Minimartzz/github-issues huggingface dataset:
cast_storage method from datasets.features
State: closed
Labels: description: Something isn't working
name: bug

## 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 ?
How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script
State: closed
Labels: description: New feature or request
name: enhancement

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.
Fix double dots in data files
State: closed

As mentioned in https://github.com/huggingface/transformers/pull/17715 `data_files` can't find a file if the path contains double dots `/../`. This has been introduced in https://github.com/huggingface/datasets/pull/4412, by trying to ignore hidden files and directories (i.e. if they start with a dot)

I fixed this and added a test

cc @sgugger @ydshieh 

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

The CI fails are unrelated to this PR (apparently something related to `seqeval` on windows) - merging :)
Refactor and add metadata to fever dataset
State: closed

Related to: #4452 and #3792.

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

But this is somehow fever v3 dataset (see this link https://fever.ai/ under the dropdown menu called Datasets). Our fever dataset already contains v1 and v2 configs. Then, I added this as if v3 config (but named feverous instead of v3 to align with the original naming by data owners).

In any case, if you really think this should be a new dataset, then I would propose to create it on the Hub instead, as "fever/feverous".

> In any case, if you really think this should be a new dataset, then I would propose to create it on the Hub instead, as "fever/feverous".

Yea makes sense ! thanks :) let's push more datasets on the hub rather than on github from now on

I have added "feverous" dataset to the Hub: https://huggingface.co/datasets/fever/feverous

I change the name of this PR accordingly, as now it only:
- Refactors code and include for both Fever v1.0 and v2.0 specific:
  - Descriptions
  - Citations
  - Homepages
- Updates documentation card aligned with above:
  - It was missing v2.0 description and citation.
- Update metadata JSON
Add `concatenate_datasets` for iterable datasets
State: closed

`concatenate_datasets` currently only supports lists of `datasets.Dataset`, not lists of `datasets.IterableDataset` like `interleave_datasets`

Fix https://github.com/huggingface/datasets/issues/2564

I also moved `_interleave_map_style_datasets` from combine.py to arrow_dataset.py, since the logic depends a lot on the `Dataset` object internals

And I moved `concatenate_datasets` from arrow_dataset.py to combine.py to have it with `interleave_datasets` (though it's also copied in arrow_dataset module for backward compatibility for now)

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

Thanks ! I addressed your comments :)

> There is a slight difference in concatenate_datasets between the version for map-style datasets and the one for iterable datasets

Indeed, here is what I did to fix this:

- axis 0: fill missing columns with None.
(I first iterate over the input datasets to infer their columns from the first examples, then I set the features of the resulting dataset to be the merged features)
This is consistent with non-streaming concatenation

- axis 1: **fill the missing rows with None**, for consistency with axis 0
(but let me know what you think, I can still revert this behavior and raise an error when one of the dataset runs out of examples)
We might have to align the non-streaming concatenation with this behavior though, for consistency. What do you think ?

Added more comments as suggested, and some typing

While factorizing _apply_features_types for both IterableDataset and TypedExamplesIterable, I  fixed a missing `token_per_repo_id` that was not passed to TypedExamplesIteable

Let me know what you think now @mariosasko
WER and CER > 1
State: closed
Labels: description: Something isn't working
name: bug

## 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
Re-add download_manager module in utils
State: closed

https://github.com/huggingface/datasets/pull/4384 moved `datasets.utils.download_manager` to `datasets.download.download_manager`

This breaks `evaluate` which imports `DownloadMode` from `datasets.utils.download_manager` 

This PR re-adds `datasets.utils.download_manager` without circular imports.

We could also show a message that says that accessing it is deprecated, but I think we can do this in a subsequent PR, and just focus on doing a patch release for now

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

Thanks for the fix.

I'm wondering how this fixes backward compatibility...

Executing this code:
```python
from datasets.utils.download_manager import DownloadMode
```
we will have
```python
DownloadMode = None
```

If afterwards we use something like:
```python
if download_mode == DownloadMode.FORCE_REDOWNLOAD
```
that will raise an exception.

It works fine on my side:
```python
>>> from datasets.utils.download_manager import DownloadMode
>>> DownloadMode is not None
True
```

As reported in https://github.com/huggingface/evaluate/pull/143
```python
from datasets.utils import DownloadConfig
```
is also missing, I'm re-adding it

Took the liberty of merging this one, to do a patch release soon. If we think of a better approach we can improve it later
Replace `assertEqual` with `assertTupleEqual` in unit tests for verbosity
State: closed

As detailed in #4419 and as suggested by @mariosasko, we could replace the `assertEqual` assertions with `assertTupleEqual` when the assertion is between Tuples, in order to make the tests more verbose.

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

FYI I used the following regex to look for the `assertEqual` statements where the assertion was being done over a Tuple: `self.assertEqual(.*, \(.*,)(\)\))$`, hope this is useful!
Fix patching module that doesn't exist
State: closed

Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true

When trying to patch `scipy.io.loadmat`:

```python
ModuleNotFoundError: No module named 'scipy'
```

Instead it shouldn't raise an error and do nothing

Bug introduced by #4375

Fix https://github.com/huggingface/datasets/issues/4494

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Patching fails for modules that are not installed or don't exist
State: closed

Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true

When trying to patch `scipy.io.loadmat`:

```python
ModuleNotFoundError: No module named 'scipy'
```

Instead it shouldn't raise an error and do nothing

We use patching to extend such functions to support remote URLs and work in streaming mode

Comments:
Add `@transmit_format` in `flatten`
State: closed

As suggested by @mariosasko in https://github.com/huggingface/datasets/pull/4411, we should include the `@transmit_format` decorator to `flatten`, `rename_column`, and `rename_columns` so as to ensure that the value of `_format_columns` in an `ArrowDataset` is properly updated.

**Edit**: according to @mariosasko comment below, the decorator `@transmit_format` doesn't handle column renaming, so it's done manually for those instead.

Comments:
@mariosasko please let me know whether we need to include some sort of tests to make sure that the decorator is working as expected. Thanks! 🤗 

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4493). All of your documentation changes will be reflected on that endpoint.

Hi, thanks for working on this! Yes, please add (simple) tests so we can avoid any unexpected behavior in the future.

`@transmit_format` doesn't handle column renaming, so I removed it from `rename_column` and `rename_columns` and added a comment to explain this.

Oops, I thought this PR was already merged and deleted from the source repository, I'll be creating a new branch out of `main` so as to re-create this PR... My bad :weary:
Dataset Viewer issue for Pavithree/test
State: closed
Labels: description: Related to the dataset viewer on huggingface.co
name: dataset-viewer

### 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.
Use `torch.nested_tensor` for arrays of varying length in torch formatter
State: open
Labels: description: New feature or request
name: enhancement

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.
Add SV-Ident dataset
State: closed


Comments:
Hi @e-tornike, thanks a lot for adding this interesting dataset.

Recently at Hugging Face, we have decided to give priority to adding datasets directly on the Hub. Would you mind to transfer your loading script to the Hub? You could create a dedicated org namespace, so that your dataset would be accessible using `vadis/sv_ident` or `sdproc/sv_ident` or `coling/sv_ident` (as you prefer).

You have an example here: https://huggingface.co/datasets/projecte-aina/catalan_textual_corpus

Additionally, please feel free to ping us if you need assistance/help in creating this dataset.

You could put the link to your Hub dataset here in this Issue discussion page, so that we can follow the progress. :)

Hi @albertvillanova, thanks for the feedback! Uploading via the Hub is a lot easier! 

I've uploaded the dataset here: https://huggingface.co/datasets/vadis/sv-ident, but it's reporting a "Status400Error". Is there any way to see the logs of the dataset script and what is causing the error?

Hi @e-tornike, good job at https://huggingface.co/datasets/vadis/sv-ident.

Normally, you can run locally the loading of the dataset by passing `streaming=True` (as the previewer does):
```python
ds = load_dataset("path/to/sv_ident.py, split="train", streaming=True)
item = next(iter(ds))
item
```

Let me have a look and open a discussion on your Hub repo! ;)

I've opened an Issue: 
- #4527
Update PASS dataset version
State: closed

Update the PASS dataset to version v3 (the newest one) from the [version history](https://github.com/yukimasano/PASS/blob/main/version_history.txt).

PS: The older versions are not exposed as configs in the script because v1 was removed from Zenodo, and the same thing will probably happen to v2.

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

As described in #4075 

I could not generate the dummy data. Also, on the data repository isn't provided the split IDs, but I copy the functions that provide the correct data split. In summary, to have a better distribution, the data in this dataset should be separated based on the amount of NORs in each image.

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

Hi! Excellent job @johnnv1! There were typos/missing words in the card, so I took the liberty to rewrite some parts to make them easier to understand. Let me know if you are ok with the changes. Also, feel free to add some info under the `Who are the annotators?` section.

Additionally, I fixed the issue with streaming and renamed the `digits` feature to `objects`.

@lhoestq Are you ok with skipping the dummy data test here as it's tricky to generate it due to the splits separation logic?

I think I can also add instance segmentation: by exposing the segment of each instance, so it will be similar with object detection:

- `instances`: a dictionary containing bounding boxes, segments, and labels of the cell objects 
  - `bbox`: a list of bounding boxes
  - `segment`: a list of segments in format of `[polygon]`, where each polygon is `[x0, y0, ..., xn, yn]`
  - `label`: a list of integers representing the category

Do you think it would be ok?

Don't you think it makes sense to keep the same category IDs for all approaches? 

Now we have:
 - nucleus category ID equals 0 for object detection and instance segmentation
 - background category ID equals 0 (on the masks) for semantic segmentation

I find it weird to have a dummy label in object detection just to align the mapping with semantic segmentation. Instead, let's explain in the card (under Data Fields -> annotation) what the pixel values mean (background + object detection labels)

Ok, I can do that in the next few days. I will create a `lapix` organization, and I will add this dataset and also #4565

So, I think we can close this PR? I have already moved these files there.

The link of CCAgT dataset is: https://huggingface.co/datasets/lapix/CCAgT

🤗 

Woohoo awesome !

closing this PR :)
Fix cast to null
State: closed

It currently fails with `ArrowNotImplementedError` instead of `TypeError` when one tries to cast integer to null type.

Because if this, type inference breaks when one replaces null values with integers in `map` (it first tries to cast to the previous type before inferring the new type).

Fix https://github.com/huggingface/datasets/issues/4483

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Better ImportError message when a dataset script dependency is missing
State: closed

When a depenency is missing for a dataset script, an ImportError message is shown, with a tip to install the missing dependencies. This message is not ideal at the moment: it may show duplicate dependencies, and is not very readable.

I improved it from
```
ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance'
```
to
```
ImportError: To be able to use bigbench, you need to install the following dependency: bigbench.
Please install it using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"' for instance'
```

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

Discussed offline with @mariosasko, merging :)

Fwiw, i think this same issue is occurring on the datasets website page, where preview isn't available due to the `bigbench` import error

For the preview of BigBench datasets, we're just waiting for bigbench to have a stable version on PyPI, instead of the one hosted on GCS ;)
Test that TensorFlow is not imported on startup
State: closed

TF takes some time to be imported, and also uses some GPU memory.

I just added a test to make sure that in the future it's never imported by default when
```python
import datasets
```
is called.

Right now this fails because `huggingface_hub` does import tensorflow (though this is fixed now on their `main` branch)

I'll mark this PR as ready for review once `huggingface_hub` has a new release

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

Should we close this PR?

I'm closing this PR. Feel free to reopen it if necessary.
Fix iwslt2017
State: closed

The files were moved to google drive, I hosted them on the Hub instead (ok according to the license)

I also updated the `datasets_infos.json`

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

CI fails are just abut missing tags in the dataset card, merging !

FYI, 

The checksums have not been edited from the changes of .tgz to .zip files, and as a result a `ExpectedMoreDownloadedFiles` error occurs. Updating them in the `dataset_infos.json` should fix the error. 

Thanks for reporting and sorry for the delay, I opened https://huggingface.co/datasets/iwslt2017/discussions/2 to fix this
Include entity positions as feature in ReCoRD
State: closed

https://huggingface.co/datasets/super_glue/viewer/record/validation

TLDR: We need to record entity positions, which are included in the source data but excluded by the loading script, to enable efficient and effective training for ReCoRD.

Currently, the loading script ignores the entity positions ("entity_start", "entity_end") and only records entity text. This might be because the training method of the official baseline is to make n training instance from a datapoint by replacing \"\@ placeholder\" in query with each entity individually.

But it increases the already heavy computation by multiple folds. So DeBERTa uses a method that take entity embeddings by their positions in the passage, and thus makes one training instance from one data point. It is way more efficient and proved effective for the ReCoRD task.

Can anybody help me with the dataset card rendering error? Maybe @lhoestq ?

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

Thanks for the reply @lhoestq !

I have sucessed on `datasets-cli test ./datasets/super_glue --name record --save_infos`,
But as you can see, the check ran into `FAILED tests/test_dataset_cards.py::test_changed_dataset_card[super_glue] - V...`.
How can we solve it?

That would be neat! Let me implement it.
Dataset Viewer issue for fgrezes/WIESP2022-NER
State: closed

### 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.