to use a relative path instead of `hf_hub_url`, which does not depend on `huggingface_hub`'s version 🙂. I've opened a PR here: https://huggingface.co/datasets/facebook/winoground/discussions/3.

Awesome, big thanks to both @xiaohk and @mariosasko!

so, if i reproduce the bug, what should i do ? with huggingface_hub0.13.3  dataset2.6.1

huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name':

    tokenizer = AutoTokenizer.from_pretrained(ARGS.model_path, trust_remote_code=True)

Please handle automatically for local path and repo name inside, otherwise users always get confused about this

I think I'm also hitting this error, trying to load a model from a local path.
download_mode="force_redownload" does not refresh cached dataset
Opened by nomisto (open)

### Describe the bug

`load_datasets` does not refresh dataset when features are imported from external file, even with `download_mode="force_redownload"`. The bug is not limited to nested fields, however it is more likely to occur with nested fields.

### Steps to reproduce the bug

To reproduce the bug 3 files are needed: `dataset.py` (contains dataset loading script), `schema.py` (contains features of dataset) and `main.py` (to run `load_datasets`)

`dataset.py`
```python
import datasets
from schema import features

class NewDataset(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            features=features
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN
            )
        ]

    def _generate_examples(self):
        data = [
            {"id": 0, "nested": []},
            {"id": 1, "nested": []}
        ]
        for key, example in enumerate(data):
            yield key, example
```

`schema.py`
```python
import datasets
features = datasets.Features(
    {
        "id": datasets.Value("int32"),
        "nested": [
            {"text": datasets.Value("string")}
        ]
    }
)
```

`main.py`
```python
import datasets
a = datasets.load_dataset("dataset.py")
print(a["train"].info.features)
```

Now if `main.py` is run it prints the following correct output: `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`. However, if f.e. the label of the feature "text" is changed to something else, f.e. to


`schema.py`
```python
import datasets
features = datasets.Features(
    {
        "id": datasets.Value("int32"),
        "nested": [
            {"textfoo": datasets.Value("string")}
        ]
    }
)
```

 `main.py` still prints `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`, even if run with `download_mode="force_redownload"`. The only fix is to delete the folder in the cache.

### Expected behavior

The cached dataset is deleted and refreshed when using `load_datasets` with `download_mode="force_redownload"`.

### Environment info

- `datasets` version: 2.7.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.9
- PyArrow version: 10.0.0
- Pandas version: 1.3.5

Comments:
Sharded save_to_disk + multiprocessing
Opened by lhoestq (closed)

Added `num_shards=` and `num_proc=` to `save_to_disk()`

EDIT: also added `max_shard_size=` to `save_to_disk()`, and also `num_shards=` to `push_to_hub`

I also:
- deprecated the fs parameter in favor of storage_options (for consistency with the rest of the lib) in save_to_disk and load_from_disk
- always embed the image/audio data in arrow when doing `save_to_disk`
- added a tqdm bar in `save_to_disk`
- Use the MockFileSystem in tests for `save_to_disk` and `load_from_disk`
- removed the unused integration tests with S3, since we can now test with `mockfs` instead of `s3fs`

TODO:
- [x] implem save_to_disk for dataset dict
- [x] save_to_disk for dataset dict tests
- [x] deprecate fs in dataset dict load_from_disk as well
- [x] update docs

Close #5263 
Close https://github.com/huggingface/datasets/issues/4196
Close https://github.com/huggingface/datasets/issues/4351

Comments:
_The documentation is not available anymore as the PR was closed or merged._

Added both num_shards and max_shard_size in push_to_hub/save_to_disk. Will take care of updating the tests later

It's ready for a final review @mariosasko and @albertvillanova, let me know what you think :)

Took your comments into account, and also changed `iflatmap_unordered` to take an iterable of kwargs to make the code more redable :)
Specify arguments as keywords in librosa.reshape to avoid future errors 
Opened by polinaeterna (closed)

Fixes a warning and future deprecation from `librosa.reshape`:
```
FutureWarning: Pass orig_sr=16000, target_sr=48000 as keyword args. From version 0.10 passing these as positional arguments will result in an error

   array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
```

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Get an IterableDataset from a map-style Dataset
Opened by lhoestq (closed)

This is useful to leverage iterable datasets specific features like:
- fast approximate shuffling
- lazy map, filter etc.

Iterating over the resulting iterable dataset should be at least as fast at iterating over the map-style dataset.

Here are some ideas regarding the API:
```python
# 1. 
# - consistency with load_dataset(..., streaming=True)
# - gives intuition that map/filter/etc. are done on-the-fly
ids = ds.stream()

# 2.
# - more explicit on the output type
# - but maybe sounds like a conversion tool rather than a step in a processing pipeline
ids = ds.as_iterable_dataset()
```

Comments:
I think `stream` could be misleading since the data is not being streamed from remote endpoints (one could think that's the case when they see `load_dataset` followed by `stream`). Hence, I prefer the second option.

PS: When we resolve https://github.com/huggingface/datasets/issues/4542, we could add `as_tf_dataset` to the API for consistency and deprecate `to_tf_dataset`.
AttributeError: 'Value' object has no attribute 'names'
Opened by emnaboughariou (closed)

Hello

I'm trying to build a model for custom token classification 
I already followed the token classification course on huggingface

while adapting the code to my work, this message occures : 
'Value' object has no attribute 'names'

Here's my code:
`raw_datasets` 
generates
DatasetDict({
    train: Dataset({
        features: ['isDisf', 'pos', 'tokens', 'id'],
        num_rows: 14
    })
})

`raw_datasets["train"][3]["isDisf"]`
generates 
['B_RM', 'I_RM', 'I_RM', 'B_RP', 'I_RP', 'O', 'O']

`dis_feature = raw_datasets["train"].features["isDisf"]
dis_feature`
generates
Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)

and
`label_names = dis_feature.feature.names
label_names`
generates
AttributeError                            Traceback (most recent call last)
[<ipython-input-28-972fd54a869a>](https://localhost:8080/#) in <module>
----> 1 label_names = dis_feature.feature.names
      2 label_names

AttributeError: 'Value' object has
AttributeError: 'Value' object has no attribute 'names'

Thank you for your help

Comments:
Hi ! It looks like your "isDif" column is a Sequence of Value("string"), not a Sequence of ClassLabel.

You can convert your Value("string") feature type to a ClassLabel feature type this way:
```python
from datasets import ClassLabel, Sequence

# provide the label_names yourself
label_names = [...]
# OR get them from the dataset
label_names = sorted(set(label for labels in raw_datasets["train"]["isDif"] for label in labels))

# Cast to ClassLabel
raw_datasets = raw_datasets.cast_column("isDif", Sequence(ClassLabel(names=label_names)))
```


thank you 
it works 💯
Add PubTables-1M
Opened by NielsRogge (open)

### Name

PubTables-1M

### Paper

https://openaccess.thecvf.com/content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html

### Data

https://github.com/microsoft/table-transformer

### Motivation

Table Transformer is now available in 🤗 Transformer, and it was trained on PubTables-1M. It's a large dataset for table extraction and structure recognition in unstructured documents.

Comments:
cc @albertvillanova the author would like to add this dataset to the hub: https://github.com/microsoft/table-transformer/issues/68#issuecomment-1319114621. Could you help him out?
Restore order of split names in dataset_info for canonical datasets
Opened by albertvillanova (closed)

After a bulk edit of canonical datasets to create the YAML `dataset_info` metadata, the split names were accidentally sorted alphabetically. See for example:
- https://huggingface.co/datasets/bc2gm_corpus/commit/2384629484401ecf4bb77cd808816719c424e57c

Note that this order is the one appearing in the preview of the datasets.

I'm making a bulk edit to align the order of the splits appearing in the metadata info with the order appearing in the loading script.

Related to:
- #5202

Comments:
The bulk edit is running...

See for example: 
- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2
- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1

TODO: Add "dataset_info" YAML metadata to:
- [x] "chr_en" has no metadata JSON file, nor "dataset_info" YAML tag in its card
  - Fixing PR: https://huggingface.co/datasets/chr_en/discussions/1 
- [x] "conll2000" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [x] "crime_and_punish" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [x] "dart" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [x] "iwslt2017" has no metadata JSON file, but it has "dataset_info" YAML tag in its card
- [ ] "mc4" has no metadata JSON file, nor "dataset_info" YAML tag in its card
- [ ] "the_pile" has no metadata JSON file, nor "dataset_info" YAML tag in its card
- [ ] "timit_asr" has no metadata JSON file, nor "dataset_info" YAML tag in its card

The bulk edit is finished.
Support for decoding Image/Audio types in map when format type is not default one
Opened by mariosasko (closed)

Add support for decoding the `Image`/`Audio` types in `map` for the formats (Numpy, TF, Jax, PyTorch) other than the default one (Python).

Additional improvements:
* make `Dataset`'s "iter" API  cleaner by removing `_iter` and replacing `_iter_batches` with `iter(batch_size)` (also implemented for `IterableDataset`)
* iterate over arrow tables in `map` to avoid `_getitem` calls, which are much slower than `__iter__`/`iter(batch_size)`, when the `format_type` is not Python
* fix `_iter_batches` (now named `iter`) when `drop_last_batch=True` and `pyarrow<=8.0.0` is installed
* lazily extract and decode arrow data in the default format 

TODO:
* [x] update the `iter` benchmark in the docs (the `BeamBuilder` cannot load the preprocessed datasets from our bucket, so wait for this to be fixed (cc @lhoestq))

Fix https://github.com/huggingface/datasets/issues/3992, fix https://github.com/huggingface/datasets/issues/3756

Comments:
_The documentation is not available anymore as the PR was closed or merged._

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5252). All of your documentation changes will be reflected on that endpoint.

Yes, if the image column is the first in the batch keys, it will decode the images because it reads the actual values. We could avoid this by checking the batch type, and if it's `LazyDict`, `num_examples` is equal to `len(batch.pa_table)`, which doesn't lead to decoding.

Good idea. This can be done in a subsequent PR btw, since it's out of scope of the original goal of this PR

Just fixed a small bug where it would show the pyarrow 10 warning about None -> empty lists conversions even with an Array2D with no nulls

Fixed another bug when your map function returns a mix of LazyDict or regular dict and added some tests
Docs are not generated after latest release
Opened by albertvillanova (closed)

After the latest `datasets` release version 0.7.0, the docs were not generated.

As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:

https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8

Related to:
- #5250

CC: @mishig25 

Comments:
After a discussion with @mishig25:
- He said that this action should be triggered if we call our release branch according to the regex `v*-release`, as transformers does
- I said that our procedure is different: our release branch is *temporary* and it is deleted just after the release PR is merged to main
  - Indeed the release tag is not yet created when we make the release PR (not event when this is merged to main), but when we make the Release itself.

I was thinking that maybe we could change the triggering event: use `release` instead of `push`.

What do you think, @huggingface/datasets?

Why is it an issue if our branch is temporary ?

He says not; but the branch has no tag yet; does the doc building require the tag? Or just the version number in `__init__.py` or setup.py?

It uses `module.__version__` (i.e. the one defined in `__init__.py`) - no need to have a tag

https://github.com/huggingface/doc-builder/blob/81575cf081964c30ea5fd39450f4820db963f18e/src/doc_builder/commands/build.py#L69

Thanks, @lhoestq.

@mishig25 has manually forced the generation of the docs, that are live for 2.7.0 version: https://huggingface.co/docs/datasets/v2.7.0/en/index 

Cool ! this can be closed then ?

I was waiting for #5250 to be merged to close this.

just to confirm, is there anything I need to do from my side ? Or is everything good here ?
Protect the main branch from inadvertent direct pushes
Opened by albertvillanova (closed)

We have decided to implement a protection mechanism in this repository, so that nobody (not even administrators) can inadvertently push accidentally directly to the main branch.

See context here:
- d7c942228b8dcf4de64b00a3053dce59b335f618

To do:
- [x] Protect main branch
  - Settings > Branches > Branch protection rules > main > Edit
    - [x] Check: Do not allow bypassing the above settings 
      - The above settings will apply to administrators and custom roles with the "bypass branch protections" permission.
    - [x] Additionally, uncheck: Require approvals [under "Require a pull request before merging", which was already checked]
      - Before, we could exceptionally merge a non-approved PR, using Administrator bypass
      - Now that Administrator bypass is no longer possible, we would always need an approval to be able to merge; and pull request authors cannot approve their own pull requests. This could be an inconvenient in some exceptional circumstances when an urgent fix is needed
      - Nevertheless, although it is no longer enforced, it is strongly recommended to merge PRs only if they have at least one approval
- [x] #5250
  - So that direct pushes to main branch are no longer necessary

Comments:
It seems all the tasks have been addressed, meaning this issue can be closed, no?
Complete doc migration
Opened by mishig25 (closed)

Reverts huggingface/datasets#5214

Everything is handled on the doc-builder side now 😊

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5248). All of your documentation changes will be reflected on that endpoint.

Thanks for the fix @mishig25.

I guess this is the reason why the docs are not generated for the latest release version 2.7.0? https://huggingface.co/docs/datasets/index