mengta666/huggingface_github huggingface dataset:
Issue: Allow .JPEG as a file extension

## Describe the bug
When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed.

## Steps to reproduce the bug
```python
# use bash to create 2 sham datasets with jpeg and JPEG ext
!mkdir dataset_a
!mkdir dataset_b
!wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg
!cp example_img.jpeg ./dataset_a/
!mv example_img.jpeg ./dataset_b/example_img.JPEG

from datasets import load_dataset

# working
df1 = load_dataset("./dataset_a", ignore_verifications=True)

#not working
df2 = load_dataset("./dataset_b", ignore_verifications=True)

# show
print(df1, df2)
```

## Expected results
```
DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 1
    })
}) DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 1
    })
})
```

## Actual results
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```

I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome.

Comments:
Hi, thanks for reporting!  I've opened a PR with the fix.

Wow, that was quick! Thank you very much 🙏
Issue: cast_storage method from datasets.features

## Describe the bug
A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets.

## Steps to reproduce the bug
Steps are:
- load whatever datset
- write a preprocessing function such as "tokenize_and_align_labels" written in https://huggingface.co/docs/transformers/tasks/token_classification
- map the function on dataset and get "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features

# Sample code to reproduce the bug
def tokenize_and_align_labels(examples):
    tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True, max_length=38,padding="max_length")

    labels = []
    for i, label in enumerate(examples[f"labels"]):
        word_ids = tokenized_inputs.word_ids(batch_index=i)  # Map tokens to their respective word.
        previous_word_idx = None
        label_ids = []
        for word_idx in word_ids:  # Set the special tokens to -100.
            if word_idx is None:
                label_ids.append(-100)
            elif word_idx != previous_word_idx:  # Only label the first token of a given word.
                label_ids.append(label[word_idx])
            else:
                label_ids.append(-100)
            previous_word_idx = word_idx
        labels.append(label_ids)

    tokenized_inputs["labels"] = labels
    return tokenized_inputs

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
dt = dataset.map(tokenize_and_align_labels, batched=True)

## Expected results
New dataset objects should load and do on older versions.

## Actual results
"ValueError: Class label -100 less than -1" from cast_storage method from datasets.features

## Environment info
everything works fine on older installations of datasets/transformers

Issue arises when installing datasets on google collab under python3.7
I can't manage to find the exact output you're requirering but version printed is datasets-2.3.2


Comments:
Hi! We've recently added a check to the `ClassLabel` type to ensure the values are in the valid label range `-1, 0, ..., num_classes-1` (-1 is used for missing values). The error in your case happens only if the `labels` column is of type `Sequence(ClassLabel(...))` before the `map` call and can be avoided by calling `dataset = dataset.cast_column("labels", Sequence(Value("int")))` beforehand. The token-classification examples in Transformers introduce a new `labels` column, so their type is also `Sequence(Value("int"))`, which doesn't lead to an error as this type unbounded. 

I'm fine with re-adding support for all negative values for unknown/missing labels @mariosasko, wdyt ?
Issue: How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script

If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair.

Or I can paraphrase the question in the following way: how to skip `_split_generators` step in `DatasetBuilder` to let `as_dataset` gives a single `Dataset` rather than a list`[Dataset]`?

Many thanks for any help.

Comments:
Hi @liyucheng09.

Users can pass the `split` parameter to `load_dataset`. For example, if your split name is "train",
```python
ds = load_dataset("dataset_name", split="train")
```
will return a `Dataset` instance.

@albertvillanova Thanks! I can't believe I didn't know this feature till now.
Issue: WER and CER > 1

## Describe the bug
It seems that in some cases in which the `prediction` is longer than the `reference` we may have word/character error rate higher than 1 which is a bit odd.

If it's a real bug I think I can solve it with a PR changing [this](https://github.com/huggingface/datasets/blob/master/metrics/wer/wer.py#L105) line to
```python
return min(incorrect / total, 1.0)
```

## Steps to reproduce the bug
```python
from datasets import load_metric
wer = load_metric("wer")
wer_value = wer.compute(predictions=["Hi World vka"], references=["Hello"])
print(wer_value)
```

## Expected results
```
1.0
```

## Actual results
```
3.0
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5

Comments:
WER can have values bigger than 1.0, this is expected when there are too many insertions

From [wikipedia](https://en.wikipedia.org/wiki/Word_error_rate):
> Note that since N is the number of words in the reference, the word error rate can be larger than 1.0
Issue: Dataset Viewer issue for Pavithree/test

### Link

https://huggingface.co/datasets/Pavithree/test

### Description

I have extracted the subset of original eli5 dataset found at hugging face. However, while loading the dataset It throws ArrowNotImplementedError: Unsupported cast from string to null using function cast_null error. Is there anything missing from my end? Kindly help.

### Owner

_No response_

Comments:
This issue can be resolved according to this post https://stackoverflow.com/questions/70566660/parquet-with-null-columns-on-pyarrow. It looks like first data entry in the json file must not have any null values as pyarrow uses this first file to infer schema for entire dataset.
Issue: Use `torch.nested_tensor` for arrays of varying length in torch formatter

Use `torch.nested_tensor` for arrays of varying length in `TorchFormatter`.

The PyTorch API of nested tensors is in the prototype stage, so wait for it to become more mature.

Comments:
What's the current behavior?

Currently, we return a list of Torch tensors if their shapes don't match. If they do, we consolidate them into a single Torch tensor.
Issue: Dataset Viewer issue for fgrezes/WIESP2022-NER

### Link

_No response_

### Description

_No response_

### Owner

_No response_

Comments:
https://huggingface.co/datasets/fgrezes/WIESP2022-NER

The error:

```
Message:       Couldn't find a dataset script at /src/services/worker/fgrezes/WIESP2022-NER/WIESP2022-NER.py or any data file in the same directory. Couldn't find 'fgrezes/WIESP2022-NER' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**test*', '**eval*'] in dataset repository fgrezes/WIESP2022-NER with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```

I understand the issue is not related to the dataset viewer in itself, but with the autodetection of the data files without a loading script in the datasets library. cc @lhoestq @albertvillanova @mariosasko 

Apparently it finds `scoring-scripts/compute_seqeval.py` which matches `**eval*`, a regex that detects a test split. We should probably improve the regex because it's not supposed to catch this kind of files. It must also only check for files with supported extensions: txt, csv, png etc.
Issue: `to_pandas` doesn't take into account format.

**Is your feature request related to a problem? Please describe.**

I have a large dataset that I need to convert part of to pandas to do some further analysis. Calling `to_pandas` directly on it is expensive. So I thought I could simply select the columns that I want and then call `to_pandas`.

**Describe the solution you'd like**

```python
from datasets import Dataset

ds = Dataset.from_dict({'a': [1,2,3], 'b': [5,6,7], 'c': [8,9,10]})
pandas_df = ds.with_format(columns=['a', 'b']).to_pandas()

# I would expect `pandas_df` to only include a,b as column.
```


**Describe alternatives you've considered**
I could remove all columns that I don't want? But I don't know all of them in advance. 

**Additional context**
I can probably make a PR with some pointers.


Comments:
Thanks for opening a discussion :)

Note that you can use `.remove_columns(...)` to keep only the ones you're interested in before calling `.to_pandas()`

Yes I can do that thank you!

Do you think that conceptually my example should work? If not, I'm happy to close this issue. 

If yes, I can start working on it.

Hi! Instead of `with_format(columns=['a', 'b']).to_pandas()`, use `with_format("pandas", columns=["a", "b"])` for easy conversion of the parts of the dataset to pandas via indexing/slicing.

The full code:
```python
from datasets import Dataset

ds = Dataset.from_dict({'a': [1,2,3], 'b': [5,6,7], 'c': [8,9,10]})
pandas_df = ds.with_format("pandas", columns=['a', 'b'])[:]
```

Ahhhh Thank you!

closing then :)
Issue: CI error with repo lhoestq/_dummy

## Describe the bug
CI is failing because of repo "lhoestq/_dummy". See: https://app.circleci.com/pipelines/github/huggingface/datasets/12461/workflows/1b040b45-9578-4ab9-8c44-c643c4eb8691/jobs/74269
```
requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/api/datasets/lhoestq/_dummy?full=true
```

The repo seems to no longer exist: https://huggingface.co/api/datasets/lhoestq/_dummy
```
error: "Repository not found"
```

CC: @lhoestq 

Comments:
fixed by https://github.com/huggingface/datasets/pull/4472
Issue: BigBench: NonMatchingSplitsSizesError when passing a dataset configuration parameter

As noticed in https://github.com/huggingface/datasets/pull/4125 when a dataset config class has a parameter that reduces the number of examples (e.g. named `max_examples`), then loading the dataset and passing `max_examples` raises `NonMatchingSplitsSizesError`.

This is because it will check for expected the number of examples of the config with the same name without taking into account the `max_examples` parameter. This can be fixed by checking the expected number of examples using the **config id** instead of name. Indeed the config id corresponds to the config name + an optional suffix that depends on the config parameters

Comments:
Why not adding `max_examples` as part of the config name?

Yup it can also work, and maybe it's simpler this way. Opening a PR to fix bigbench instead of https://github.com/huggingface/datasets/pull/4463

Hi @lhoestq,

Thank you for taking a look at this issue, and proposing a solution. 
Unfortunately, after trying the fix in #4465 I still see the same issue.

I think there is some subtlety where the config name gets overwritten somewhere when `BUILDER_CONFIGS`[(link)](https://github.com/huggingface/datasets/blob/master/datasets/bigbench/bigbench.py#L126) is defined. 

If I print out the `self.config.name` in the current version (with the fix in #4465), I see just the task name, but if I comment out `BUILDER_CONFIGS`, the `num_shots` and `max_examples` gets appended as was meant by #4465.

I haven't managed to track down where this happens, but I thought you might know? 

(Another comment on your fix: the `name` variable is used to fetch the task from the bigbench API, so modifying it causes an error if it's actually called. This can easily be fixed by having `config_name` variable in addition to the `task_name`)
Issue: AttributeError: module 'datasets' has no attribute 'load_dataset'

## Describe the bug
I have piped install datasets, but this package doesn't have these attributes: load_dataset, load_metric.

## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-5.13.0-44-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.6.13
- PyArrow version: 6.0.1



Comments:
I'm having the same issue，Can you tell me how to solve it？

I have the same issue, can you tell me how to solve it? Thanks

I had a folder named 'datasets' so this is why it can't find the import, it's looking in the wrong place

@briandw your comment saved my day 👍
Issue: Workflow for Tabular data

Tabular data are treated very differently than data for NLP, audio, vision, etc. and therefore the worflow for tabular data in `datasets` is not ideal.

For example for tabular data, it is common to use pandas/spark/dask to process the data, and then load the data into X and y (X is an array of features and y an array of labels), then train_test_split and finally feed the data to a machine learning model.

In `datasets` the workflow is different: we use load_dataset, then map, then train_test_split (if we only have a train split) and we end up with columnar dataset splits, not formatted as X and y.

Right now, it is already possible to convert a dataset from and to pandas, but there are still many things that could improve the workflow for tabular data:
- be able to load the data into X and y
- be able to load a dataset from the output of spark or dask (as far as I know it's usually csv or parquet files on S3/GCS/HDFS etc.)
- support "unsplit" datasets explicitly, instead of putting everything in "train" by default

cc @adrinjalali @merveenoyan feel free to complete/correct this :)

Feel free to also share ideas of APIs that would be super intuitive in your opinion !

Comments:
I use below to load a dataset:
```
dataset = datasets.load_dataset("scikit-learn/auto-mpg")
df = pd.DataFrame(dataset["train"])
```
TBH as said, tabular folk split their own dataset, they sometimes have two splits, sometimes three. Maybe somehow avoiding it for tabular datasets might be good for later. (it's just UX improvement) 

is very slow batch access of a dataset (tabular, csv) with many columns to be expected?

Define "many" ? x)

~20k! I was surprised batch loading with as few as 32 samples was really slow. I was speculating the columnar format was the cause -- or do you see good performance with this approx size of tabular data?

20k can be a lot for a columnar format but maybe we can optimize a few things.

It would be cool to profile the code to see if there's an unoptimized part of the code that slows everything down.

(it's also possible to kill the job when it accesses the batch, it often gives you the traceback at the location where the code was running)

FWIW I've worked with tabular data with 540k columns.

thats awesome, whats your secret? would love to see an example!

@wconnell I'm not sure what you mean by my secret, I load them into a numpy array 😁 

An example dataset is [here](https://portal.gdc.cancer.gov/repository?facetTab=files&filters=%7B%22content%22%3A%5B%7B%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22TCGA-CESC%22%5D%7D%2C%22op%22%3A%22in%22%7D%2C%7B%22content%22%3A%7B%22field%22%3A%22files.data_category%22%2C%22value%22%3A%5B%22DNA%20Methylation%22%5D%7D%2C%22op%22%3A%22in%22%7D%5D%2C%22op%22%3A%22and%22%7D&searchTableTab=files) which is a dataset of DNA methylation reads. This dataset is about 950 rows and 450k columns.
Issue: Dataset Viewer issue for Yaxin/SemEval2015

### Link

_No response_

### Description

_No response_

### Owner

_No response_

Comments:
I understand that the issue is that a remote file (URL) is being loaded as a local file. Right @albertvillanova @lhoestq?

```
Message:       [Errno 2] No such file or directory: 'https://raw.githubusercontent.com/YaxinCui/ABSADataset/main/SemEval2015Task12Corrected/train/restaurants_train.xml'
```

`xml.dom.minidom.parse` is not supported in streaming mode. I opened a PR here to fix it:
https://huggingface.co/datasets/Yaxin/SemEval2015/discussions/1

Please review the PR @WithYouTo and let me know if it works !

Additionally, I'm also patching our library, so that we support streaming datasets that use `xml.dom.minidom.parse`.
Issue: Trying to load FEVER dataset results in NonMatchingChecksumError

## Describe the bug
Trying to load the `fever` dataset fails with `datasets.utils.info_utils.NonMatchingChecksumError`.

I tried with `download_mode="force_redownload"` but that did not fix the error. I also tried with `ignore_verification=True` but then that raised a `json.decoder.JSONDecodeError`.

## Steps to reproduce the bug
```python
from datasets import load_dataset

dataset = load_dataset('fever', 'v1.0') # Fails with NonMatchingChecksumError
dataset = load_dataset('fever', 'v1.0', download_mode="force_redownload") # Fails with NonMatchingChecksumError
dataset = load_dataset('fever', 'v1.0', ignore_verification=True)` # Fails with JSONDecodeError
```

## Expected results
I expect this call to return with no error raised.

## Actual results
With `ignore_verification=False`:
```
*** datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://s3-eu-west-1.amazonaws.com/fever.public/train.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev_public.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_test.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_test.jsonl']
```
With `ignore_verification=True`:
```
*** json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.3.dev0
- Platform: Linux-4.15.0-50-generic-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.2


Comments:
Thanks for reporting @santhnm2. We are fixing it.

Data owners updated their URLs recently. We have to align with them, otherwise you do not download anything (that is why ignore_verifications does not work).

Hello! Is there any update on this? I am having the same issue 6 months later.
Issue: New Preprocessing Feature  - Deduplication [Request]

**Is your feature request related to a problem? Please describe.**
Many large datasets are full of duplications and it has been shown that deduplicating datasets can lead to better performance while training, and more truthful evaluation at test-time.

A feature that allows one to easily deduplicate a dataset can be cool!

**Describe the solution you'd like**
We can define a function and keep only the first/last data-point that yields the value according to this function.

**Describe alternatives you've considered**
The clear alternative is to repeat a clear boilerplate every time someone want to deduplicate a dataset.


Comments:
Hi! The [datasets_sql](https://github.com/mariosasko/datasets_sql) package lets you easily find distinct rows in a dataset (an example with `SELECT DISTINCT` is in the readme). Deduplication is (still) not part of the official API because it's hard to implement for datasets bigger than RAM while only using the native PyArrow ops.

(Btw, this is a duplicate of https://github.com/huggingface/datasets/issues/2514)

Here is an example using the [datasets_sql](https://github.com/mariosasko/datasets_sql) mentioned 

```python 
from datasets_sql import query

dataset = load_dataset("imdb", split="train")

# If you dont have an id column just add one by enumerating
dataset=dataset.add_column("id", range(len(dataset)))

id_column='id'
unique_column='text'

# always selects min id
unique_dataset = query(f"SELECT dataset.* FROM dataset JOIN (SELECT MIN({id_column}) as unique_id FROM dataset group by {unique_column}) ON unique_id=dataset.{id_column}")
```
Not ideal for large datasets but good enough for basic cases.
Sure would be nice to have in the library 🤗
Issue: Dataset Viewer issue for openclimatefix/nimrod-uk-1km

### Link

_No response_

### Description

_No response_

### Owner

_No response_

Comments:
If I understand correctly, this is due to the key `split` missing in the line https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L41 of the script.
Maybe @albertvillanova could confirm.

I'm having a look.

Indeed there are several issues in this dataset loading script.

The one pointed out by @severo: for the default configuration "crops": https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L244
- The download manager downloads `_URL`
- But `_URL` is not defined:  https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L41
  ```python
  _URL = {'train': []}
  ```
- Afterwards, for each split, a different key in `_ULR` is used, but it only contains one key: "train"
  - "valid" key: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L260
  - "test key: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L269
  
These keys do not exist inside `_URL`, thus the error message reported in the viewer: 
```
Exception:     KeyError
Message:       'valid'
```

Would anyone want to submit a Hub PR (or open a Discussion for the authors to be aware) to this dataset? https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km

Hi, I'm the main author for that dataset, so I'll work on updating it! I was working on debugging some stuff awhile ago, which is what broke it. 

I've opened a Discussion page, so that we can ask/answer and propose fixes until the script works properly: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/discussions/1

CC: @julien-c @jacobbieker 

can we close this issue and followup in the discussion?
Issue: Dataset Viewer issue for amazon_polarity

### Link

https://huggingface.co/datasets/amazon_polarity/viewer/amazon_polarity/test

### Description

For some reason the train split is OK but the test split is not for this dataset:

```
Server error
Status code:   400
Exception:     FileNotFoundError
Message:       [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/amazon_polarity/__init__.py'
```

### Owner

No

Comments:
Thanks, looking at it

Not sure what happened 😬, but it's fixed
Issue: Dataset Viewer issue for aeslc

### Link

https://huggingface.co/datasets/aeslc

### Description

The dataset viewer can't find `dataset_infos.json` in it's cache:

```
Server error
Status code:   400
Exception:     FileNotFoundError
Message:       [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/aeslc/eb8e30234cf984a58ebe9f205674597ac1db2ec91e7321cd7f36864f7e3671b8/dataset_infos.json'
```

### Owner

No

Comments:
Not sure what happened 😬, but it's fixed
Issue: TIMIT won't load after manual download: Errors about files that don't exist

## Describe the bug

I get the message from HuggingFace that it must be downloaded manually. From the URL provided in the message, I got to UPenn page for manual download. (UPenn apparently want $250? for the dataset??)   ...So, ok, I obtained a copy from a friend and also a smaller version from Kaggle. But in both cases the HF dataloader fails; it is looking for files that don't exist anywhere in the dataset: it is looking for files with lower-case letters like "**test*" (all the filenames in both my copies are uppercase) and certain file extensions that exclude the .DOC which is provided in TIMIT:


## Steps to reproduce the bug
```python
data = load_dataset('timit_asr', 'clean')['train']
```

## Expected results
The dataset should load with no errors. 

## Actual results
This error message:
```
  File "/home/ubuntu/envs/data2vec/lib/python3.9/site-packages/datasets/data_files.py", line 201, in resolve_patterns_locally_or_by_urls
    raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to resolve any data file that matches '['**test*', '**eval*']' at /home/ubuntu/datasets/timit with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```

But this is a strange sort of error: why is it looking for lower-case file names when all the TIMIT dataset filenames are uppercase?  Why does it exclude .DOC files when the only parts of the TIMIT data set with "TEST" in them have ".DOC" extensions?   ...I wonder, how was anyone able to get this to work in the first place?

The files in the dataset look like the following: 
```
³       PHONCODE.DOC
³       PROMPTS.TXT
³       SPKRINFO.TXT
³       SPKRSENT.TXT
³       TESTSET.DOC
```
...so why are these being excluded by the dataset loader? 


## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-1060-aws-x86_64-with-glibc2.27
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.2


Comments:
To have some context, please see:
- #4145

Please, also note that we have recently made some fixes to the script, which are in our GitHub master branch but not yet released:
- #4422
- #4425 
- #4436

Thanks Albert! I'll try pulling `datasets` from the git repo instead of PyPI, and/or just wait for the next release.


I'm closing this issue then. Please, feel free to reopen it again if the problem persists.
Issue: Load a local cached dataset that has been modified

## Describe the bug
I have loaded a dataset as follows:
```
d = load_dataset("emotion", split="validation")
```
Afterwards I make some modifications to the dataset via a `map` call:
```
d.map(some_update_func, cache_file_name=modified_dataset)
```
This generates a cached version of the dataset on my local system in the same directory as the original download of the data (/path/to/cache). Running an `ls` returns:
```
modified_dataset
dataset_info.json  
emotion-test.arrow  
emotion-train.arrow  
emotion-validation.arrow
```
as expected. However, when I try to load up the modified cached dataset via a call to 
```
modified = load_dataset("emotion", split="validation", data_files="/path/to/cache/modified_dataset") 
```
it simply redownloads a new version of the dataset and dumps to a new cache rather than loading up the original modified dataset:
```
Using custom data configuration validation-cdbf51685638421b
Downloading and preparing dataset emotion/validation to ...
```

How am I supposed to load the original modified local cache copy of the dataset?

## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.2



Comments:
Hi! `datasets` caches every modification/loading, so you can either rerun the pipeline up to the `map` call or use `Dataset.from_file(modified_dataset)` to load the dataset directly from the cache file.

Awesome, hvala Mario! This works.
Issue: Add loading variable number of columns for different splits

**Is your feature request related to a problem? Please describe.**
The original dataset `blended_skill_talk` consists of different sets of columns for the different splits: (test/valid) splits have additional data column `label_candidates` that the (train) doesn't have.
When loading such data, an exception occurs at table.py:cast_table_to_schema, because of mismatched columns.

Comments:
Hi! Indeed the column is missing, but you shouldn't get an error? Have you made some modifications (locally) to the loading script? I've opened a PR to add the missing columns to the script.
Issue: Cannot load timit_asr data set

## Describe the bug
I am trying to load the timit_asr data set. I have tried with a copy from the LDC, and a copy from deepai. In both cases they fail with a "duplicate key" error. With the LDC version I have to convert the file extensions all to upper-case before I can load it at all.

## Steps to reproduce the bug
```python
timit = datasets.load_dataset("timit_asr", data_dir = "/path/to/dataset")
# Sample code to reproduce the bug
```

## Expected results

The data set should load without error. It worked for me before the LDC url change.

## Actual results
```
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: SA1
Keys should be unique and deterministic in nature
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2



Comments:
Thanks for reporting, @bhaddow.

I'm fixing it.

Thanks for the quick fix!

@bhaddow we have also made a fix so that you don't have to convert to uppercase the file extensions of the LDC data.

Would you mind checking if it works OK now for you and reporting if there are any issues? Thanks. 

Hi @albertvillanova  -It loads fine on a copy of the data from deepai - although I have to remove the copies of the .WAV files (with extension .WAV,wav). On a copy of the data that was obtained from the LDC, the glob still fails to find the files. The LDC copy looks like it was copied from CD, in 2004, so the structure may be different to a current download.

Ah, if I change the train/ and test/ directories to TRAIN/ and TEST/  then it works!

Thanks for your investigation and report, @bhaddow. I'm adding another fix for the TRAIN/train and TEST/test directory names.
Issue: Update `unittest` assertions over tuples from `assertEqual` to `assertTupleEqual`

**Is your feature request related to a problem? Please describe.**

So this is more a readability improvement rather than a proposal, wouldn't it be better to use `assertTupleEqual` over the tuples rather than `assertEqual`? As `unittest` added that function in `v3.1`, as detailed at https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual, so maybe it's worth updating.

Find an example of an `assertEqual` over a tuple in 🤗 `datasets` unit tests over an `ArrowDataset` at https://github.com/huggingface/datasets/blob/0bb47271910c8a0b628dba157988372307fca1d2/tests/test_arrow_dataset.py#L570

**Describe the solution you'd like**

Start slowly replacing all the `assertEqual` statements with `assertTupleEqual` if the assertion is done over a Python tuple, as we're doing with the Python lists using `assertListEqual` rather than `assertEqual`.

**Additional context**

If so, please let me know and I'll try to go over the tests and create a PR if applicable, otherwise, if you consider this should stay as `assertEqual` rather than `assertSequenceEqual` feel free to close this issue! Thanks 🤗 


Comments:
Hi! If the only goal is to improve readability, it's better to use `assertTupleEqual` than `assertSequenceEqual` for Python tuples. Also, note that this function is called internally by `assertEqual`, but I guess we can accept a PR to be more verbose.

Hi @mariosasko, right! I'll update the issue title/desc with `assertTupleEqual` even though as you said it seems to be internally using `assertEqual` so I'm not sure whether it's worth it or not...

https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual

I thought we were supposed to move gradually from `unittest` to `pytest`...
Issue: Dataset Viewer issue for ett

### Link

https://huggingface.co/datasets/ett

### Description

Timestamp is not JSON serializable. 

```
Status code:   500
Exception:     Status500Error
Message:       Type is not JSON serializable: Timestamp
```

### Owner

No

Comments:
Thanks for reporting @dgcnz.

I have checked that the dataset works fine in streaming mode.

Additionally, other datasets containing timestamps are properly rendered by the viewer: https://huggingface.co/datasets/blbooks

I have tried to force the refresh of the preview, but the endpoint is not responsive: Connection timed out

CC: @severo 

I've just resent the refresh of the preview to the new endpoint, without success.

CC: @severo 

Fixed!

https://huggingface.co/datasets/ett/viewer/h1/test

<img width="982" alt="Capture d’écran 2022-06-15 à 09 30 22" src="https://user-images.githubusercontent.com/1676121/173769035-a075d753-ecfc-4a43-b54b-973105d464d3.png">
Issue: Dataset Viewer issue for conll2012_ontonotesv5

### Link

https://huggingface.co/datasets/conll2012_ontonotesv5

### Description

Dataset viewer outage.

### Owner

No

Comments:
Thanks for reporting, @jiangwy99.

I guess this could be addressed only once we fix our issue with irresponsive backend endpoint.

CC: @severo 

I've just sent the forcing of the refresh of the preview to the new endpoint.

Fixed, thanks for the patience. The issue was the amount of RAM allowed to extract the first rows of the dataset was not sufficient.
Issue: Dataset should have a `.name` field

**Is your feature request related to a problem? Please describe.**
If building pipelines that can evaluate on more than one dataset, it would be nice to be able to log results of things like `Evaluating on {dataset.name}` or `results for {dataset.name} are: {results}`

Without some way of concisely identifying a dataset from the dataset object, tools which might run on more than one dataset must be passed the dataset object _and_ the name/id of the dataset being used. 

**Describe the solution you'd like**
The DatasetInfo class should have a `name` field which is the name of a dataset. then for a given dataset if it evolves in time the `version` can be updated but its different versions of the same dataset with a unique `name`. The name could then all be accessed by `dataset.name`

**Describe alternatives you've considered**
For my own purposes I am considering making `NamedDataset[Dataset]` where the subclass just has a .name field. 

**Additional context**
My guess is that most usecases are not working with more than one dataset in a given pipeline so a name is not really needed. This has surprised me though as one of the advantages of a standard dataset interface is to be able to build pipelines which can be passed in a dataset and separate responsibilities of the dataset loading from the train or eval pipeline.


Comments:
Hi! You can already use `dset.builder_name` and `dset.config_name` for that purpose. And when it comes to versioning, it's better to use `dset._fingerprint` than the `version` attribute as the former represents a deterministic hash that encodes all the mutable ops executed on a dataset, and the latter stays the same unless it's manually updated after each op.

@mariosasko  Can we make ._fingerprint not private? seems a critical component for tracking how a model was generated to ensure reproducibility.
Issue: "NonMatchingChecksumError" when importing 'spider' dataset

## Describe the bug
When importing 'spider' dataset [https://huggingface.co/datasets/spider] an error occurs

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('spider')
```

## Expected results
Dataset object

## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']

## Environment info
- `datasets` version: 2.2.2
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.11
- PyArrow version: 6.0.1
- Pandas version: 1.3.5


Comments:
Thanks for reporting, @OmarAlaaeldein.

Datasets hosted at Google Drive give problems quite often due to a change in their service:
- #3786 

Related to:
- #3906

I'm having a look.

We have made a Pull Request to replace the Google Drive URL. This fix will be accessible in our next `datasets` library release.

In the meantime, once the PR merged into master, you can get this fix by installing our library from the GitHub master branch:
```shell
pip install git+https://github.com/huggingface/datasets#egg=datasets
```
Then, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):
```shell
load_dataset("...", download_mode="force_redownload")
```
Issue: load dataset wikitext-2-raw-v1 failed. Could not reach wikitext-2-raw-v1.py.

## Describe the bug
Could not reach wikitext-2-raw-v1.py

## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikitext-2-raw-v1")
```

## Expected results
Download `wikitext-2-raw-v1` dataset successfully.

## Actual results
```
  File "load_datasets.py", line 13, in <module>
    load_dataset("wikitext-2-raw-v1")
  File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1715, in load_dataset
    **config_kwargs,
  File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1536, in load_dataset_builder
    data_files=data_files,
  File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1282, in dataset_module_factory
    raise e1 from None
  File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1224, in dataset_module_factory
    dynamic_modules_path=dynamic_modules_path,
  File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 559, in get_module
    local_path = self.download_loading_script(revision)
  File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 539, in download_loading_script
    return cached_path(file_path, download_config=download_config)
  File "/root/miniconda3/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 246, in cached_path
    download_desc=download_config.download_desc,
  File "/root/miniconda3/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 582, in get_from_cache
    raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.2.2/datasets/wikitext-2-raw-v1/wikitext-2-raw-v1.py (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Read timed out. (read timeout=100)",),))
```
I tried to download wikitext-2-raw-v1.py by chrome and got:
![image](https://user-images.githubusercontent.com/20658907/170171595-0ca9f1da-c05a-4b57-861e-9530bfa3bdb9.png)

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: CentOS 7
- Python version: 3.6
- PyArrow version: 3.0.0


Comments:
I tried in this way.

```python
from datasets import load_dataset
dataset = load_dataset(path="wikitext", name="wikitext-103-v1", split="train")
```
Issue: TypeError: __init__() missing 1 required positional argument: 'scheme'

  "name" : "node-1",
  "cluster_name" : "elasticsearch",
  "cluster_uuid" : "",
  "version" : {
    "number" : "7.5.0",
    "build_flavor" : "default",
    "build_type" : "tar",
    "build_hash" : "",
    "build_date" : "2019-11-26T01:06:52.518245Z",
    "build_snapshot" : false,
    "lucene_version" : "8.3.0",
    "minimum_wire_compatibility_version" : "6.8.0",
    "minimum_index_compatibility_version" : "6.0.0-beta1"
 
when I run the order:
nohup python3 custom_service.pyc > service.log 2>&1&

the log:
nohup: 忽略输入
Traceback (most recent call last):
  File "/home/xfz/p3_custom_test/custom_service.py", line 55, in <module>
  File "/home/xfz/p3_custom_test/custom_service.py", line 48, in doInitialize
  File "custom_impl.py", line 286, in custom_setup
  File "custom_impl.py", line 127, in create_es_index
  File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/__init__.py", line 345, in __init__
    ssl_show_warn=ssl_show_warn,
  File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/utils.py", line 105, in client_node_configs
    node_configs = hosts_to_node_configs(hosts)
  File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/utils.py", line 154, in hosts_to_node_configs
    node_configs.append(host_mapping_to_node_config(host))
  File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/utils.py", line 221, in host_mapping_to_node_config
    return NodeConfig(**options)  # type: ignore
TypeError: __init__() missing 1 required positional argument: 'scheme'
[1]+  退出 1                nohup python3 custom_service.pyc > service.log 2>&1

custom_service_pyc can't running



Comments:
Duplicate of:
- #3956

I think you should report that issue to `elasticsearch` library: https://github.com/elastic/elasticsearch-py
Issue: The dataset preview is not available for this split.

I have uploaded the corpus developed by our lab in the speech domain to huggingface [datasets](https://huggingface.co/datasets/Roh/ryanspeech). You can read about the companion paper accepted in interspeech 2021 [here](https://arxiv.org/abs/2106.08468). The dataset works fine but I can't make the dataset preview work. It gives me the following error that I don't understand. Can you help me to begin debugging it?

```
Status code:   400
Exception:     AttributeError
Message:       'NoneType' object has no attribute 'split'
``` 

Comments:
Hi!  A dataset has to be streamable to work with the viewer. I did a quick test, and yours is, so this might be a bug in the viewer. cc @severo 


Looking at it. The message is now:

```
Message:       cannot cache function '__shear_dense': no locator available for file '/src/services/worker/.venv/lib/python3.9/site-packages/librosa/util/utils.py'
```

so possibly it's related to the libraries versions?


Maybe this SO thread can help: https://stackoverflow.com/questions/59290386/runtimeerror-at-cannot-cache-function-shear-dense-no-locator-available-fo

Same error for https://huggingface.co/datasets/LIUM/tedlium/viewer/release1/test. cc @sanchit-gandhi . I'm on it

Fixed in the datasets viewer, by setting the `NUMBA_CACHE_DIR` env var to a writable directory.

https://huggingface.co/datasets/Roh/ryanspeech/viewer/male/train

<img width="1538" alt="Capture d’écran 2022-06-08 à 11 30 08" src="https://user-images.githubusercontent.com/1676121/172583285-4cd49a0f-5715-423b-95dd-5f6ace3b2416.png">


https://huggingface.co/datasets/LIUM/tedlium/viewer/

<img width="1538" alt="Capture d’écran 2022-06-08 à 14 31 52" src="https://user-images.githubusercontent.com/1676121/172616897-fbcb7df7-0308-4d09-a17d-48826bc91374.png">
Issue: Missing dataset tags and sections in some dataset cards

Summary of CircleCI errors for different dataset metadata:

- **BoolQ**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'
- **Conllpp**: expected some content in section `Citation Information` but it is empty.
- **GLUE**:   'annotations_creators', 'language_creators', 'source_datasets' :['unknown'] are not registered tags
- **ConLL2003**:  field 'task_ids':  ['part-of-speech-tagging'] are not registered tags for 'task_ids'
-  **Hate_speech18:**   Expected some content in section `Data Instances` but it is empty, Expected some content in section `Data Splits` but it is empty
- **Jjigsaw_toxicity_pred**:  `Citation Information` but it is empty.
- **LIAR** :  `Data Instances`,`Data Fields`,  `Data Splits`,  `Citation Information` are empty.
- **MSRA NER** : Dataset Summary`, `Data Instances`, `Data Fields`,  `Data Splits`, `Citation Information` are empty.
- **sem_eval_2010_task_8**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'
- **sms_spam**: `Data Instances` and`Data Splits` are empty.
- **Quora** : Expected some content in section `Citation Information` but it is empty,  missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'
- **sentiment140**:  missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'

Comments:
@lhoestq I can take this issue. Please can you point out to me where I can find the other positional arguments?

Hi @RohitRathore1 :)

You can find all the YAML tags in the tagging app here: https://hf.co/spaces/huggingface/datasets-tagging). They're all passed as arguments to a DatasetMetadata object used to validate the tags.