sunny2309/githug-issues huggingface dataset:
load_dataset("financial_phrasebank") NonMatchingChecksumError

## Describe the bug
Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("financial_phrasebank", 'sentences_allagree')
```

## Expected results
I expect to see the financial_phrasebank dataset downloaded successfully

## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']

## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6
- Python version: 3.7.10
- PyArrow version: 4.0.1
Streaming for the Json loader

It was not using `open` in the builder. Therefore `pyarrow.json.read_json` was downloading the full file to start yielding rows.

Moreover, it appeared that `pyarrow.json.read_json` was not really suited for streaming as it was downloading too much data and failing if `block_size` was not properly configured (related to #2573).

So I switched to using `open` which is extended to support reading from remote file progressively, and I removed the pyarrow json reader which was not practical.
Instead, I'm using the classical `json.loads` from the standard library.
add image-classification task template

Snippet below is the tl;dr, but you can try it out directly here:

[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/nateraw/005c025d41f0e48ae3d4ee61c0f20b70/image-classification-task-template-demo.ipynb)

```python
from datasets import load_dataset

ds = load_dataset('nateraw/image-folder', data_files='PetImages/')
# DatasetDict({
#     train: Dataset({
#         features: ['file', 'labels'],
#         num_rows: 23410
#     })
# })

ds = ds.prepare_for_task('image-classification')
# DatasetDict({
#     train: Dataset({
#         features: ['image_file_path', 'labels'],
#         num_rows: 23410
#     })
# })
```
Load datasets from the Hub without requiring a dataset script

As a user I would like to be able to upload my csv/json/text/parquet/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script.

Moreover I would like to be able to specify which file goes into which split using the `data_files` argument.

This feature should be compatible with private repositories and dataset streaming.

This can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv/json/text/parquet/etc.)
can't set verbosity for `metric.py`

## Describe the bug
```
[2021-07-10 20:13:11,528][datasets.utils.filelock][INFO] - Lock 139705371374976 acquired on /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.lock
[2021-07-10 20:13:11,529][datasets.arrow_writer][INFO] - Done writing 32 examples in 6100 bytes /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.
[2021-07-10 20:13:11,531][datasets.arrow_dataset][INFO] - Set __getitem__(key) output type to python objects for no columns  (when key is int or slice) and don't output other (un-formatted) columns.
[2021-07-10 20:13:11,543][/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric.py][INFO] - Removing /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow
```
As you can see, `datasets` logging come from different places. 
`filelock`, `arrow_writer` & `arrow_dataset` comes from `datasets.*` which are expected 
However, `metric.py` logging comes from `/conda/envs/myenv/lib/python3.8/site-packages/datasets/`

So when setting `datasets.utils.logging.set_verbosity_error()`,  it still logs the last message which is annoying during evaluation. 

I had to do 
```
logging.getLogger("/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric").setLevel(logging.ERROR)
``` 
to fully mute these messages

## Expected results
it shouldn't log these messages when setting `datasets.utils.logging.set_verbosity_error()`

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: tried both 1.8.0 & 1.9.0
- Platform: Ubuntu 18.04.5 LTS 
- Python version: 3.8.10
- PyArrow version: 3.0.0
Integration with AugLy

**Is your feature request related to a problem? Please describe.**
Facebook recently launched a library, [AugLy](https://github.com/facebookresearch/AugLy) , that has a unified API for augmentations for image, video and text.

It would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial.

**Describe the solution you'd like**
The biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader.

**Describe alternatives you've considered**

One possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.
Add ASR task for SUPERB

This PR starts building up the SUPERB benchmark by including the ASR task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/v0.2.0/downstream#asr-automatic-speech-recognition).

Usage:

```python
from datasets import load_dataset 

asr = load_dataset("superb", "asr")
# DatasetDict({
#     train: Dataset({
#         features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
#         num_rows: 28539
#     })
#     validation: Dataset({
#         features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
#         num_rows: 2703
#     })
#     test: Dataset({
#         features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
#         num_rows: 2620
#     })
# })
```

I've used the GLUE benchmark as a guide for filling out the README.

To move fast during the evaluation PoC I propose to merge one task at a time, so we can continue building the training / evaluation framework in parallel.

Note: codewise this PR is ready for review - I'll add the missing YAML tags once #2620 is merged :)
`filelock.py` Error

## Describe the bug

It seems that the `filelock.py` went error. 

```
>>> ds=load_dataset('xsum')

^CTraceback (most recent call last):
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available
```

According to error log, it is OSError, but there is an `except` in the `_acquire` function.

```
    def _acquire(self):
        open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
        try:
            fd = os.open(self._lock_file, open_mode)
        except (IOError, OSError):
            pass
        else:
            self._lock_file_fd = fd
        return None
```

I don't know why it stucked rather than `pass` directly.

I am not quite familiar with filelock operation, so any help is highly appriciated.

## Steps to reproduce the bug
```python

ds = load_dataset('xsum')
```

## Expected results
A clear and concise description of the expected results.

## Actual results
```
>>> ds=load_dataset('xsum')

^CTraceback (most recent call last):
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 818, in load_dataset
    use_auth_token=use_auth_token,
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 470, in prepare_module
    with FileLock(lock_path):
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 323, in __enter__
    self.acquire()
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 272, in acquire
    self._acquire()
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
KeyboardInterrupt
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid
- Python version: 3.6.13
- PyArrow version: 4.0.1
Jsonlines export error

## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default

## Steps to reproduce the bug
This what I'm running:

in python:

```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```

then out of python:

```
head -10000 ptb.jsonl
```

## Expected results
Properly separated lines

## Actual results
The last line is a concatenation of two lines

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 4.0.1
Return Python float instead of numpy.float64 in sklearn metrics

This PR converts the return type of all `sklearn` metrics to be Python `float` instead of `numpy.float64`.

The reason behind this is that our Hub evaluation framework relies on converting benchmark-specific metrics to YAML ([example](https://huggingface.co/datasets/autonlp/autonlp-benchmark-raft-neelalex__raft-test-neelalex__raft-predictions-3/blob/main/README.md#L11)) and the `numpy.float64` format produces garbage like:

```python
import yaml
from datasets import load_metric

metric = load_metric("accuracy")
score = metric.compute(predictions=[0,1], references=[0,1])
print(yaml.dump(score["accuracy"])) # output below
# !!python/object/apply:numpy.core.multiarray.scalar
# - !!python/object/apply:numpy.dtype
#   args:
#   - f8
#   - false
#   - true
#   state: !!python/tuple
#   - 3
#   - <
#   - null
#   - null
#   - null
#   - -1
#   - -1
#   - 0
# - !!binary |
#   AAAAAAAA8D8=
```
[Metrics] addition of wiki_split metrics

**Is your feature request related to a problem? Please describe.**
While training the model on sentence split the task in English we require to evaluate the trained model on `Exact Match`, `SARI` and `BLEU` score
like this 
![image](https://user-images.githubusercontent.com/26653468/124746876-ff5a3380-df3e-11eb-9a01-4b48db7a6694.png)
While training we require metrics which can give all the output

Currently, we don't have an exact match for text normalized data

**Describe the solution you'd like**
A custom metrics for wiki_split that can calculate these three values and provide it in the form of a single dictionary
For exact match, we can refer to [this](https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py) 

**Describe alternatives you've considered**
Two metrics are already present one more can be added for an exact match then we can run all three metrics in training script

#self-assign
Add option to delete temporary files (e.g. extracted files) when loading dataset

I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
Unable to download omp dataset

## Describe the bug
The omp dataset cannot be downloaded because of a DuplicatedKeysError

## Steps to reproduce the bug
from datasets import load_dataset
omp = load_dataset('omp', 'posts_labeled')
print(omp)

## Expected results
This code should download the omp dataset and print the dictionary

## Actual results
Downloading and preparing dataset omp/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to /home/erika_distefano/.cache/huggingface/datasets/omp/posts_labeled/1.1.0/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b...
0 examples [00:00, ? examples/s]2021-07-06 09:43:55.868815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0
Traceback (most recent call last):      
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 990, in _prepare_split
    writer.write(example, key)
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 338, in write
    self.check_duplicate_keys()
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
    raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "hf_datasets.py", line 32, in <module>
    omp = load_dataset('omp', 'posts_labeled')
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
    use_auth_token=use_auth_token,
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 992, in _prepare_split
    num_examples, num_bytes = writer.finalize()
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 409, in finalize
    self.check_duplicate_keys()
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
    raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature

## Environment info
- `datasets` version: 1.8.0
- Platform: Ubuntu 18.04.4 LTS
- Python version: 3.6.9
- PyArrow version: 3.0.0
ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets

Error traceback:
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-8-a7b592d3bca0> in <module>()
      1 from datasets import load_dataset, load_metric
      2 
----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation")
      4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test")

9 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>()
     19 
     20 import datasets
---> 21 from datasets.tasks import AutomaticSpeechRecognition
     22 
     23 

ModuleNotFoundError: No module named 'datasets.tasks'
Cached dataset overflowing disk space

I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).

The cache folder is 500gb (and now my disk space is full).

Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).

This might not technically be a bug, but I was unsure and I felt that the bug was the closest one.

Traceback (most recent call last):
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker
    result = (True, func(*args, **kwds))
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper
    out = func(self, *args, **kwargs)
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single
    writer.finalize()
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize
    self.pa_writer.close()
  File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close
  File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device
"""

The above exception was the direct cause of the following exception:
Add skip and take

As discussed in https://github.com/huggingface/datasets/pull/2375#discussion_r657084544 I added the `IterableDataset.skip` and `IterableDataset.take` methods that allows to do basic splitting of iterable datasets.

You can create new dataset with the first `n` examples using `IterableDataset.take()`, or you can get a dataset with the rest of the examples by skipping the first `n` examples with `IterableDataset.skip()`

One implementation detail:

Using `take` (or `skip`) prevents future dataset shuffling from shuffling the dataset shards, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer.
I would have loved to allow the shards of the taken examples to be shuffled anyway, but since we don't know in advance the length of each shard we don't know what shards to take or skip.
I think this is ok though since users can shuffle before doing take or skip. I mentioned this in the documentation

cc @vblagoje @lewtun
Weights of model checkpoint not initialized for RobertaModel for Bertscore

When applying bertscore out of the box, 

```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```

Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html

```
from datasets import load_metric
metric = load_metric('bertscore')

# Example of typical usage
for batch in dataset:
    inputs, references = batch
    predictions = model(inputs)
    metric.add_batch(predictions=predictions, references=references)
score = metric.compute(lang="en")
#score = metric.compute(model_type="roberta-large") # gives the same error
```

I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo.... 

## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27
- Python version:  3.9.5
- PyArrow version: 3.0.0
Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`

## Describe the bug
If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.

## Steps to reproduce the bug

- Create a local dataset builder class
- load the local builder class file using `load_dataset` and let the cache build
- update the file's content
- The cache should rebuilt.

## Expected results

With `ignore_verifications=True`, `load_dataset` should pick up existing cache.

## Actual results

Creates new cache.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0
Memory usage consistently increases when processing a dataset with `.map`

## Describe the bug

I have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help.

## Steps to reproduce the bug

Providing code as it is would be hard. I can provide a MVP if that helps.

## Expected results

Memory usage should become consistent after some time following the launch of processing.

## Actual results

Memory usage keeps on increasing.

## Environment info

- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0
Better DuplicateKeysError error to help the user debug the issue

As mentioned in https://github.com/huggingface/datasets/issues/2552 it would be nice to improve the error message when a dataset fails to build because there are duplicate example keys.

The current one is
```python
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys should be unique and deterministic in nature
```

and we could have something that guides the user to debugging the issue:
```python
DuplicateKeysError: both 42th and 1337th examples have the same keys `48`.
Please fix the dataset script at <path/to/the/dataset/script>
```