ahmedfarazsyk/datasets-github-issues huggingface dataset:
Error while following docs to load the `ted_talks_iwslt` dataset
Author: ekdnam
State: closed
Labels: name: dataset bug

I am currently trying to load the `ted_talks_iwslt` dataset into google colab.

The [docs](https://huggingface.co/datasets/ted_talks_iwslt) mention the following way of doing so.

```python
dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014")
```

Executing it results in the error attached below.

```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-7dcc67154ef9> in <module>()
----> 1 dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014")

4 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)
    730         hash=hash,
    731         features=features,
--> 732         **config_kwargs,
    733     )
    734 

/usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, writer_batch_size, *args, **kwargs)
    927 
    928     def __init__(self, *args, writer_batch_size=None, **kwargs):
--> 929         super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
    930         # Batch size used by the ArrowWriter
    931         # It defines the number of samples that are kept in memory before writing them

/usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)
    241             name,
    242             custom_features=features,
--> 243             **config_kwargs,
    244         )
    245 

/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
    337             if "version" not in config_kwargs and hasattr(self, "VERSION") and self.VERSION:
    338                 config_kwargs["version"] = self.VERSION
--> 339             builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs)
    340 
    341         # otherwise use the config_kwargs to overwrite the attributes

/root/.cache/huggingface/modules/datasets_modules/datasets/ted_talks_iwslt/024d06b1376b361e59245c5878ab8acf9a7576d765f2d0077f61751158e60914/ted_talks_iwslt.py in __init__(self, language_pair, year, **kwargs)
    219             description=description,
    220             version=datasets.Version("1.1.0", ""),
--> 221             **kwargs,
    222         )
    223 

TypeError: __init__() got multiple values for keyword argument 'version'
```

How to resolve this? 

PS: Thanks a lot @huggingface team for creating this great library!

Comments: @skyprince999 as you authored the PR for this dataset, any comments?

This has been fixed in #2064  by @mariosasko (thanks again !)

The fix is available on the master branch and we'll do a new release very soon :)
Is it possible to convert a `tfds` to HuggingFace `dataset`?
Author: abarbosa94
State: closed

I was having some weird bugs with `C4`dataset version of HuggingFace, so I decided to try to download `C4`from `tfds`. I would like to know if it is possible to convert a tfds dataset to HuggingFace dataset format :)

I can also open a new issue reporting the bug I'm receiving with `datasets.load_dataset('c4','en')` in the future if you think that it would be useful.

Thanks!


Comments: Hi! You can either save the TF dataset to one of the formats supported by datasets (`parquet`, `csv`, `json`, ...) or pass a generator function to `Dataset.from_generator` that yields its examples.
is there a way to override a dataset object saved with save_to_disk?
Author: shamanez
State: closed

At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file.  Is there a way to override such an object? 

Comments: Hi
You can rename the arrow file and update the name in `state.json`

I tried this way, but when there is a mapping process to the dataset, it again uses a random cache name. atm, I am trying to use the following method by setting an exact cache file,

```
            dataset_with_embedding =csv_dataset.map(
                partial(self.embed, ctx_encoder=ctx_encoder, ctx_tokenizer=self.context_tokenizer),
                batched=True,
                batch_size=1,
                features=new_features,
                cache_file_name=cache_arrow_path,
                load_from_cache_file=False
            )
```
So here we set a cache_file_name , after this it uses the same  file name when saving again and again. 

I'm not sure I understand your issue, can you elaborate ?

`cache_file_name` is indeed an argument you can set to specify the cache file that will be used for the processed dataset. By default the file is named with something like `cache-<fingerprint>.arrow` where the fingerprint is a hash.

Let's say I am updating a set of embedding in a dataset that is around 40GB inside a training loop every 500 steps (Ex: calculating the embeddings in updated ctx_encoder in RAG and saving it to the passage path).  So when we use **dataset_object.save_to_disk('passage_path_directory')** it will save the new dataset object every time with a random file name, especially when we do some transformations to dataset objects such as map or shards. This way, we keep collecting unwanted files that will eventually eat up all the disk space. 

But if we can save the dataset object every time by a single name like **data_shard_1.arrow**, it will automatically remove the previous file and save the new one in the same directory.  I found the above-mentioned code snippet useful to complete this task. 

Is this clear?
Could not find file for ZEST dataset
Author: bhadreshpsavani
State: closed
Labels: name: dataset bug

I am trying to use zest dataset from Allen AI using below code in colab,
```
!pip install -q datasets
from datasets import load_dataset
dataset = load_dataset("zest")
```

I am getting the following error,
```
Using custom data configuration default

Downloading and preparing dataset zest/default (download: 5.53 MiB, generated: 19.96 MiB, post-processed: Unknown size, total: 25.48 MiB) to /root/.cache/huggingface/datasets/zest/default/0.0.0/1f7a230fbfc964d979bbca0f0130fbab3259fce547ee758ad8aa4f9c9bec6cca...
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-6-18dbbc1a4b8a> in <module>()
      1 from datasets import load_dataset
      2 
----> 3 dataset = load_dataset("zest")

9 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
    612             )
    613         elif response is not None and response.status_code == 404:
--> 614             raise FileNotFoundError("Couldn't find file at {}".format(url))
    615         _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
    616         raise ConnectionError("Couldn't reach {}".format(url))

FileNotFoundError: Couldn't find file at https://ai2-datasets.s3-us-west-2.amazonaws.com/zest/zest.zip
```

Comments: The zest dataset url was changed (allenai/zest#3) and #2057 should resolve this.

This has been fixed in #2057 by @matt-peters (thanks again !)

The fix is available on the master branch and we'll do a new release very soon :)

Thanks @lhoestq and @matt-peters 

I am closing this issue since its fixed!
Timit_asr dataset repeats examples
Author: fermaat
State: closed

Summary

When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same
Steps to reproduce

As an example, on this code there is the text from the training part:

Code snippet:
```
from datasets import load_dataset, load_metric

timit = load_dataset("timit_asr")
timit['train']['text']
#['Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
```
The same behavior happens for other columns

Expected behavior:

Different info on the actual timit_asr dataset

Actual behavior:

When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same. I've checked datasets 1.3 and the rows are different
Debug info

    Streamlit version: (get it with $ streamlit version)
    Python version: Python 3.6.12
    Using Conda? PipEnv? PyEnv? Pex? Using pip
    OS version: Centos-release-7-9.2009.1.el7.centos.x86_64

Additional information

You can check the same behavior on https://huggingface.co/datasets/viewer/?dataset=timit_asr

Comments: Hi,

this was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: 
```bash
pip install git+https://github.com/huggingface/datasets
```

Ty!
Build custom dataset to fine-tune Wav2Vec2
Author: Omarnabk
State: closed
Labels: name: dataset request

Thank you for your recent tutorial on how to finetune Wav2Vec2 on a custom dataset. The example you gave here (https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) was on the CommonVoice dataset. However, what if I want to load my own dataset?  I have a manifest (transcript and their audio files) in a JSON file. 


Comments: @lhoestq - We could simply use the "general" json dataset for this no? 

Sure you can use the json loader
```python
data_files = {"train": "path/to/your/train_data.json", "test": "path/to/your/test_data.json"}
train_dataset = load_dataset("json", data_files=data_files, split="train")
test_dataset = load_dataset("json", data_files=data_files, split="test")
```

You just need to make sure that the data contain the paths to the audio files.
If not, feel free to use `.map()` to add them.

Many thanks! that was what I was looking for.
github is not always available - probably need a back up
Author: stas00
State: closed

Yesterday morning github wasn't working:

```
:/tmp$ wget https://raw.githubusercontent.com/huggingface/datasets/1.4.1/metrics/sacrebleu/sacrebleu.py--2021-03-12 18:35:59-- https://raw.githubusercontent.com/huggingface/datasets/1.4.1/metrics/sacrebleu/sacrebleu.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 500 Internal Server Error
2021-03-12 18:36:11 ERROR 500: Internal Server Error.
```

Suggestion: have a failover system and replicate the data on another system and reach there if gh isn't reachable? perhaps gh can be a master and the replicate a slave - so there is only one true source.
ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk
Author: simonschoe
State: closed

Hi there,

I am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):
```python
concatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])
```
Yielding the following error:
```python
ValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.
However datasets' indices [1] come from memory and datasets' indices [0] come from disk.
```
Been trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...

`load_from_disk(PATH_DATA_CLS_A)['train']` yields:
```python
Dataset({
    features: ['labels', 'text'],
    num_rows: 785
})
```
`load_from_disk(PATH_DATA_CLS_B)['train']` yields:
```python
Dataset({
    features: ['labels', 'text'],
    num_rows: 3341
})
```

Comments: Hi ! To help me understand the situation, can you print the values of  `load_from_disk(PATH_DATA_CLS_A)['train']._indices_data_files` and `load_from_disk(PATH_DATA_CLS_B)['train']._indices_data_files` ?
They should both have a path to an arrow file

Also note that from #2025 concatenating datasets will no longer have such restrictions.

Sure, thanks for the fast reply!

For dataset A: `[{'filename': 'drive/MyDrive/data_target_task/dataset_a/train/cache-4797266bf4db1eb7.arrow'}]`
For dataset B: `[]`

No clue why for B it returns nothing. `PATH_DATA_CLS_B` is exactly the same in `save_to_disk` and `load_from_disk`... Also I can verify that the folder physically exists under 'drive/MyDrive/data_target_task/dataset_b/'

In the next release you'll be able to concatenate any kinds of dataset (either from memory or from disk).

For now I'd suggest you to flatten the indices of the A and B datasets. This will remove the indices mapping and you will be able to concatenate them. You can flatten the indices with
```python
dataset = dataset.flatten_indices()
```

Indeed this works. Not the most elegant solution, but it does the trick. Thanks a lot!
outdated dataset_infos.json might fail verifications
Author: songfeng
State: closed

The [doc2dial/dataset_infos.json](https://github.com/huggingface/datasets/blob/master/datasets/doc2dial/dataset_infos.json) is outdated. It would fail data_loader when verifying download checksum etc..

Could you please update this file or point me how to update this file?

Thank you.

Comments: Hi ! Thanks for reporting.

To update the dataset_infos.json you can run:
```
datasets-cli test ./datasets/doc2dial --all_configs --save_infos --ignore_verifications
```

Fixed by #2041, thanks again @songfeng !
Cannot load wikitext
Author: Gpwner
State: closed

when I execute these codes
```
>>> from datasets import load_dataset
>>> test_dataset = load_dataset("wikitext")
```

I got an error,any help?

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
  File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
    local_path = cached_path(file_path, download_config=download_config)
  File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
    use_etag=download_config.use_etag,
  File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
    raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/wikitext/wikitext.py
```

Comments: Solved!
Use Arrow filtering instead of writing a new arrow file for Dataset.filter
Author: lhoestq
State: closed
Labels: name: enhancement

Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.

Using a mask directly on the arrow table doesn't do any read or write operation therefore it's significantly quicker.

I think there are two cases:
- if the dataset doesn't have an indices mapping, then one can simply use the arrow filtering on the main arrow table `dataset._data.filter(...)`
- if the dataset an indices mapping, then the mask should be applied on the indices mapping table `dataset._indices.filter(...)`

The indices mapping is used to map between the idx at `dataset[idx]` in `__getitem__` and the idx in the actual arrow table.

The new filter method should therefore be faster, and allow users to pass either a filtering function (that returns a boolean given an example), or directly a mask.

Feel free to discuss this idea in this thread :)

One additional note: the refactor at #2025 would make all the pickle-related stuff work directly with the arrow filtering, so that we only need to change the Dataset.filter method without having to deal with pickle.

cc @theo-m @gchhablani 

related issues: #1796 #1949 

Comments: Actually table.filter returns a new table in memory, which can fill users RAM.

Therefore it's not a good solution if we want to keep supporting bigger than RAM datastes
wikipedia.py generator that extracts XML doesn't release memory
Author: miyamonz
State: closed

I tried downloading Japanese wikipedia, but it always failed because of out of memory maybe.

I found that the generator function that extracts XML data in wikipedia.py doesn't release memory in the loop.

https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L464-L502

`root.clear()` intend to clear memory, but it doesn't.
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L490
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L494
I replaced them with `elem.clear()`, then it seems to work correctly.

here is the notebook to reproduce it.
https://gist.github.com/miyamonz/dc06117302b6e85fa51cbf46dde6bb51#file-xtract_content-ipynb

Comments: Hi @miyamonz 
Thanks for investigating this issue, good job !
It would be awesome to integrate your fix in the library, could you open a pull request ?

OK! I'll send it later.
ValueError when rename_column on splitted dataset
Author: simonschoe
State: closed

Hi there,
I am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so:

```python
split = {
    'train': ReadInstruction('train', to=90, unit='%'),
    'test': ReadInstruction('train', from_=-10, unit='%')
}

dataset = load_dataset(
    path='csv',               # use 'text' loading script to load from local txt-files
    delimiter='\t',           # xxx
    data_files=text_files,    # list of paths to local text files
    split=split,              # xxx
)

dataset
```

Part of output:
```python
DatasetDict({
    train: Dataset({
        features: ['sentence', 'sentiment'],
        num_rows: 900
    })
    test: Dataset({
        features: ['sentence', 'sentiment'],
        num_rows: 100
    })
})
```
Afterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however:
```python
dataset['train'].rename_column('sentence', 'text')
```
```python
/usr/local/lib/python3.7/dist-packages/datasets/splits.py in __init__(self, name)
    353         for split_name in split_names_from_instruction:
    354             if not re.match(_split_re, split_name):
--> 355                 raise ValueError(f"Split name should match '{_split_re}'' but got '{split_name}'.")
    356 
    357     def __str__(self):

ValueError: Split name should match '^\w+(\.\w+)*$'' but got 'ReadInstruction('.
```
In particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split.

Thanks in advance! :)

Comments: Hi,

This is a bug so thanks for reporting it. `Dataset.__setstate__`  is the problem, which is called when `Dataset.rename_column` tries to copy the dataset with `copy.deepcopy(self)`. This only happens if the `split` arg in `load_dataset` was defined as `ReadInstruction`.

To overcome this issue, use the named splits API (for now):
```python
train_ds, test_ds = load_dataset(
    path='csv',               
    delimiter='\t',          
    data_files=text_files,    
    split=['train[:90%]', 'train[-10%:]'],
)

train_ds = train_ds.rename_column('sentence', 'text')
```

This has been fixed in #2043 , thanks @mariosasko 
The fix is available on master and we'll do a new release soon :)

feel free to re-open if you still have issues
Interactively doing  save_to_disk and load_from_disk corrupts the datasets object?
Author: shamanez
State: closed

 dataset_info.json file saved after using  save_to_disk gets corrupted as follows. 
 
 
![image](https://user-images.githubusercontent.com/16892570/110568474-ed969880-81b7-11eb-832f-2e5129656016.png)

Is there a way to disable the cache that will save to /tmp/huggiface/datastes ? 
I have a feeling there is a serious issue with cashing.

Comments: Hi,

Can you give us a minimal reproducible example? This [part](https://huggingface.co/docs/datasets/master/processing.html#controling-the-cache-behavior) of the docs explains how to control caching.
No upstream branch
Author: theo-m
State: closed
Labels: name: documentation

Feels like the documentation on adding a new dataset is outdated?

https://github.com/huggingface/datasets/blob/987df6b4e9e20fc0c92bc9df48137d170756fd7b/ADD_NEW_DATASET.md#L49-L54

There is no upstream branch on remote. 

Comments: What's the issue exactly ?

Given an `upstream` remote repository with url `https://github.com/huggingface/datasets.git`, you can totally rebase from `upstream/master`.

It's mentioned at the beginning how to add the `upstream` remote repository

https://github.com/huggingface/datasets/blob/987df6b4e9e20fc0c92bc9df48137d170756fd7b/ADD_NEW_DATASET.md#L10-L14

~~What difference is there with the default `origin` remote that is set when you clone the repo?~~ I've just understood that this applies to **forks** of the repo 🤡
Local testing fails
Author: theo-m
State: closed
Labels: name: bug

I'm following the CI setup as described in 

https://github.com/huggingface/datasets/blob/8eee4fa9e133fe873a7993ba746d32ca2b687551/.circleci/config.yml#L16-L19

in a new conda environment, at commit https://github.com/huggingface/datasets/commit/4de6dbf84e93dad97e1000120d6628c88954e5d4

and getting

```
FAILED tests/test_caching.py::RecurseDumpTest::test_dump_ipython_function - TypeError: an integer is required (got type bytes)
1 failed, 2321 passed, 5109 skipped, 10 warnings in 124.32s (0:02:04)
```

Seems like a discrepancy with CI, perhaps a lib version that's not controlled? 
Tried with `pyarrow=={1.0.0,0.17.1,2.0.0}`

Comments: I'm not able to reproduce on my side.
Can you provide the full stacktrace please ?
What version of `python` and `dill` do you have ? Which OS are you using ?

```
co_filename = '<ipython-input-2-e0383a102aae>', returned_obj = [0]
                                                                                                                                                                       
    def create_ipython_func(co_filename, returned_obj):
        def func():
            return returned_obj
     
        code = func.__code__
>       code = CodeType(*[getattr(code, k) if k != "co_filename" else co_filename for k in code_args])
E       TypeError: an integer is required (got type bytes)

tests/test_caching.py:152: TypeError
```

Python 3.8.8 
dill==0.3.1.1


I managed to reproduce. This comes from the CodeType init signature that is different in python 3.8.8
I opened a PR to fix this test
Thanks !