AniketTec/github-issues huggingface dataset:
Title: Loading an external dataset in a format similar to conll2003
State: closed

I'm trying to load a custom dataset in a Dataset object, it's similar to conll2003 but with 2 columns only (word entity), I used the following script:

features = datasets.Features(
            {"tokens": datasets.Sequence(datasets.Value("string")),
        "ner_tags": datasets.Sequence(
            datasets.features.ClassLabel(
                names=["B-PER", .... etc.]))}
        )


from datasets import Dataset

INPUT_COLUMNS = "tokens ner_tags".split(" ")

def read_conll(file):
    #all_labels = []
    example = {col: [] for col in INPUT_COLUMNS}
    idx = 0
    with open(file) as f:
        for line in f:
          if line:
            if line.startswith("-DOCSTART-") and example["tokens"] != []:
                print(idx, example)
                yield idx, example
                idx += 1
                example = {col: [] for col in INPUT_COLUMNS}
            elif line == "\n" or (line.startswith("-DOCSTART-") and example["tokens"] == []):
              continue
            else:
                row_cols = line.split(" ")
                for i, col in enumerate(example):
                    example[col] = row_cols[i].rstrip()

dset = Dataset.from_generator(read_conll, gen_kwargs={"file": "/content/new_train.txt"}, features = features)   

The following error happened:
[/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <genexpr>(.0)
    285     for key in unique_values(itertools.chain(*dicts)):  # set merge all keys
    286         # Will raise KeyError if the dict don't have the same keys
--> 287         yield key, tuple(d[key] for d in dicts)
    288 
    

TypeError: tuple indices must be integers or slices, not str

What does this mean and what should I modify?

Comments:
Title: Add notebook / other resource links to the task-specific data loading guides
State: closed
Labels: description: New feature or request
name: enhancement

Does it make sense to include links to notebooks / scripts that show how to use a dataset for training / fine-tuning a model? 

For example, here in [https://huggingface.co/docs/datasets/image_classification] we could include a mention of https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb.

Applies to https://huggingface.co/docs/datasets/object_detection as well.

Cc: @osanseviero @nateraw 

Comments:
Yea this would be great! We would need an object detection tutorial notebook too if it doesn't already exist there. 

There is one: https://huggingface.co/docs/datasets/object_detection.

I will start the work.
Title: Add a guide for semantic segmentation
State: closed
Labels: description: Improvements or additions to documentation
name: documentation

Currently, we have these guides for object detection and image classification:

* https://huggingface.co/docs/datasets/object_detection
* https://huggingface.co/docs/datasets/image_classification

I am proposing adding a similar guide for semantic segmentation. 

I am happy to contribute a PR for it. 

Cc: @osanseviero @nateraw 

Comments:
Sure this sounds great! Would this be pure torchvision, albumentations, or something else?

I am considering `torchvision` and `albumentations`. Also [works with TensorFlow](https://github.com/deep-diver/segformer-tf-transformers/blob/main/notebooks/TFSegFormer_Finetune.ipynb). 

I am assigning the issue to myself then.
Title: An example or recommendations for creating large image datasets?
State: open

I know that Apache Beam and `datasets` have [some connector utilities](https://huggingface.co/docs/datasets/beam). But it's a little unclear what we mean by "But if you want to run your own Beam pipeline with Dataflow, here is how:". What does that pipeline do? 

As a user, I was wondering if we have this support for creating large image datasets. If so, we should mention that [here](https://huggingface.co/docs/datasets/image_dataset). 

Cc @lhoestq 

Comments:
The beam utilities allow to prepare a dataset as parquet in your cloud storage. From my perspective this CLI is not super easy to use, but we've been working on a new python API to prepare a dataset in your cloud storage:
```python
from datasets import load_dataset_builder

builder = load_dataset_builder("c4", "en")
builder.download_and_prepapre("s3://my-bucket/c4", file_format="parquet")
```

And to use Beam you can do:
```python
beam_runner = ...  # one of "SparkRunner", "DataFlowRunner", "DirectRunner", etc.
beam_options = ...

builder.download_and_prepapre(
    "s3://my-bucket/c4",
    file_format="parquet",
    beam_runner=beam_runner,
    beam_options=beam_options
)
```

Though Beam can be used ONLY if there is a dataset script based on the `BeamBasedBuilder` right now - it doesn't work on an arbitrary dataset (see [wikipedia.py](https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py) for example).

Thanks! 

Would be nice to have something similar for creating large image datasets.
Title: Loading an external NER dataset
State: closed

I need to use huggingface datasets to load a custom dataset similar to conll2003 but with more entities and each the files contain only two columns: word and ner tag.

I tried this code snnipet that I found here as an answer to a similar issue:

from datasets import Dataset

INPUT_COLUMNS = "ID Text NER".split()

def read_conll(file):
    example = {col: [] for col in INPUT_COLUMNS}
    idx = 0
    with open(file) as f:
        for line in f:
            if line.startswith("-DOCSTART-") or line == "\n" or not line:
                if example[next(iter(example))]:
                    yield idx, example
                idx += 1
                example = {col: [] for col in INPUT_COLUMNS}
            else:
                row_cols = line.split()
                for i, col in enumerate(example):
                    example[col] = row_cols[i].rstrip()

train = Dataset.from_generator(read_conll, gen_kwargs={"file": "some_path"})   

But the following error happened:

ValueError: Please pass `features` or at least one example when writing data

Comments:
Title: Preserve None in list type cast in PyArrow 10
State: closed

The `ListArray` type in PyArrow 10.0.0 supports the `mask` parameter, which allows us to preserve Nones in nested lists in `cast` instead of replacing them with empty lists.

Fix https://github.com/huggingface/datasets/issues/3676

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Title: Raise ffmpeg warnings only once
State: closed

Our warnings looks nice now.

`librosa` warning that was raised at each decoding: 
```
/usr/local/lib/python3.7/dist-packages/librosa/core/audio.py:165: UserWarning: PySoundFile failed. Trying audioread instead.
  warnings.warn("PySoundFile failed. Trying audioread instead.")
```
is suppressed with `filterwarnings("ignore")` in a context manager. That means the first warning is also ignored (setting `filterwarnings("once")` didn't work!), so I added info that audioread is used for decoding to our message. Hope it's enough.


Tests failed at first because they used to check if the warning was raised at (each) decoding in `librosa` case but now we throw only one warning (at first decoding). I removed this check for warnings, do you think it's fine? 

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Title: Inconsistency behavior between handling local file protocol and other FS protocols
State: open

### Describe the bug

These lines us used during load_from_disk:
```
if is_remote_filesystem(fs):
    dest_dataset_dict_path = extract_path_from_uri(dataset_dict_path)
else:
    fs = fsspec.filesystem("file")
    dest_dataset_dict_path = dataset_dict_path
```
If a local FS is given, then it will the URL as the path name. If a remote Fs is given, then it will use the path of the URL. This is an inconsistent behavior when handling a file: when using remote FS, you must write a URL, but for local FS, even if you passed LocalFileSystem as `fs` you still can't use a `file://` URL. It will be recognized as a directory named `file:`.

### Steps to reproduce the bug

```
import fsspec.core
url = "hdfs:///somewhere/MNIST"
# url = "file:///somewhere/MNIST"
fs, path = fsspec.core.url_to_fs(url)

fs.ls(path) # this will always work
load_from_disk(path, fs) # only works for local FS
load_from_disk(url, fs) # only works for remote FS

```

### Expected behavior

one of `url` or `path` should always work

I think we extract path from given URL by using `fsspec.core.url_to_fs` instead of using `is_remote_filesystem` and `extract_path_from_uri` will fix this, since:
```
fsspec.core.url_to_fs("/somewhere/MNIST") ->  LocalFs, '/somewhere/MNIST'
fsspec.core.url_to_fs("file:///somewhere/MNIST") ->  LocalFs, '/somewhere/MNIST'
fsspec.core.url_to_fs("hdfs:///somewhere/MNIST") ->  HDFS, '/somewhere/MNIST'
```
and
```
fsspec.core.url_to_fs("file:///somewhere/MNIST") == fsspec.core.url_to_fs("/somewhere/MNIST")
```
In theory, this wouldn't break anything, since giving local path and remote uri still works. It will only affect local URI (make it works too)

### Environment info

- `datasets` version: 2.5.1
- Platform: Linux-5.4.205.1**HIDDEN**
- Python version: 3.7.10
- PyArrow version: 8.0.0
- Pandas version: 1.2.4


Comments:
Title: [Caching] Deterministic hashing of torch tensors
State: closed
Labels: description: New feature or request
name: enhancement

Currently this fails
```python
import torch
from datasets.fingerprint import Hasher

t = torch.tensor([1.])

def func(x):
    return t + x

hash1 = Hasher.hash(func)
t = torch.tensor([1.])
hash2 = Hasher.hash(func)
assert hash1 == hash2
```

Also as noticed in https://discuss.huggingface.co/t/dataset-cant-cache-models-outputs/24945, using a model in a `map` function doesn't work well with caching. Indeed the `bert-base-uncased` model has a different hash every time you reload it. Supporting torch tensors may also help in this case.

This can be fixed by registering a custom pickling functions for torch tensors - as we did for other objects such as CodeType, FunctionType and Regex in `py_utils.py`

Comments:
Title: Add "ipykernel" to list of `co_filename`s to remove
State: closed

Should resolve #5157 

Comments:
I don't know how I could add some tests for this, although jupyter is not among the dependencies so at least that would need to be added. If someone can tell a recommended way I will try to do it!

So testing by myself and looking around the jupyter codebase it looks like the `co_filename` of objects created within jupyter is of the form `f"{tempfile.tempdir}/ipykernel_{id1}/{id2}.py"` however I can't find the exact command setting it so I [asked in discourse](https://discourse.jupyter.org/t/co-filename-within-notebooks/16538). For now adapted the `co_filename` filter and added tests according to this I hope to get an answer and possibly fix based on that.

Ok ! I think it's fine to just check if the parent folder is named like `ipykernel_*` then

see the source code of how it's created:

https://github.com/ipython/ipykernel/blob/7f73ff705510b35d1e2faad7f5a676c620ce08d4/ipykernel/compiler.py#L72-L75

Should look better now didn't notice the duplicated tests

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

Should work now on windows too

I did the changes you suggested and tried to rebase, the first part went fine, the second less so :( 

If you have time to spare, can you tell me what should I do now to fix this? thanks

Instead of rebasing you can just merge `main` into your branch, otherwise the GitHub preview of your PR shows changes of from `main`.

Feel free to close this PR and create a new one. Or alternatively your can force push to this PR with a new clean git history.

I have force-pushed and merged main, only shows the right changes, if you can run CI one more time it should be ok now

Hi, sorry I have been busy, the thing is I can't really understand why the test fail, besides the ugly thing I had done in the last commit to check if within CI smth stange happened with `os`, locally tests pass

The CI wasn't passing when using the latest version `dill==0.3.6`. We have a separate function to dump CodeType objects for 0.3.6

I applied the same changes you did to this other function as well - it should be all good now

> The CI wasn't passing when using the latest version `dill==0.3.6`. We have a separate function to dump CodeType objects for 0.3.6
> 
> I applied the same changes you did to this other function as well - it should be all good now

Thanks, it would have taken a long time to figure out :)
Title: Fix CI require beam
State: closed

This PR:
- Fixes the CI `require_beam`: before it was requiring PyTorch instead
  ```python
  def require_beam(test_case):
      if not config.TORCH_AVAILABLE:
          test_case = unittest.skip("test requires PyTorch")(test_case)
      return test_case
  ```
- Fixes a missing `require_beam` in `test_beam_based_builder_download_and_prepare_as_parquet`
- Refactors `require_beam` to use `pytest` (`skipif`) instead

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

I'm merging this PR because it is quite a trivial fix and this is required by:
- #5166
Title: Add ffmpeg4 installation instructions in warnings
State: closed

Adds instructions on how to install `ffmpeg=4` on Linux (relevant for Colab users).

Looks pretty ugly because I didn't find a way to check `ffmpeg` version from python (without `subprocess.call()`; `ctypes.util.find_library` doesn't work`), so the warning is raised on each decoding. Any suggestions on how to make it look nice are welcome!

This is how it looks on Colab:
![image](https://user-images.githubusercontent.com/16348744/198052412-d48018d1-4416-4aa5-9114-f7f9b4af031f.png)


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

To make it warn only once, feel free to use a global counter in python - and if the warning has already been done, you don't do it again

> Added the same formatting for the error message :)

nice!! thank you! 

> Oh and regarding the warning counter, you can do it in another PR maybe ?

Yes, more warnings is better then no warnings.... I'll merge when the CI passes
Title: Memory explosion when trying to access 4d tensors in datasets cast to torch or np
State: open

### Describe the bug

When trying to access an item by index, in a datasets.Dataset cast to torch/np using `set_format` or `with_format`, we get a memory explosion if the item contains 4d (or above) tensors. 

### Steps to reproduce the bug

MWE:

```python
from datasets import load_dataset
import numpy as np

def create_4d_tensor(item):  
    i = item["num_nodes"]
    item["x_big"] = np.random.rand(i, 2*i, int(i/2), 1) + 1 # we create a big 4d tensor
    return item

if __name__ == "__main__":
    dataset = load_dataset(path=f"graphs-datasets/PROTEINS")

    # This works
    print(dataset["train"].format)
    print(dataset["train"][0].keys())

    dataset = dataset.map( 
        create_4d_tensor, 
        batched=False, 
        writer_batch_size=100, 
    )

    # This works
    print(dataset["train"].format)
    print(dataset["train"][0].keys())

    dataset.set_format("torch")
    
    print(dataset["train"].format)
    # This gets killed :(
    print(dataset["train"][0].keys())
```

The problem likely comes from `format_table` [here](https://cs.github.com/huggingface/datasets/blob/f09f781be3278156ce3aa6ec90c1926b1846a78f/src/datasets/arrow_dataset.py#L2328)

### Expected behavior

No memory explosion when trying to access dataset items after cast.

### Environment info

- `datasets` version: 2.3.2
- Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3

Comments:
Title: WIP: drop labels in Image and Audio folders by default
State: closed

will fix https://github.com/huggingface/datasets/issues/5153 and redundant labels displaying for most of the images datasets on the Hub (which are used just to store files)

TODO: discuss adding `drop_labels` (and `drop_metadata`) params to yaml

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

close in favor of https://github.com/huggingface/datasets/pull/5192
Title: Pip-compile: Could not find a version that matches dill<0.3.6,>=0.3.6
State: closed

### Describe the bug

When using `pip-compile` (part of `pip-tools`) to generate a pinned requirements file that includes `datasets`, a version conflict of `dill` appears.

It is caused by a transitive dependency conflict between `datasets` and `multiprocess`.

### Steps to reproduce the bug

```bash
$ echo "datasets" > requirements.in
$ pip install pip-tools
$ pip-compile requirements.in 
Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets==2.6.1->-r requirements.in (line 1))
Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6
Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1
There are incompatible versions in the resolved dependencies:
  dill<0.3.6 (from datasets==2.6.1->-r requirements.in (line 1))
  dill>=0.3.6 (from multiprocess==0.70.14->datasets==2.6.1->-r requirements.in (line 1))
```

### Expected behavior

A correctly generated file `requirements.txt` with pinned dependencies

### Environment info

Tested with versions `2.6.1, 2.6.0, 2.5.2` on Python 3.8 and 3.10 on Ubuntu 20.04LTS and Python 3.10 on MacOS 12.6 (M1).

Comments:
Thanks for reporting, @Rijgersberg.

We were waiting for the release of `dill` 0.3.6, that happened 2 days ago (24 Oct 2022): https://github.com/uqfoundation/dill/releases/tag/dill-0.3.6
- See comment: https://github.com/huggingface/datasets/pull/4397#discussion_r880629543

Also `multiprocess` 0.70.14 was released 2 days ago: https://github.com/uqfoundation/multiprocess/releases/tag/multiprocess-0.70.14

We are addressing this issue to align dependencies.

In your specific setup, I guess the compatible configuration is with `multiprocess` 0.70.13 (instead of 0.70.14).

@Rijgersberg this issue is fixed. It will be available in our next `datasets` release.

Thanks!

> @Rijgersberg this issue is fixed. It will be available in our next `datasets` release.

Any chance you have a eta? 

@StefanSamba we are disussing about making a release early this week.

@Rijgersberg, please also that you can make `pip-compile` work by using the backtracking resolver (instead of the legacy one): https://pip-tools.readthedocs.io/en/latest/#a-note-on-resolvers
```
pip-compile --resolver=backtracking requirements.in
```
This resolver will automatically use `multiprocess` 0.70.13 version.
Title: Dataset can’t cache model’s outputs
State: closed

### Describe the bug

Hi,

I try to cache some outputs of teacher model( Knowledge Distillation ) by using map function of Dataset library, while every time I run my code, I still recompute all the sequences. I tested Bert Model like this, I got different hash every single run, so any idea to deal with this?

### Steps to reproduce the bug

1. run below code
2. get different hash
```
from transformers import BertModel
from transformers import AutoTokenizer
import torch
token = ['hello']
model = BertModel.from_pretrained("bert-base-uncased").eval()
tok = AutoTokenizer.from_pretrained("bert-base-uncased")
def abcd():
    with torch.no_grad():
        
        out = model(**tok(token,return_tensors='pt'))[0]
        # out = tok(token)
    return out
from datasets.fingerprint import Hasher
my_func = abcd
print(Hasher.hash(my_func))

print(abcd())
```

### Expected behavior

I wanna cache all the model output 

### Environment info

datasets:2.5.0

Comments:
Addressed in https://github.com/huggingface/datasets/pull/5191 (torch.Tensor objects now produce deterministic hashes)
Title: Fix language and license tag names in all Hub datasets
State: closed
Labels: description: Contribution to a dataset script
name: dataset contribution

While working on this:
- #5137

we realized there are still many datasets with deprecated "languages" and "licenses" tag names (instead of "language" and "license").

This is a blocking issue: no subsequent PR can be opened to modify their metadata: a ValueError will be thrown.

We should fix the "language" and "license" tag names in all Hub datasets.

TODO:
- [x] Fix language and license tag names in 402 Hub datasets

CC: @julien-c 

Comments:
There are currently 402 datasets with deprecated "languages" or "licenses".

hey @albertvillanova ,i would love to work on this issue if you like.

Hi @ayushthe1, thanks for your offer.

But as you can see, I self-assigned this issue.

I have already fixed 200 out of the 402 datasets. My script is still running and fixing the rest.

For example: https://huggingface.co/datasets/fhamborg/news_sentiment_newsmtsc/discussions/2/files

Thanks for your time. Will try next time. 😇

@ayushthe1 feel free to take one of the non-assigned open issues: https://github.com/huggingface/datasets/issues

This is done.
Title: Consistent caching between python and jupyter
State: closed
Labels: description: New feature or request
name: enhancement

### Feature request

I hope this is not my mistake, currently if I use `load_dataset` from a python session on a custom dataset to do the preprocessing, it will be saved in the cache and in other python sessions it will be loaded from the cache, however calling the same from a jupyter notebook does not work, meaning the preprocessing starts from scratch.

If adjusting the hashes is impossible, is there a way to manually set dataset fingerprint to "force" this behaviour?

### Motivation

If this is not already the case and I am doing something wrong, it would be useful to have the two fingerprints consistent so one can create the dataset once and then try small things on jupyter without preprocessing everything again.


### Your contribution

I am happy to try a PR if you give me some pointers where the changes should happen

Comments:
Hi ! Maybe it's possible to have a consistent hash for a function defined in `__main__` and a function define in a notebook.

However for functions imported from another location, pickle uses the location to identify the code, so in that case we can't do much I believe.

Would it be ok for you if we only try to do this for functions in `__main__` / jupyter ?

If you'd like to contribute, you can read this part of the code and let me know if you have questions:

https://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/utils/py_utils.py#L617-L643

I think the key here would be to also ignore the "co_filename" of functions defined in `__main__`

Seems like a good solution, I will start a PR and see if I understood the changes needed. Thanks!
Title: TextConfig: added "errors"
State: closed

This patch adds the ability to set the `errors` option of `open` for loading text datasets. I needed it because some data I had scraped had bad bytes in it, so I needed `errors='ignore'`. 

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

Thanks for adding this ! You can fix the CI by formatting your code using the `make style` command :)

[**@lhoestq**](https://github.com/lhoestq) commented on [Oct 27, 2022, 4:08 PM GMT+3:30](https://github.com/huggingface/datasets/pull/5155#issuecomment-1293464680 "2022-10-27T12:38:04Z - Replied by Github Reply Comments"):
> Thanks for adding this ! You can fix the CI by formatting your code using the `make  style` command :)

I ran this and force pushed the changes.