rIsHu009/github-issues-updated huggingface dataset:
Title: `load_dataset` has ~4 seconds of overhead for cached data
Author: davidgilbertson (NONE)
State: open
Created: 2023-02-02T23:34:50Z
Labels: enhancement

### Feature request

When loading a dataset that has been cached locally, the `load_dataset` function takes a lot longer than it should take to fetch the dataset from disk (or memory).
This is particularly noticeable for smaller datasets. For example, wikitext-2, comparing `load_data` (once cached) and `load_from_disk`, the `load_dataset` method takes 40 times longer.

⏱    4.84s ⮜ load_dataset
⏱    119ms ⮜ load_from_disk

### Motivation

I assume this is doing something like checking for a newer version.

If so, that's an age old problem: do you make the user wait _every single time they load from cache_ or do you do something like load from cache always, _then_ check for a newer version and alert if they have stale data. The decision usually revolves around what percentage of the time the data will have been updated, and how dangerous old data is.

For most datasets it's extremely unlikely that there will be a newer version on any given run, so 99% of the time this is just wasted time.

Maybe you don't want to make that decision for all users, but at least having the _option_ to not wait for checks would be an improvement.

### Your contribution

.

Comments:
Hi ! To skip the verification step that checks if newer data exist, you can enable offline mode with `HF_DATASETS_OFFLINE=1`.

Although I agree this step should be much faster for datasets hosted on the HF Hub - we could just compare the commit hash from the local data and the remote git repository. We're not been leveraging the git commit hashes, since the library was built before we even had git repositories for each dataset on HF.

Thanks @lhoestq, for memory when I recorded those times I had `HF_DATASETS_OFFLINE` set.
Title: TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset
Author: vmuel (NONE)
State: closed
Created: 2023-02-02T14:46:49Z

### Describe the bug

Hi, 

Thanks for the amazing work on the library!

**Describe the bug**

I think I might have noticed a small bug in the filter method. 
Having loaded a dataset using `load_dataset`,  when I try to filter out empty entries with `batched=True`, I get a TypeError.

### Steps to reproduce the bug

```
train_dataset = train_dataset.filter(
            function=lambda example: example["image"] is not None, 
            batched=True,
            batch_size=10)
```
Error message:

```
File .../lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
    476             validate_fingerprint(kwargs[fingerprint_name])
    478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
...
-> 5666 indices_array = [i for i, to_keep in zip(indices, mask) if to_keep]
   5667 if indices_mapping is not None:
   5668     indices_array = pa.array(indices_array, type=pa.uint64())

TypeError: 'bool' object is not iterable
```

**Removing batched=True allows to bypass the issue.** 

### Expected behavior

According to the doc, "[batch_size corresponds to the] number of examples per batch provided to function if batched = True", so we shouldn't need to remove the batchd=True arg?

source: https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.Dataset.filter

### Environment info

- `datasets` version: 2.9.0
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.9.10
- PyArrow version: 10.0.1
- Pandas version: 1.5.3

Comments:
Hi! Instead of a single boolean, your filter function should return an iterable (of booleans) in the batched mode like so:
```python
train_dataset = train_dataset.filter(
            function=lambda batch: [image is not None for image in batch["image"]], 
            batched=True,
            batch_size=10)
```

PS: You can make this operation much faster by operating directly on the arrow data to skip the decoding part:
```python
train_dataset = train_dataset.with_format("arrow")
train_dataset = train_dataset.filter(
            function=lambda table: table["image"].is_valid().to_pylist(), 
            batched=True,
            batch_size=100)
train_dataset = train_dataset.with_format(None)
```

Thank a lot!

I hit the same issue and the error message isn't really clear on what's going wrong. It might be helpful to update the docs with a batched example.
Title: Add a `reduce` method
Author: zhangir-azerbayev (NONE)
State: closed
Created: 2023-02-02T04:30:22Z
Labels: enhancement

### Feature request

Right now the `Dataset` class implements `map()` and `filter()`, but leaves out the third functional idiom popular among Python users: `reduce`. 

### Motivation

A `reduce` method is often useful when calculating dataset statistics, for example, the occurrence of a particular n-gram or the average line length of a code dataset.  

### Your contribution

I haven't contributed to `datasets` before, but I don't expect this will be too difficult, since the implementation will closely follow that of `map` and `filter`. I could have a crack over the weekend.

Comments:
Hi! Sure, feel free to open a PR, so we can see the API you have in mind.

I would like to give it a go! #self-assign

Closing as `Dataset.map` can be used instead (see https://github.com/huggingface/datasets/pull/5533#issuecomment-1440571658 and https://github.com/huggingface/datasets/pull/5533#issuecomment-1446403263)

Hello, is it possible for this issue/PR to be revisited? The problem with the alternatives presented (besides multiple map stages) is that they don't use the cache. A reduce operation is just as expensive as a map operation because it also goes over the entire dataset. It's equally worth caching.

Personally, I have a situation where I would need this and map is far from ideal. I'm working on updating a project of mine to use Huggingface Datasets, and I need to port the loop at https://github.com/colonelwatch/abstracts-search/blob/b90f31ee4cc6e394f829d3a6d9d0311ca390ada9/train.py#L112-L138. Please forgive the code style, here's what it does in English. I have a dataset of about 95 million embeddings, out of which 16384 is taken as a "query" set. For each embedding in the query set, I need to find the ten closest neighbors. These nearest neighbors are used to tune the parameters of a faiss index. The solution is to set up an "accumulator" comprising of the ten closest so far and their distances, then do a single scan over the 95 million (memmapped), then save the results of the "accumulator" for when I want to prototype another index.

The closest approximation to this is multiple map stages, but with such a large "accumulator" having the RAM to do a big batch size becomes critical. At a batch size of 1000, the intermediate accumulators would in theory be about 120 GB! That can be more if I want higher precision than float32. It would already be about the same size as the original embeddings. Using larger batch sizes puts strain on the RAM because I'd be dealing with batch_size x 16384 distances. The best I'd gotten with my RAM, single-threaded, was 65536, and for speed I had to use that thread to feed a GPU. It'd be better if I could use multiple threads to get high throughput instead, or even do all the work in CPU, but to fit the threads I'd need the batch size to be smaller.

All of this intermediate memory could be eliminated if there was a reduce operation.
Title: Update audio installation doc page
Author: polinaeterna (CONTRIBUTOR)
State: closed
Created: 2023-02-01T19:07:50Z
Labels: documentation

Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on all linux versions but there is a custom ubuntu repo for it, we have insctructions in the code: https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L327 
So we should update the doc page. But first investigate [this issue](5488).

Comments:
Totally agree, the docs should be in sync with our code.

Indeed to avoid confusing users, I think we should have updated the docs at the same time as this PR:
- #5167

@albertvillanova yeah sure I should have, but I forgot back then, sorry for that 😶

No, @polinaeterna, nothing to be sorry about.

My comment was for all of us datasets team, as a reminder: when making a PR, but also when reviewing some other's PR, we should not forget to update the corresponding docstring and doc pages. It is something we can improve if we help each other in reminding about it... :hugs: 

@polinaeterna I think we can close this issue now as we no longer use `torchaudio` for decoding.
Title: Push_to_hub in a pull request
Author: lhoestq (MEMBER)
State: closed
Created: 2023-02-01T18:32:14Z
Labels: enhancement

good first issue

Right now `ds.push_to_hub()` can push a dataset on `main` or on a new branch with `branch=`, but there is no way to open a pull request. Even passing `branch=refs/pr/x` doesn't seem to work: it tries to create a branch with that name

cc @nateraw 

It should be possible to tweak the use of `huggingface_hub` in `push_to_hub` to make it open a PR or push to an existing PR

Comments:
Assigned to myself and will get to it in the next week, but if someone finds this issue annoying and wants to submit a PR before I do, just ping me here and I'll reassign :). 

I would like to be assigned to this issue, @nateraw . #self-assign
Title: Adding `sep` to TextConfig 
Author: omar-araboghli (NONE)
State: open
Created: 2023-01-31T10:39:53Z

I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination with `sample_by='paragraph'` to parse a paragraph into an array for each column ? If so, I am happy to contribute!

## Environment
* `python 3.8.10`
* `datasets 2.9.0`

## Snippet of `train.txt`

```txt
Distribution	NN	O	O
and	NN	O	O
dynamics	NN	O	O
of	NN	O	O
electron	NN	O	B-RP
complexes	NN	O	I-RP
in	NN	O	O
cyanobacterial	NN	O	B-R
membranes	NN	O	I-R

The	NN	O	O
occurrence	NN	O	O
of	NN	O	O
prostaglandin	NN	O	B-R
F2α	NN	O	I-R
in	NN	O	O
Pharbitis	NN	O	B-R
seedlings	NN	O	I-R
grown	NN	O	O
under	NN	O	O
short	NN	O	B-P
days	NN	O	I-P
or	NN	O	I-P
days	NN	O	I-P

```

## Current Behaviour 
```python
# defining 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] here would fail with `ValueError: Length of names (4) does not match length of arrays (1)`
dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='line')
dataset['train']['tokens'][0]
>>> 'Distribution\tNN\tO\tO'
```

## Expected Behaviour / Suggestion
```python
# suppose we defined 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags']
dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='paragraph', sep='\t')
dataset['train']['tokens'][0]
>>> ['Distribution', 'and', 'dynamics', ... ]

dataset['train']['ner_tags'][0]
>>> ['O', 'O', 'O', ... ]
```

Comments:
Hi @omar-araboghli, thanks for your proposal.

Have you tried to use "csv" loader instead of "text"? That already has a `sep` argument.

Hi @albertvillanova, thanks for the quick response!

Indeed, I have been trying to use `csv` instead of `text`. However I am still not able to define range of rows as one sequence, that is achievable with passing `sample_by='paragraph'` to the `TextConfig`

For instance, the below code

```python
import datasets

dataset = datasets.load_dataset(
    path='csv',
    data_files={'train': TRAINING_SET_PATH},
    sep='\t',
    header=None,
    column_names=['tokens', 'pos_tags', 'chunk_tags', 'ner_tags']
)
```

leads to 

```python
dataset
>>> DatasetDict({
    train: Dataset({
        features: ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],
        num_rows: 62543
    })
})

dataset['train'][0]
>>> {'tokens': 'Distribution',
 'pos_tags': 'NN',
 'chunk_tags': 'O',
 'ner_tags': 'O'
}
```
Is there a way to deal with multiple csv rows as one dataset instance, where each column is a sequence of those rows ?
Title: Reload features from Parquet metadata
Author: lhoestq (MEMBER)
State: closed
Created: 2023-01-28T13:12:31Z
Labels: enhancement

good second issue

The idea would be to allow this :
```python 
ds.to_parquet("my_dataset/ds.parquet")
reloaded = load_dataset("my_dataset")
assert ds.features == reloaded.features 
```
And it should also work with Image and Audio types (right now they're reloaded as a dict type)

This can be implemented by storing and reading the feature types in the parquet metadata, as we do for arrow files.

Comments:
I'd be happy to have a look, if nobody else has started working on this yet @lhoestq. 

It seems to me that for the `arrow` format features are currently attached as metadata [in `datasets.arrow_writer`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/arrow_writer.py#L412)  and retrieved from the metadata at `load_dataset` time using [`datasets.features.features.from_arrow_schema`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/features/features.py#L1602). 

This will need to be replicated for `parquet` via calls to [this api](https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_metadata.html) from `io.parquet.ParquetWriter` and `io.parquet.ParquetReader` [respectively](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/io/parquet.py#L104).

Any other important considerations?


Thanks @MFreidank ! That's correct :)

Reading the metadata to infer the features can be ideally done in the `parquet.py` file in `packaged_builder` when a parquet file is read. You can cast the arrow table to the schema you get from the features.arrow_schema

#self-assign
Title: Unpin sqlalchemy once issue is fixed
Author: albertvillanova (MEMBER)
State: closed
Created: 2023-01-27T15:01:55Z

Once the source issue is fixed:
- pandas-dev/pandas#51015

we should revert the pin introduced in:
- #5476

Comments:
@albertvillanova It looks like that issue has been fixed so I made a PR to unpin sqlalchemy! 

The source issue:
- https://github.com/pandas-dev/pandas/issues/40686

has been fixed:
- https://github.com/pandas-dev/pandas/pull/48576

The fix was released yesterday (2023-04-03) only in `pandas-2.0.0`:
- https://github.com/pandas-dev/pandas/releases/tag/v2.0.0

but it will not be back-ported to `pandas-1`:
- https://github.com/pandas-dev/pandas/pull/48576#issuecomment-1466467159

Also note that `pandas-2.0.0` dropped support for Python 3.7:
- https://github.com/pandas-dev/pandas/issues/41678
- https://github.com/pandas-dev/pandas/pull/41989

Therefore, we cannot unpin `sqlalchemy` until we drop support for Python 3.7 (these Python users cannot use `pandas-2`).
Title: Dataset scan time is much slower than using native arrow
Author: jonny-cyberhaven (CONTRIBUTOR)
State: closed
Created: 2023-01-27T01:32:25Z

### Describe the bug

I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.

I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that explains this phenomenon?

### Steps to reproduce the bug

https://colab.research.google.com/drive/11EtHDaGAf1DKCpvYnAPJUW-LFfAcDzHY?usp=sharing

### Expected behavior

I expect scan times to be on par with using pyarrow directly.

### Environment info

standard colab environment

Comments:
Hi ! In your code you only iterate on the Arrow buffers - you don't actually load the data as python objects. For a fair comparison, you can modify your code using:
```diff
-     for _ in range(0, len(table), bsz):
-         _ = {k:table[k][_ : _ + bsz] for k in cols}
+     for _ in range(0, len(table), bsz):
+         _ = {k:table[k][_ : _ + bsz].to_pylist() for k in cols}
```

I re-ran your code and got a speed ratio of 1.00x and 1.02x

Ah I see, datasets is implicitly making this conversion. Thanks for pointing that out!

If it's not too much, I would also suggest updating some of your docs with the same `.to_pylist()` conversion in the code snippet that follows [here](https://huggingface.co/course/chapter5/4?fw=pt#:~:text=let%E2%80%99s%20run%20a%20little%20speed%20test%20by%20iterating%20over%20all%20the%20elements%20in%20the%20PubMed%20Abstracts%20dataset%3A).

This code snippet shows `datasets` code that reads the Arrow data as python objects already, there is no need to add to_pylist. Or were you thinking about something else ?
Title: Column project operation on `datasets.Dataset`
Author: daskol (CONTRIBUTOR)
State: closed
Created: 2023-01-26T21:47:53Z
Labels: duplicate

enhancement

### Feature request

There is no operation to select a subset of columns of original dataset. Expected API follows.
```python
a = Dataset.from_dict({
    'int': [0, 1, 2]
    'char': ['a', 'b', 'c'],
    'none': [None] * 3,
})

b = a.project('int', 'char')  # usually, .select()

print(a.column_names)  # stdout: ['int', 'char', 'none']
print(b.column_names)  # stdout: ['int', 'char']
```
Method project can easily accept not only column names (as a `str)` but univariant function applied to corresponding column as an example. Or keyword arguments can be used in order to rename columns in advance (see `pandas`, `pyspark`, `pyarrow`, and SQL)..

### Motivation

Projection is a typical operation in every data processing library. And it is a basic block of a well-known data manipulation language like SQL. Without this operation `datasets.Dataset` interface is not complete.

### Your contribution

Not sure. Some of my PRs are still open and some do not have any discussions.

Comments:
Hi ! This would be a nice addition indeed :) This sounds like a duplicate of https://github.com/huggingface/datasets/issues/5468

> Not sure. Some of my PRs are still open and some do not have any discussions.

Sorry to hear that, feel free to ping me on those PRs
Title: Allow opposite of remove_columns on Dataset and DatasetDict
Author: hollance (NONE)
State: closed
Created: 2023-01-26T12:28:09Z
Labels: enhancement

good first issue

### Feature request

In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:

```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)

gigaspeech = gigaspeech.remove_columns(columns_to_remove)
```

This kind of thing happens a lot when you don't need to keep all columns from the dataset. It would be more convenient (and less error prone) if you could just write:

```python
gigaspeech = gigaspeech.keep_columns(["text", "audio"])
```

Internally, `keep_columns` could still call `remove_columns`, but it expresses more clearly what the user's intent is.

### Motivation

Less code to write for the user of the dataset.

### Your contribution

-

Comments:
Hi! I agree it would be nice to have a method like that. Instead of `keep_columns`, we can name it `select_columns` to be more aligned with PyArrow's naming convention (`pa.Table.select`).

Hi, I am a newbie to open source and would like to contribute. @mariosasko can I take up this issue ?

Hey, I also want to work on this issue I am a newbie to open source. 

This sounds related to https://github.com/huggingface/datasets/issues/5474

I'm fine with `select_columns`, or we could also override `select` to also accept a list of columns maybe ?

@lhoestq, I am planning to add a member function to the dataset class to perform the selection operation. Do you think its the right way to proceed? or there is a better option ?

Unless @mariosasko thinks otherwise, I think it can go in `Dataset.select()` :)
Though some parameters like keep_in_memory, indices_cache_file_name or writer_batch_size wouldn't when selecting columns, so we would need to update the docstring as well

If someone wants to give it a shot, feel free to comment `#self-assign` and it will assign the issue to you.

Feel free to ping us here if you have questions or if we can help :)

I would rather have this functionality as a separate method. IMO it's always better to be explicit than to have an API where a single method can do different/uncorrelated things (somewhat reminds me of Pandas, and there is probably a good reason why PyArrow is more rigid in this aspect).

In the end I also think it would be nice to have it as a separate method, this way we can also have it for `IterableDataset` (which can't have `select` for indices)
Title: audiofolder creates empty dataset even though the dataset passed in follows the correct structure
Author: jcho19 (NONE)
State: closed
Created: 2023-01-26T01:45:45Z

### Describe the bug

The structure of my dataset folder called "my_dataset" is : data  metadata.csv
The data folder consists of all mp3 files and metadata.csv consist of file locations like 'data/...mp3 and transcriptions. There's 400+ mp3 files and corresponding transcriptions for my dataset.

When I run the following:
ds = load_dataset("audiofolder", data_dir="my_dataset")


I get:
Using custom data configuration default-...
Downloading and preparing dataset audiofolder/default to /...
Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]
Downloading data files: 0it [00:00, ?it/s]
Extracting data files: 0it [00:00, ?it/s]
Generating train split: 0 examples [00:00, ? examples/s]
Dataset audiofolder downloaded and prepared to /.... Subsequent calls will reuse this data.
  0%|          | 0/1 [00:00<?, ?it/s]
DatasetDict({
    train: Dataset({
        features: ['audio', 'transcription'],
        num_rows: 1
    })
})





### Steps to reproduce the bug

Create a dataset folder called 'my_dataset' with a subfolder called 'data' that has mp3 files. Also, create metadata.csv that has file locations like 'data/...mp3' and their corresponding transcription.

Run:
ds = load_dataset("audiofolder", data_dir="my_dataset")

### Expected behavior

It should generate a dataset with numerous rows. 

### Environment info

Run on Jupyter notebook

Comments:
Title: NonMatchingChecksumError for hendrycks_test
Author: sarahwie (NONE)
State: closed
Created: 2023-01-26T00:43:23Z

### Describe the bug

The checksum of the file has likely changed on the remote host.


### Steps to reproduce the bug

`dataset = nlp.load_dataset("hendrycks_test", "anatomy")`


### Expected behavior

no error thrown


### Environment info

- `datasets` version: 2.2.1
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1

Comments:
Thanks for reporting, @sarahwie.

Please note this issue was already fixed in `datasets` 2.6.0 version:
- #5040

If you update your `datasets` version, you will be able to load the dataset:
```
pip install -U datasets
```

Oops, missed that I needed to upgrade. Thanks!
Title: slice split while streaming
Author: SvenDS9 (NONE)
State: closed
Created: 2023-01-24T14:08:17Z

### Describe the bug

When using the `load_dataset` function with streaming set to True, slicing splits is apparently not supported.

Did I miss this in the documentation?

### Steps to reproduce the bug

`load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train[:3]")`

causes ValueError: Bad split: train[:3]. Available splits: ['train', 'test'] in builder.py, line 1213, in as_streaming_dataset

### Expected behavior

The first 3 entries of the dataset as a stream

### Environment info

- `datasets` version: 2.8.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.10.9
- PyArrow version: 10.0.1
- Pandas version: 1.5.2

Comments:
Hi! Yes, that's correct. When `streaming` is `True`, only split names can be specified as `split`, and for slicing, you have to use `.skip`/`.take` instead.

E.g. 
`load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train[:3]")`

rewritten with `.skip`/`.take`:
`load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train").take(3)`




Thank you for your quick response!
Title: ImageFolder BadZipFile: Bad offset for central directory
Author: hmartiro (NONE)
State: closed
Created: 2023-01-22T23:50:12Z

### Describe the bug

I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents        │
│                                                                                                  │
│   1350 │   │   # self.start_dir:  Position of start of central directory                         │
│   1351 │   │   self.start_dir = offset_cd + concat                                               │
│   1352 │   │   if self.start_dir < 0:                                                            │
│ ❱ 1353 │   │   │   raise BadZipFile("Bad offset for central directory")                          │
│   1354 │   │   fp.seek(self.start_dir, 0)                                                        │
│   1355 │   │   data = fp.read(size_cd)                                                           │
│   1356 │   │   fp = io.BytesIO(data)                                                             │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
BadZipFile: Bad offset for central directory                                                                            
Extracting data files:  35%|█████████████████▊                                 | 38572/110812 [00:10<00:20, 3576.26it/s]
```



### Steps to reproduce the bug

```
load_dataset(
    args.dataset_name,
    args.dataset_config_name,
    cache_dir=args.cache_dir,
),
```

### Expected behavior

loads the dataset

### Environment info

datasets==2.8.0
Python 3.10.8
Linux 129-146-3-202 5.15.0-52-generic #58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux

Comments:
Hi ! Could you share the full stack trace ? Which dataset did you try to load ?

it may be related to https://github.com/huggingface/datasets/pull/5640

The `BadZipFile` error means the ZIP file is corrupted, so I'm closing this issue as it's not directly related to `datasets`.

For others that find this issue following a `BadZipFile` error, I had the same problem because I had a file in a folder dataset `my-image.target` and the datasets library was incorrectly determining that the (PNG) file was a zip archive. When it tried to extract the file, this error occurred. 

Updating to `datasets==2.12.0` fixed the problem for me.
Title: test v0.12.0.rc0
Author: Wauplin (CONTRIBUTOR)
State: closed
Created: 2023-01-20T10:05:19Z

DO NOT MERGE.
Only to test the CI.

cc @lhoestq @albertvillanova

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

@Wauplin I was testing it in a dedicated branch without opening a PR: https://github.com/huggingface/datasets/commits/test-hfh-0.12.0rc0

Oops, sorry @albertvillanova. I thought for next time I'll start the CIs before pinging everyone.
I'm closing this one.

@Wauplin in your Slack message, you asked people from every major dependent library to check that our CI work. That is why I am checking it... :)

Also, I think for this purpose it is better to test it in a dedicated branch, rather than opening and closing a PR.

Yes, yes I know. Completely my fault on this one
Title: CI tests are broken: AttributeError: 'mappingproxy' object has no attribute 'target'
Author: albertvillanova (MEMBER)
State: closed
Created: 2023-01-20T10:03:10Z
Labels: bug

CI tests are broken, raising `AttributeError: 'mappingproxy' object has no attribute 'target'`. See: https://github.com/huggingface/datasets/actions/runs/3966497597/jobs/6797384185
```
...
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - AttributeError: 'mappingproxy' object has no attribute 'target'
===== 2076 passed, 19 skipped, 15 warnings, 47 errors in 115.54s (0:01:55) =====
```

Comments:
Title: OneDrive Integrations  with HF Datasets 
Author: Mohammed20201991 (NONE)
State: closed
Created: 2023-01-19T23:12:08Z
Labels: enhancement

### Feature request

First of all , I would like to thank all community who are developed DataSet storage and make it free available  
How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section.
For example, if  I have **50GB** on my **Onedrive** account and I want to move between drive and Hugging face repo or vis versa 

### Motivation

make the dataset section more flexible with other possible storage 
like the integration between Google Collab and Google drive the storage

### Your contribution

Can be done using Hugging face CLI

Comments:
Hi! 

We use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co/docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).

[`gdrivefs`](https://github.com/fsspec/gdrivefs) makes it possible to use Google Drive as a storage service in Datasets, but this is not the case for OneDrive, since its[ Python SDK](https://github.com/OneDrive/onedrive-sdk-python) is not integrated with `fsspec`. Can you please request the integration with `fsspec` in their repo to address this limitation?

I'm closing this issue as implementing a fsspec-compliant OneDrive filesystem is not our responsibility.
Title: [dataset request] Add Common Voice 12.0
Author: MohammedRakib (NONE)
State: closed
Created: 2023-01-18T13:07:05Z
Labels: enhancement

### Feature request

Please add the common voice 12_0 datasets. Apart from English, a significant amount of audio-data has been added to the other minor-language datasets.

### Motivation

The dataset link:
https://commonvoice.mozilla.org/en/datasets

Comments:
@polinaeterna any tentative date on when the Common Voice 12.0 dataset will be added ?

This dataset is now hosted on the Hub here: https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0
Title: Can't load png dataset with 4 channel (RGBA)
Author: WiNE-iNEFF (NONE)
State: closed
Created: 2023-01-17T18:22:27Z

I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand.![Screenshot_20230117_212213.jpg](https://user-images.githubusercontent.com/41611046/212980147-9aa68e30-76e9-4b61-a937-c2fdabd56564.jpg)

Comments:
Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.



> Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.
> 
> 

I have only 1 folder that I use in the load_dataset function with the name "IMGDATA" and all my 9000 images are located in this folder.
`
from datasets import load_dataset

dataset = load_dataset("IMGDATA")
`
At the same time, using another data set with images consisting of 3 RGB channels, everything works

Okay, I figured out what was wrong. When uploading my dataset via Google Drive, the images broke and Pillow couldn't open them. As a result, I solved the problem by downloading the ZIP archive
Title: Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage
Author: DanielYang59 (NONE)
State: closed
Created: 2023-01-17T10:04:16Z

### Describe the bug

In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:

> Using take (or skip) prevents future calls to shuffle from shuffling the dataset shards order, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer. Therefore it is advised to shuffle the dataset before splitting using take or skip. See more details in the [Shuffling the dataset: shuffle](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#iterable-dataset-shuffling) section.`
>> \# You can also create splits from a shuffled dataset
>> train_dataset = shuffled_dataset.skip(1000)
>> eval_dataset = shuffled_dataset.take(1000)


Where the shuffled dataset comes from:
`shuffled_dataset = dataset.shuffle(buffer_size=10_000, seed=42)`

At least in Tensorflow 2.9/2.10/2.11, [docs](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) states the `reshuffle_each_iteration` argument is `True` by default. This means the dataset would be shuffled after each epoch, and as a result **the validation data would leak into training test**.






### Steps to reproduce the bug

N/A

### Expected behavior

The `reshuffle_each_iteration` argument should be set to `False`.

### Environment info

Tensorflow 2.9/2.10/2.11

Comments:
Just for your information, Tensorflow confirmed this issue [here.](https://github.com/tensorflow/tensorflow/issues/59279)

Thanks for reporting, @HaoyuYang59.

Please note that these are different "dataset" objects: our docs refer to Hugging Face `datasets.Dataset` and not to TensorFlow `tf.data.Dataset`.

Our `datasets.Dataset.shuffle` method does not have a `reshuffle_each_iteration` argument. Therefore, I would say the statement in our docs is True because they refer to `datasets.Dataset.shuffle`, `datasets.Dataset.skip` and `datasets.Dataset.take`.

I think this issue is restricted to TensorFlow dataset, and this would be addressed by them in the issue you opened in their repo: https://github.com/tensorflow/tensorflow/issues/59279

Also note that you are referring to an outdated documentation page: datasets 1.10.2 version

Current datasets version is 2.8.0 and the corresponding documentation page is: https://huggingface.co/docs/datasets/stream#split-dataset

Hi @albertvillanova thanks for your reply and your explaination here. 

Sorry for the confusion as I'm not actually a user of your repo and I just happen to find the thread by Google (and didn't read carefully).

Great to know that and you made everything very clear now.

Thanks for your time and sorry for the consusion.

Wishing you a wonderful time. 

Regards
Title: sample_dataset module not found
Author: nickums (NONE)
State: closed
Created: 2023-01-17T09:57:54Z


Comments:
Hi! Can you describe what the actual error is?

working on the setfit example script

   from setfit import SetFitModel, SetFitTrainer, sample_dataset

ImportError: cannot import name 'sample_dataset' from 'setfit' (C:\Python\Python38\lib\site-packages\setfit\__init__.py)

 apart from that, I also had to hack these loads to import thses modules:
    from datasets.load import load_dataset                        
    from datasets.arrow_dataset import Dataset
    from datasets.dataset_dict import DatasetDict

Hi! This issue is related to the [SetFit](https://github.com/huggingface/setfit) project, so can you please open it there?
Title: Support latest Docker image in CI benchmarks
Author: albertvillanova (MEMBER)
State: closed
Created: 2023-01-17T09:06:08Z
Labels: enhancement

Once we find out the root cause of:
- #5431 

we should revert the temporary pin on the Docker image version introduced by:
- #5432

Comments:
Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559; should be fixed with [v0.18.17](https://github.com/iterative/cml/releases/tag/v0.18.17).

[^1]: More or less, see https://github.com/yargs/yargs/issues/873.

Opened https://github.com/huggingface/datasets/pull/5436 unpinning again the container image.

Hi @0x2b3bfa0, thanks a lot for the investigation, the context about the the root cause and for fixing it!!

We are reviewing your PR to unpin the container image.
Title: CI benchmarks are broken: Unknown arguments: runnerPath, path
Author: albertvillanova (MEMBER)
State: closed
Created: 2023-01-17T06:49:57Z
Labels: maintenance

Our CI benchmarks are broken, raising `Unknown arguments` error: https://github.com/huggingface/datasets/actions/runs/3932397079/jobs/6724905161
```
Unknown arguments: runnerPath, path
```

Stack trace:
```
100%|██████████| 500/500 [00:01<00:00, 338.98ba/s]
Updating lock file 'dvc.lock'

To track the changes with git, run:

	git add dvc.lock

To enable auto staging, run:

	dvc config core.autostage true
Use `dvc push` to send your updates to remote storage.
cml send-comment <markdown file>

Global Options:
  --log                    Logging verbosity
          [string] [choices: "error", "warn", "info", "debug"] [default: "info"]
  --driver                 Git provider where the repository is hosted
    [string] [choices: "github", "gitlab", "bitbucket"] [default: infer from the
                                                                    environment]
  --repo                   Repository URL or slug
                                  [string] [default: infer from the environment]
  --driver-token, --token  CI driver personal/project access token (PAT)
                                  [string] [default: infer from the environment]
  --help                   Show help                                   [boolean]

Options:
  --target                    Comment type (`commit`, `pr`, `commit/f00bar`,
                              `pr/42`, `issue/1337`),default is automatic (`pr`
                              but fallback to `commit`).                [string]
  --watch                     Watch for changes and automatically update the
                              comment                                  [boolean]
  --publish                   Upload any local images found in the Markdown
                              report                   [boolean] [default: true]
  --publish-url               Self-hosted image server URL
                                     [string] [default: "https://asset.cml.dev/"]
  --publish-native, --native  Uses driver's native capabilities to upload assets
                              instead of CML's storage; not available on GitHub
                                                                       [boolean]
  --watermark-title           Hidden comment marker (used for targeting in
                              subsequent `cml comment update`); "{workflow}" &
                              "{run}" are auto-replaced   [string] [default: ""]

Unknown arguments: runnerPath, path
Error: Process completed with exit code 1.
```

Issue reported to iterative/cml:
- iterative/cml#1319

Comments:
Title: Support Apache Beam >= 2.44.0
Author: albertvillanova (MEMBER)
State: closed
Created: 2023-01-17T06:42:12Z
Labels: enhancement

Once we find out the root cause of:
- #5426 

we should revert the temporary pin on apache-beam introduced by:
- #5429

Comments:
Some of the shard files now have 0 number of rows.

We have opened an issue in the Apache Beam repo:
- https://github.com/apache/beam/issues/25041
Title: Load/Save FAISS index using fsspec
Author: Dref360 (CONTRIBUTOR)
State: closed
Created: 2023-01-16T16:08:12Z
Labels: enhancement

### Feature request

From what I understand `faiss` already support this [link](https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning#generic-io-support)

I would like to use a stream as input to `Dataset.load_faiss_index` and `Dataset.save_faiss_index`.

### Motivation

In my case, I'm saving faiss index in cloud storage and use `fsspec` to load them. It would be ideal if I could send the stream directly instead of copying the file locally (or mounting the bucket) and then load the index.

### Your contribution

I can submit the PR

Comments:
Hi! Sure, feel free to submit a PR. Maybe if we want to be consistent with the existing API, it would be cleaner to directly add support for `fsspec` paths in `Dataset.load_faiss_index`/`Dataset.save_faiss_index` in the same manner as it was done in `Dataset.load_from_disk`/`Dataset.save_to_disk`.

That's a great idea! I'll do that instead.
Title: Unable to download dataset id_clickbait
Author: ilos-vigil (NONE)
State: closed
Created: 2023-01-16T16:05:36Z

### Describe the bug

I tried to download dataset `id_clickbait`, but receive this error message.

```
FileNotFoundError: Couldn't find file at https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/k42j7x2kpn-1.zip
```

When i open the link using browser, i got this XML data.

```xml
<?xml version="1.0" encoding="UTF-8"?>
<Error><Code>NoSuchBucket</Code><Message>The specified bucket does not exist</Message><BucketName>md-datasets-cache-zipfiles-prod</BucketName><RequestId>NVRM6VEEQD69SD00</RequestId><HostId>W/SPDxLGvlCGi0OD6d7mSDvfOAUqLAfvs9nTX50BkJrjMny+X9Jnqp/Li2lG9eTUuT4MUkAA2jjTfCrCiUmu7A==</HostId></Error>
```

### Steps to reproduce the bug

Code snippet:

```
from datasets import load_dataset

load_dataset('id_clickbait', 'annotated')
load_dataset('id_clickbait', 'raw')
```

Link to Kaggle notebook: https://www.kaggle.com/code/ilosvigil/bug-check-on-id-clickbait-dataset

### Expected behavior

Successfully download and load `id_newspaper` dataset.

### Environment info

- `datasets` version: 2.8.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5

Comments:
Thanks for reporting, @ilos-vigil.

We have transferred this issue to the corresponding dataset on the Hugging Face Hub: https://huggingface.co/datasets/id_clickbait/discussions/1
Title: When applying `ReadInstruction` to custom load it's not DatasetDict but list of Dataset?
Author: macabdul9 (NONE)
State: closed
Created: 2023-01-16T06:54:28Z

### Describe the bug

I am loading datasets from custom `tsv` files stored locally and applying split instructions for each split. Although the ReadInstruction is being applied correctly and I was expecting it to be `DatasetDict` but instead it is a list of `Dataset`. 

### Steps to reproduce the bug

Steps to reproduce the behaviour:

1. Import 
   `from datasets import load_dataset, ReadInstruction`
2. Instruction to load the dataset
  ```
instructions = [
      ReadInstruction(split_name="train", from_=0, to=10, unit='%', rounding='closest'),
      ReadInstruction(split_name="dev", from_=0, to=10, unit='%', rounding='closest'),
      ReadInstruction(split_name="test", from_=0, to=5, unit='%', rounding='closest')
  ]
```
3. Load 
`dataset = load_dataset('csv', data_dir="data/", data_files={"train":"train.tsv", "dev":"dev.tsv", "test":"test.tsv"}, delimiter="\t", split=instructions)`


### Expected behavior

**Current behaviour**

![Screenshot from 2023-01-16 10-45-27](https://user-images.githubusercontent.com/25720695/212614754-306898d8-8c27-4475-9bb8-0321bd939561.png)
:
 
**Expected behaviour**

![Screenshot from 2023-01-16 10-45-42](https://user-images.githubusercontent.com/25720695/212614813-0d336bf7-5266-482e-bb96-ef51f64de204.png)


### Environment info

``datasets==2.8.0
``
`Python==3.8.5
`
`Platform - Ubuntu 20.04.4 LTS`

Comments:
Hi! You can get a `DatasetDict` if you pass a dictionary with read instructions as follows:
```python
instructions = [
    ReadInstruction(split_name="train", from_=0, to=10, unit='%', rounding='closest'),
    ReadInstruction(split_name="dev", from_=0, to=10, unit='%', rounding='closest'),
    ReadInstruction(split_name="test", from_=0, to=5, unit='%', rounding='closest')
]

dataset = load_dataset('csv', data_dir="data/", data_files={"train":"train.tsv", "dev":"dev.tsv", "test":"test.tsv"}, delimiter="\t", split={inst.split_name: inst for inst in instructions})
```
Title: Support case-insensitive Hub dataset name in load_dataset
Author: severo (COLLABORATOR)
State: closed
Created: 2023-01-13T13:07:07Z
Labels: enhancement

### Feature request

The dataset name on the Hub is case-insensitive (see https://github.com/huggingface/moon-landing/pull/2399, internal issue), i.e., https://huggingface.co/datasets/GLUE redirects to https://huggingface.co/datasets/glue.

Ideally, we could load the glue dataset using the following:

```
from datasets import load_dataset
load_dataset('GLUE', 'cola')
```

It breaks because the loading script `GLUE.py` does not exist (`glue.py` should be selected instead).

Minor additional comment: in other cases without a loading script, we can load the dataset, but the automatically generated config name depends on the casing:
- `load_dataset('severo/danish-wit')` generates the config name `severo--danish-wit-e6fda5b070deb133`, while
- `load_dataset('severo/danish-WIT')` generates the config name `severo--danish-WIT-e6fda5b070deb133`

### Motivation

To follow the same UX on the Hub and in the datasets library.

### Your contribution

...

Comments:
Closing as case-insensitivity should be only for URL redirection on the Hub. In the APIs, we will only support the canonical name (https://github.com/huggingface/moon-landing/pull/2399#issuecomment-1382085611)
Title: label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator
Author: CreatixEA (NONE)
State: closed
Created: 2023-01-13T09:40:07Z

### Describe the bug

When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default column name is `label` if binary or `label_ids` if multi-class problem.

It is required to rename the column accordingly to the expected name : `label` or `label_ids`

### Steps to reproduce the bug

```python
from datasets import TextClassification, AutoTokenized, DataCollatorWithPadding
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0'))
print(ds_prepared)

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
ds_tokenized = ds_prepared.map(lambda x: tokenizer(x['text'], truncation=True), batched=True)
print(ds_tokenized)

data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")

tf_data = model.prepare_tf_dataset(ds_tokenized, shuffle=True, batch_size=16, collate_fn=data_collator)

print(tf_data)
```

### Expected behavior

Without renaming the the column, the  target column is not in the final tf_data since it is not in the column name expected by the data_collator.

To correct this, we have to rename the column:

```python
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0')).rename_column('labels', 'label')
```

### Environment info

- `datasets` version: 2.8.0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.1
- Pandas version: 1.5.2

- `transformers` version: 4.26.0.dev0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.11.1
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): 2.11.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>

Comments:
Hi! Thanks for pointing out this inconsistency. Changing the default value at this point is probably not worth it, considering we've started discussing the state of the task API internally - we will most likely deprecate the current one and replace it with a more robust solution that relies on the `train_eval_index` field stored in the YAML section of the dataset cards.

The task templates API has been deprecated (will be removed in version 3.0), so I'm closing this issue.