helmo/github-issues huggingface dataset:
Add more compression types for `to_json`
State: closed | Author: bhavitvyamalik (CONTRIBUTOR)

This PR adds `bz2`, `xz`, and `zip` (WIP) for `to_json`. I also plan to add `infer` like how `pandas` does it

Comments: @lhoestq, I looked into how to compress with `zipfile` for which few methods exist, let me know which one looks good:
1. create the file in normal `wb` mode and then zip it separately
2. use `ZipFile.write_str` to write file into the archive. For this we'll need to change how we're writing files from `_write` method  

How `pandas` handles it is that they have created a wrapper for standard library class `ZipFile` and allow the returned file-like handle to accept byte strings via `write` method instead of `write_str` (purpose was to change the name of function by creating that wrapper)

1. sounds not ideal since it creates an intermediary file.
I like pandas' approach. Is it possible to implement 2. using the pandas class ? Or maybe we can have something similar ?

Definitely, @lhoestq! I've adapted that from original code and turns out it is faster than `gz` compression. Apart from that I've also added `infer` option to automatically infer compression type from `path_or_buf` given

One small thing, currently I'm assuming that user will provide compression extension in `path_or_buf`. Is it this also possible?
`dataset.to_json("from_dataset.json", compression="zip")`? 
Should I put an `assert` to ensure the file name provided always has a compression extension?

Thanks !

> One small thing, currently I'm assuming that user will provide compression extension in path_or_buf. Is it this also possible?
>dataset.to_json("from_dataset.json", compression="zip")?
>Should I put an assert to ensure the file name provided always has a compression extension?

I think it's fine as it is right now :) No need to check the extension of the filename passed to `path_or_buf`.


> turns out it is faster than gz compression

I think the default compression level of `gzip` is 9 in python, which is very slow. Maybe we can switch to compression level 6 instead which is faster, like the `gzip` command on unix

I found that `fsspec` has something that may interest you: [fsspec.open(..., compression=...)](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.open). I don't remember if we've already mentioned it or not

It also has `zip` if I understand correctly ! see https://github.com/fsspec/filesystem_spec/blob/master/fsspec/compression.py#L70

Since `fsspec` is a dependency of `datasets` we can use all this :)

Let me know if you prefer using `fsspec` instead (I haven't tested this yet to write compressed files). IMO it sounds pretty easy to use and it would make the code base simpler

Just tried `fsspec` but I'm not able to write compressed `zip` files :/
`gzip`, `xz`, `bz2` are all working fine and it's really simple (no need for `FileWriteHandler` now!)
Bug in `openbookqa` dataset
State: closed | Author: lucadiliello (CONTRIBUTOR)
Labels: name: bug

name: dataset bug

## Describe the bug
Dataset entries contains a typo.

## Steps to reproduce the bug

```python
>>> from datasets import load_dataset
>>> obqa = load_dataset('openbookqa', 'main')
>>> obqa['train'][0]
```

## Expected results
```python
{'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['A', 'B', 'C', 'D']}, 'answerKey': 'D'}
```

## Actual results
```python
{'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting']}, 'answerKey': 'D'}
```

The bug is present in all configs and all splits.

## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-5.4.0-1057-aws-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 4.0.1


Comments: Closed by:
- #4259
Fix sem_eval_2018_task_1 download location
State: closed | Author: maxpel (CONTRIBUTOR)

This changes the download location of sem_eval_2018_task_1 files to include the test set labels as discussed in https://github.com/huggingface/datasets/issues/2745#issuecomment-954588500_ with @lhoestq.

Comments: Hi ! Thanks for pushing this :)

It seems that you created this PR from an old version of `datasets` that didn't have the sem_eval_2018_task_1.py file.

Can you try merging `master` into your branch ? Or re-create your PR from a branch that comes from a more recent version of `datasets` ?

And sorry for the late response !

Hi! No problem! I made the new branch like you said and opened https://github.com/huggingface/datasets/pull/3643 for it. I will close this one.
Specify the feature types of a dataset on the Hub without needing a dataset script
State: closed | Author: lhoestq (MEMBER)
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**
Currently if I upload a CSV with paths to audio files, the column type is string instead of Audio.

**Describe the solution you'd like**
I'd like to be able to specify the types of the column, so that when loading the dataset I directly get the features types I want.

The feature types could read from the `dataset_infos.json` for example.

**Describe alternatives you've considered**
Create a dataset script to specify the features, but that seems complicated for a simple thing.

cc @abidlabs 

Comments: After looking into this, discovered that this is already supported if the `dataset_infos.json` file is configured correctly! Here is a working example: https://huggingface.co/datasets/abidlabs/test-audio-13

This should be probably be documented, though.
fix: 🐛 pass token when retrieving the split names
State: closed | Author: severo (COLLABORATOR)


Comments: Currently, it does not work with https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0/blob/main/common_voice_7_0.py#L146 (which was the goal), because `dl_manager.download_config.use_auth_token` is ignored, and the authentication is required to be use `huggingface-cli login`.
In my use case (dataset viewer), I'd prefer to use a specific "User Token Access", with only the "read" role (https://huggingface.co/settings/token).

See https://github.com/huggingface/datasets-preview-backend/issues/74#issuecomment-1007316853 for the context

> Simply passing download_config is ok :)

hmm, I prefer only passing use_auth_token. But the question is more: is it correct, in the (convoluted) case if `download_config.use_auth_token` exists and is different from `use_auth_token`? Which one should be used?

If both are passed, `use_auth_token` should have the priority (more specific parameters have the higher priority)
Ability to split a dataset in multiple files.
State: open | Author: Dref360 (CONTRIBUTOR)
Labels: name: enhancement

Hello,

**Is your feature request related to a problem? Please describe.**

My use case is that I have one writer that adds columns and multiple workers reading the same `Dataset`. Each worker should have access to columns added by the writer when they reload the dataset.

I understand that we shouldn't overwrite an arrow file as this could cause Segfault and so on. Before 1.16, I was able to overwrite the dataset and that would work most of the time with some retries.

**Describe the solution you'd like**

I was thinking that if we could append `Dataset._data_files`, when the workers reload the Dataset, they would get the new columns.


**Describe alternatives you've considered**
I currently need to
1. Save multiple "versions" of the dataset and load the latest.
2. Try working with cache files to get the latest columns.

**Additional context**

I think this would be a great addition to HFDataset as Parquet supports multi-files input out of the box!

I can make a PR myself with some pointers as needed :)
Allow loading community metrics from the hub, just like datasets
State: closed | Author: eladsegal (CONTRIBUTOR)
Labels: name: enhancement

name: generic discussion

**Is your feature request related to a problem? Please describe.**
Currently, I can load a metric implemented by me by providing the local path to the file in `load_metric`.
However, there is no option to do it with the metric uploaded to the hub.
This means that if I want to allow other users to use it, they must download it first which makes the usage less smooth.

**Describe the solution you'd like**
Load metrics from the hub just like datasets are loaded.
In order to not break stuff, the convention can be to put the metric file in a "metrics" folder in the hub.



Comments: Hi ! Thanks for your message :) This is a great idea indeed. We haven't started working on this yet though. For now I guess you can host your metric on the Hub (either with your model or your dataset) and use `hf_hub_download` to download it (docs [here](https://github.com/huggingface/huggingface_hub/blob/main/docs/hub/how-to-downstream.md#cached_download))

This is a great solution in the meantime, thanks!

Here's the code I used, in case it can be of help to someone else:
```python
import os, shutil
from huggingface_hub import hf_hub_download
def download_metric(repo_id, file_path):
    # repo_id: for models "username/model_name", for datasets "datasets/username/model_name"
    local_metric_path = hf_hub_download(repo_id=repo_id, filename=file_path)
    updated_local_metric_path = (os.path.dirname(local_metric_path) + os.path.basename(local_metric_path).replace(".", "_") + ".py")
    shutil.copy(local_metric_path, updated_local_metric_path)
    return updated_local_metric_path

metric = load_metric(download_metric(REPO_ID, FILE_PATH))
```

Solved with https://github.com/huggingface/evaluate 🤗 

Yay!! cc @lvwerra @sashavor @douwekiela 

Please share your feedback @eladsegal =)
Support 7-zip compressed data files
State: open | Author: albertvillanova (MEMBER)
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**

We should support 7-zip compressed data files:
- [x] in `extract`:
  - #4672 
- [ ] in `iter_archive`: for streaming mode

both in streaming and non-streaming modes.


Comments: This should also resolve: https://github.com/huggingface/datasets/issues/3185.
How to convert  torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?
State: open | Author: CindyTing (NONE)
Labels: name: enhancement

Hi,
I use torch.utils.data.Dataset to define my own data, but I need to use the 'map' function of datasets.arrow_dataset.Dataset later, so I hope to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset.

Here is an example.
```
from torch.utils.data import Dataset
from datasets.arrow_dataset import Dataset as HFDataset

class ADataset(Dataset):
    def __init__(self, data): 
        super().__init__()
        self.data = data
    def __getitem__(self, index):
        return self.data[index]
    def __len__(self):
        return self.len
class MDataset():
    def __init__(self, tokenizer: AutoTokenizer, data_args, training_args):
        self.train_dataset = ADataset(data_args)
        self.tokenizer = tokenizer
        self.data_args = data_args

        self.train_dataset = self.train_dataset.map(
            self.process_function,
            batched=True,
            remove_columns=column_names,
            load_from_cache_file=True,
            desc="Running tokenizer on train dataset",
             )
    def process_function(self, examples):
        sentences = [" ".join(sample[0][3]) for sample in examples]

        tokenized = self.tokenizer(
            sentences,
            max_length=self.max_seq_len,
            padding=self.padding, 
            truncation=True)
```
But it would raise an ERROR, AttributeError: 'ADataset' object has no attribute 'map'.

so how to convert  torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?

Thanks in advance!
Task search function on hub not working correctly
State: open | Author: patrickvonplaten (CONTRIBUTOR)
Labels: name: bug

When I want to look at all datasets of the category: `speech-processing`  *i.e.* https://huggingface.co/datasets?task_categories=task_categories:speech-processing&sort=downloads , then the following dataset doesn't show up for some reason:

- https://huggingface.co/datasets/speech_commands

even thought it's task tags seem correct: 
https://raw.githubusercontent.com/huggingface/datasets/master/datasets/speech_commands/README.md

Comments: known issue due to https://github.com/huggingface/datasets/pull/2362 (and [internal](https://github.com/huggingface/moon-landing/issues/946)) , will be solved soon

hmm actually i have no recollection of why I said that

Because it has dots in some YAML keys, it can't be parsed and indexed by the back-end
Give clearer instructions to add the YAML tags
State: closed | Author: albertvillanova (MEMBER)

Fix #3531.

CC: @julien-c @VictorSanh 

Comments: this is great, maybe just put all of it in one line?

> TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
Give clearer instructions to add the YAML tags
State: closed | Author: albertvillanova (MEMBER)
Labels: name: bug

## Describe the bug
As reported by @julien-c, many community datasets contain the line `YAML tags:` at the top of the YAML section in the header of the README file.  See e.g.: https://huggingface.co/datasets/bigscience/P3/commit/a03bea08cf4d58f268b469593069af6aeb15de32

Maybe we should give clearer instruction/hints in the README template.
Update license to bookcorpus dataset card
State: closed | Author: meg-huggingface (CONTRIBUTOR)
Labels: name: dataset contribution

Not entirely sure, following the links here, but it seems the relevant license is at https://github.com/soskek/bookcorpus/blob/master/LICENSE

Comments: The smashwords ToS apply for this dataset, we did the same for https://github.com/huggingface/datasets/pull/3525

_The documentation is not available anymore as the PR was closed or merged._
Adding license information for Openbookcorpus
State: closed | Author: meg-huggingface (CONTRIBUTOR)

Not entirely sure, following the links here, but it seems the relevant license is at https://github.com/soskek/bookcorpus/blob/master/LICENSE

Comments: The MIT license seems to be for the crawling code, no ? Then maybe we can also redirect users to the [terms of smashwords.com](https://www.smashwords.com/about/tos) regarding copyrights, in particular the paragraph 10 for end-users. In particular it seems that end users can download and use the content "for their personal enjoyment in any reasonable non-commercial manner in compliance with copyright law" and the smashwords end-users agreement.

It should be the same for https://github.com/huggingface/datasets/pull/3526 as well

May I merge this one ?

_The documentation is not available anymore as the PR was closed or merged._
wmt19 is broken (zh-en)
State: closed | Author: AjayP13 (NONE)
Labels: name: bug

name: dataset bug

## Describe the bug
A clear and concise description of what the bug is.

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("wmt19", 'zh-en')
```

## Expected results
The dataset should download.

## Actual results
`ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip`

## Environment info
- `datasets` version: 1.15.1
- Platform: Linux
- Python version: 3.8 


Comments: This issue is not reproducible.
Audio datacard update - first pass
State: closed | Author: meg-huggingface (CONTRIBUTOR)

Filling out data card "Personal and Sensitive Information" for speech datasets to note PII concerns

Comments: I'm not sure that we want to change the tags at the top of the cards by hand. Those are used to create the tags in the hub. Although looking at all the tags now, we might want to normalize the current tags again (hyphens or no, ".0" or no). Maybe we could add a binary tag for public domain or not?

> 

That's a good point, I didn't realize these were auto-populated.
At the same time, some of them are wrong -- how/where are they auto-populated?  Seems like we should fix it at that source for the future.
In the mean time, I see that "cc0-1.0" is the desired tag for public domain, so I will change that for now.
Add PubMed Central Open Access dataset
State: closed | Author: albertvillanova (MEMBER)
Labels: name: dataset request

## Adding a Dataset
- **Name:** PubMed Central Open Access
- **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
- **Motivation:** *what are some good reasons to have this dataset*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments: In the framework of BigScience:
- bigscience-workshop/data_tooling#121

I have created this dataset as a community dataset: https://huggingface.co/datasets/albertvillanova/pmc_open_access

However, I was wondering that it may be more appropriate to move it under an org namespace: `pubmed_central` or `pmc`
This way, we could add other datasets I'm also working on: Author Manuscript Dataset, Historical OCR Dataset, LitArch Open Access Subset.

What do you think? @lhoestq @mariosasko 

Why not ! Having them under such namespaces would also help people searching for this kind of datasets.
We can also invite people from pubmed at one point

DONE: https://huggingface.co/datasets/pmc/open_access
Fix to_tf_dataset references in docs
State: closed | Author: mariosasko (COLLABORATOR)

Fix the `to_tf_dataset` references in the docs. The currently failing example of usage will be fixed by #3338.  

Comments: The code snippet in [this section](https://huggingface.co/docs/datasets/master/use_dataset.html?highlight=to_tf_dataset#tensorflow) is missing an import (`DataCollatorWithPadding`) and doesn't initialize the TF model before the `model.fit` call.
Dataset
State: closed | Author: MIKURI0114 (NONE)
Labels: name: dataset-viewer

## Dataset viewer issue for '*name of the dataset*'

**Link:** *link to the dataset viewer page*

*short description of the issue*

Am I the one who added this dataset ? Yes-No


Comments: Can you reopen with the correct dataset name (if relevant)?

Thanks

The dataset viewer was down tonight. It works again.
`wiki_dpr` details for Open Domain Question Answering tasks
State: closed | Author: pk1130 (NONE)

Hey guys!

Thanks for creating the `wiki_dpr` dataset!

I am currently trying to use the dataset for context retrieval using DPR on NQ questions and need details about what each of the files and data instances mean, which version of the Wikipedia dump it uses, etc. Please respond at your earliest convenience regarding the same! Thanks a ton!

P.S.: (If one of @thomwolf @lewtun @lhoestq could respond, that would be even better since they have the first-hand details of the dataset. If anyone else has those, please reach out! Thanks!)

Comments: Hi ! According to the DPR paper, the wikipedia dump is the one from Dec. 20, 2018.
Each instance contains a paragraph of at most 100 word, as well as the title of the wikipedia page it comes from and the DPR  embedding (a 768-d vector).

Closed by:
- #3534
Batched in filter throws error
State: closed | Author: gpucce (CONTRIBUTOR)
Labels: name: bug

I hope this is really a bug, I could not find it among the open issues

## Describe the bug
using `batched=False` in DataSet.filter throws error
```python
TypeError: filter() got an unexpected keyword argument 'batched'
```
but in the docs it is lister as an argument.

## Steps to reproduce the bug
```python
task = "mnli"
max_length = 128
tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/")

dataset = load_dataset("glue", task)

task_to_keys = {
    "cola": ("sentence", None),
    "mnli": ("premise", "hypothesis"),
    "mnli-mm": ("premise", "hypothesis"),
    "mrpc": ("sentence1", "sentence2"),
    "qnli": ("question", "sentence"),
    "qqp": ("question1", "question2"),
    "rte": ("sentence1", "sentence2"),
    "sst2": ("sentence", None),
    "stsb": ("sentence1", "sentence2"),
    "wnli": ("sentence1", "sentence2"),
}

##### tokenization_parameters
sentence1_key, sentence2_key = task_to_keys[task]
def preprocess_function(examples, max_length):
    if sentence2_key is None:
        return tokenizer(
            examples[sentence1_key], truncation=True, max_length=max_length
        )
    return tokenizer(
        examples[sentence1_key],
        examples[sentence2_key],
        truncation=False,
        padding="max_length",
        max_length=max_length,
    )

encoded_dataset = dataset.map(
    lambda x: preprocess_function(x, max_length=max_length), batched=False
)

encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False)
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1, 1.17.0
- Platform: ubuntu
- Python version: 3.8.12
Add QuALITY
State: closed | Author: jaketae (CONTRIBUTOR)
Labels: name: dataset contribution

Fixes #3441.

Comments: Thanks for your contribution, @jaketae. Are you still interested in adding this dataset?

We are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets

We would suggest you create this dataset there. Please, feel free to tell us if you need some help.
Adjusting chunk size for streaming datasets
State: closed | Author: JoelNiklaus (CONTRIBUTOR)
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**
I want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing. 

**Describe the solution you'd like**
I would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.



Comments: Hi ! Data streaming uses `fsspec` to read the data files progressively. IIRC the block size for buffering is 5MiB by default. So every time you finish iterating over a block, it downloads the next one. You can still try to increase the `fsspec` block size for buffering if it can help. To do so you just need to increase `fsspec.spec.AbstractBufferedFile.DEFAULT_BLOCK_SIZE `

Currently this is unfortunately done in a single thread, so it blocks the processing to download and uncompress the next block. At one point it would be nice to be able to do that in parallel !

Hi! Thanks for the help, I will try it :)
update `pretty_name` for first 200 datasets
State: closed | Author: bhavitvyamalik (CONTRIBUTOR)

I made a script some time back to fetch `pretty_names` from `papers_with_code` dataset along with some other rules incase that dataset wasn't available on `papers_with_code`. Updating them in the `README` of `datasets`. Took only the first 200 datasets into consideration and after some eyeballing, most of them were looking good to me!