KRadim/github-issues-minimal huggingface dataset:
Allow downloading/processing/caching only specific splits
State: open
Labels: name: enhancement

Allow downloading/processing/caching only specific splits without downloading/processing/caching the other splits.

This PR implements two steps to handle only specific splits:
- it allows processing/caching only specific splits into Arrow files
- for some simple cases, it allows downloading only specific splits (which is more intricate as it depends on the user-defined method `_split_generators`)

This PR makes several assumptions:
- `DownloadConfig` contains the configuration settings for downloading
- the parameter `split` passed to `load_dataset` is just a parameter for loading (from cache), not for downloading
Faster map w/ input_columns & faster slicing w/ Iterable keys
State: closed

@lhoestq Fixes #2193 

- `map` now uses `with_format` to only load needed columns in memory when `input_columns` is set
- Slicing datasets with Iterables of indices now uses a new `Table.fast_gather` method, implemented with `np.searchsorted`, to find the appropriate batch indices all at once. `pa.concat_tables` is no longer used for this; we just call `pa.Table.from_batches` with a list of all the batch slices.

Together these changes have sped up batched `map()` calls over subsets of columns quite considerably in my initial testing.
Add `key` type and duplicates verification with hashing
State: closed

Closes #2230 
There is currently no verification for the data type and the uniqueness of the keys yielded by the `dataset_builder`.
This PR is currently a work in progress with the following goals:

-  [x] Adding `hash_salt` to `ArrowWriter` so that the keys belonging to different splits have different hash
-  [x] Add `key` arrtibute to `ArrowWriter.write()` for hashing
-  [x] Add a hashing class which takes an input key of certain type (`str`/`int`/anything convertible to string) and produces a 128-bit hash using `hashlib.md5`
-  [x] Creating a function giving a custom error message when non-unique keys are found 
   **[This will take care of type-checking for keys]**
-  [x] Checking for duplicate keys in `writer.write()` for each batch

[**NOTE**: This PR is currently concerned with `GeneratorBasedBuilder` only, for simplification. A subsequent PR will be made in future for `ArrowBasedBuilder`]

@lhoestq Thank you for the feedback. It would be great to have your guidance on this!
Map is slow and processes batches one after another
State: closed
Labels: name: bug

## Describe the bug

I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. 

I process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.

pseudo code:
```python
ds = datasets.load_from_disk("path")
new_dataset = ds.map(work, batched=True, ...)  # fast uses all processes
final_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another
```

## Expected results
Second stage should be as fast as the first stage.

## Versions
Paste the output of the following code:
- Datasets: 1.5.0
- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)
- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    

Do you guys have any idea? Thanks a lot!
Link to datasets viwer on Quick Tour page returns "502 Bad Gateway"
State: closed
Labels: name: bug

Link to datasets viwer (https://huggingface.co/datasets/viewer/)  on Quick Tour page  (https://huggingface.co/docs/datasets/quicktour.html) returns "502 Bad Gateway"

The same error with https://huggingface.co/datasets/viewer/?dataset=glue&config=mrpc
Request to add StrategyQA dataset
State: open
Labels: name: dataset request

## Request to add StrategyQA dataset
- **Name:** StrategyQA
- **Description:** open-domain QA [(project page)](https://allenai.org/data/strategyqa)
- **Paper:** [url](https://arxiv.org/pdf/2101.02235.pdf)
- **Data:** [here](https://allenai.org/data/strategyqa)
- **Motivation:** uniquely-formulated dataset that also includes a question-decomposition breakdown and associated Wikipedia annotations for each step. Good for multi-hop reasoning modeling.
`xnli` dataset creating a tuple key while yielding instead of `str` or `int`
State: closed

When using  `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196

Since, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset. 
I'm up for sending a fix for this, I think we can simply use `file_idx + "_" + row_idx` as a unique key instead of a tuple.
Batched map fails when removing all columns
State: closed
Labels: name: bug

Hi @lhoestq ,

I'm hijacking this issue, because I'm currently trying to do the approach you recommend:

> Currently the optimal setup for single-column computations is probably to do something like
> 
> ```python
> result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names)
> ```

Here is my code: (see edit, in which I added a simplified version

```
This is the error:
```bash
pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000
```
I wonder why this error occurs, when I delete every column? Can you give me a hint?

### Edit:
I preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error?  I can iterate over the
complete dataset and print every sample before calling map. There seems to be no other problem with the dataset.

I tried to simplify the code that crashes:

```python
# works
log.debug(dataset.column_names)
log.debug(dataset)
for i, sample in enumerate(dataset):
    log.debug(i, sample)

# crashes
counted_dataset = dataset.map(
    lambda x: {"a": list(range(20))},
    input_columns=column,
    remove_columns=dataset.column_names,
    load_from_cache_file=False,
    num_proc=num_workers,
    batched=True,
)
```

```
pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000
```

Edit2: 

May this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:

```python
# crashes
counted_dataset = dataset.map(
    lambda x: {"a": list(range(20))},
    input_columns=column,
    remove_columns=dataset.column_names,
    load_from_cache_file=False,
    num_proc=num_workers,
    batched=True,
    features=datasets.Features(
        {
              "a": datasets.Sequence(datasets.Value("int32"))
         }
    )
)
```

```
 File "env/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1704, in _map_single
    writer.write_batch(batch)
  File "env/lib/python3.8/site-packages/datasets/arrow_writer.py", line 312, in write_batch
    col_type = schema.field(col).type if schema is not None else None
  File "pyarrow/types.pxi", line 1341, in pyarrow.lib.Schema.field
KeyError: 'Column tokens does not exist in schema'
```

_Originally posted by @villmow in https://github.com/huggingface/datasets/issues/2193#issuecomment-820230874_
Add SLR70 - SLR80 and SLR86 to OpenSLR dataset
State: closed

I would like to add SLR70, SLR71, SLR72, SLR73, SLR74, SLR75, SLR76, SLR77, SLR78, SLR79, SLR80 and SLR86 to OpenSLR dataset. The languages are:
Nigerian English, Chilean Spanish, Columbian Spanish, Peruvian Spanish, Puerto Rico Spanish, Venezuelan Spanish, Basque, Galician, Gujarati and Kannada.
Duplicates in the LAMA dataset
State: open

I observed duplicates in the LAMA probing dataset, see a minimal code below. 

```
>>> import datasets
>>> dataset = datasets.load_dataset('lama')
No config specified, defaulting to: lama/trex
Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc)
>>> train_dataset = dataset['train']
>>> train_dataset[0]
{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}
>>> train_dataset[1]
{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}
```

I checked the original data available at https://dl.fbaipublicfiles.com/LAMA/data.zip. This particular duplicated comes from:
```
{"uuid": "40b2ed1c-0961-482e-844e-32596b6117c8", "obj_uri": "Q150", "obj_label": "French", "sub_uri": "Q441235", "sub_label": "Louis Jules Trochu", "predicate_id": "P103", "evidences": [{"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}, {"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}]}
``` 

What is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?
Revert breaking change in cache_files property
State: closed

#2025 changed the format of `Dataset.cache_files`.
Before it was formatted like
```python
[{"filename": "path/to/file.arrow", "start": 0, "end": 1337}]
```
and it was changed to
```python
["path/to/file.arrow"]
```
since there's no start/end offsets available anymore.

To make this less breaking, I'm setting the format back to a list of dicts:
```python
[{"filename": "path/to/file.arrow"}]
```
Add datasets SLR35 and SLR36 to OpenSLR 
State: closed

I would like to add [SLR35](https://openslr.org/35/) (18GB) and [SLR36](https://openslr.org/36/)  (22GB) which are Large Javanese and Sundanese ASR training data set collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia.
load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
State: closed
Labels: name: bug

I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.

```python
>>> from datasets import load_metric
>>> metric = load_metric("glue", "sst2")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 502, in load_metric
  File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 66, in import_main_class
  File "/ext3/miniconda3/lib/python3.8/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
  File "<frozen importlib._bootstrap>", line 991, in _find_and_load
  File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 671, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 783, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/home/ns4008/.cache/huggingface/modules/datasets_modules/metrics/glue/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de/glue.py", line 105, in <module>
    @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
AttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
```
Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset
State: closed

I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running: 

```Python
fquad = load_dataset("fquad")
```

which produces the following error:

```
Using custom data configuration default

Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to /root/.cache/huggingface/datasets/fquad/default/0.1.0/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...

---------------------------------------------------------------------------

ConnectionError                           Traceback (most recent call last)

<ipython-input-48-a2721797e23b> in <module>()
----> 1 fquad = load_dataset("fquad")

11 frames

/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
    614             raise FileNotFoundError("Couldn't find file at {}".format(url))
    615         _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
--> 616         raise ConnectionError("Couldn't reach {}".format(url))
    617 
    618     # Try a second time

ConnectionError: Couldn't reach https://storage.googleapis.com/illuin/fquad/train.json.zip
```

Does anyone know why that is and how to fix it?
Getting checksum error when trying to load lc_quad dataset
State: closed

I'm having issues loading the [lc_quad](https://huggingface.co/datasets/fquad) dataset by running:

```Python
lc_quad = load_dataset("lc_quad")
```

which is giving me the following error:

``` 
Using custom data configuration default

Downloading and preparing dataset lc_quad/default (download: 3.69 MiB, generated: 19.77 MiB, post-processed: Unknown size, total: 23.46 MiB) to /root/.cache/huggingface/datasets/lc_quad/default/2.0.0/5a98fe174603f5dec6df07edf1c2b4d2317210d2ad61f5a393839bca4d64e5a7...

---------------------------------------------------------------------------

NonMatchingChecksumError                  Traceback (most recent call last)

<ipython-input-42-404ace83f73c> in <module>()
----> 1 lc_quad = load_dataset("lc_quad")

3 frames

/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
     37     if len(bad_urls) > 0:
     38         error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))
     40     logger.info("All the checksums matched successfully" + for_verification_name)
     41 

NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/AskNowQA/LC-QuAD2.0/archive/master.zip']
```

Does anyone know why this could be and how I fix it?
making labels consistent across the datasets
State: closed

Hi
For accessing the labels one can type 
```
>>> a.features['label']
ClassLabel(num_classes=3, names=['entailment', 'neutral', 'contradiction'], names_file=None, id=None)
```
The labels however are not consistent with the actual labels sometimes, for instance in case of XNLI, the actual labels are 0,1,2, but if one try to access as above they are entailment, neutral,contradiction,
it would be great to have the labels consistent.

thanks
Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
State: closed
Labels: name: bug

I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:

Traceback (most recent call last):
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_single
    writer.write(example)
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 296, in write
    self.write_on_file()
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 270, in write_on_file
    pa_array = pa.array(typed_sequence)
  File "pyarrow/array.pxi", line 222, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 108, in __arrow_array__
    out = out.cast(pa.list_(self.optimized_int_type))
  File "pyarrow/array.pxi", line 810, in pyarrow.lib.Array.cast
  File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/pyarrow/compute.py", line 281, in cast
    return call_function("cast", [arr], options)
  File "pyarrow/_compute.pyx", line 465, in pyarrow._compute.call_function
  File "pyarrow/_compute.pyx", line 294, in pyarrow._compute.Function.call
  File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127

Do you have any idea about it?
Fix ArrowWriter overwriting features in ArrowBasedBuilder
State: closed

This should fix the issues with CSV loading experienced in #2153 and #2200.

The CSV builder is an ArrowBasedBuilder that had an issue with its ArrowWriter used to write the arrow file from the csv data.
The writer wasn't initialized with the features passed by the user. Therefore the writer was inferring the features from the arrow data, discarding the features passed by the user.

I fixed that and I updated the tests
_prepare_split will overwrite DatasetBuilder.info.features
State: closed

Hi, here is my issue:
I initialized a Csv datasetbuilder with specific features:
```
def get_dataset_features(data_args):
    features = {}
    if data_args.text_features:
        features.update({text_feature: hf_features.Value("string") for text_feature in data_args.text_features.strip().split(",")})
    if data_args.num_features:
        features.update({text_feature: hf_features.Value("float32") for text_feature in data_args.num_features.strip().split(",")})
    if data_args.label_classes:
        features["label"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(","))
    else:
        features["label"] = hf_features.Value("float32")
    return hf_features.Features(features)

datasets = load_dataset(extension,
                                data_files=data_files,
                                sep=data_args.delimiter,
                                header=data_args.header,
                                column_names=data_args.column_names.split(",") if data_args.column_names else None,
                                features=get_dataset_features(data_args=data_args))
```
The `features` is printout as below before `builder_instance.as_dataset` is called:
```
{'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}
````

But after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to:
```
{'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}
```

After digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`. 
But `ArrowWriter` is initailized without passing `features`.
So my concern is:
It's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function?
added file_permission in load_dataset
State: closed

As discussed in #2065 I've added `file_permission` argument in `load_dataset`. 

Added mainly 2 things here:
1) Permission of downloaded datasets when converted to .arrow files  can be changed with argument `file_permission` argument in `load_dataset` (default is 0o644 only)
2) Incase the user uses `map` later on to generate another cache file of dataset, it ensures the permissions of newly generated file are similar to that of` *-train.arrow` file inside cache_dir for that dataset.
`load_dataset` caches two arrow files?
State: closed
Labels: name: question

Hi,

I am using datasets to load large json file of 587G.
I checked the cached folder and found that there are two arrow files created:
* `cache-ed205e500a7dc44c.arrow` - 355G
*  `json-train.arrow` - 582G

Why is the first file created?
If I delete it, would I still be able to `load_from_disk`?
KeyError: '_indices_files' in `arrow_dataset.py`
State: closed
Labels: name: bug

After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset.

Trace:
```
Traceback (most recent call last):
  File "load_data.py", line 11, in <module>
    dataset = load_from_disk(SRC)
  File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/load.py", line 784, in load_from_disk
    return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)
  File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/dataset_dict.py", line 692, in load_from_disk
    dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)
  File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 634, in load_from_disk
    if state["_indices_files"]:
KeyError: '_indices_files'
```

I believe this is the line causing the error since there may not be a `_indices_files` key in the older versions:
https://github.com/huggingface/datasets/blob/b70141e3c5149430951773aaa0155555c5fb3e76/src/datasets/arrow_dataset.py#L634

May I suggest using `state.get()` instead of directly indexing the dictionary?

@lhoestq
Filtering/mapping on one column is very slow
State: closed
Labels: name: question

I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.

I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that— I'm not very familiar with the pyarrow API.

I know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.

PS: This is definitely not a "dataset request." I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.
News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
State: closed

I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi.  

```
train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]')
val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]')

# filtering out examples that are not ar-en translations but ar-hi
val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True)
```

* I'm fairly new to using datasets so I might be doing something wrong
save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object.
State: closed

As you can see, it saves the entire dataset.

@lhoestq 

You can  check by going through the following example,

```
from datasets import load_from_disk,concatenate_datasets

loaded_data=load_from_disk('/home/gsir059/HNSW-ori/my_knowledge_dataset')
n=20
kb_list=[loaded_data.shard(n, i, contiguous=True) for i in range(n)]
final_dataset=concatenate_datasets([kb_list[1],kb_list[2]])
final_dataset.save_to_disk('/home/gsir059/haha/k.arrow')
```
Duplicate data in Timit dataset
State: closed

I ran a simple code to list all texts in Timit dataset and the texts were all the same.
Is this dataset corrupted?
**Code:**
timit = load_dataset("timit_asr")
print(*timit['train']['text'], sep='\n')
**Result:**
Would such an act of refusal be useful?
Would such an act of refusal be useful?
Would such an act of refusal be useful?
Would such an act of refusal be useful?
...
...
Would such an act of refusal be useful?
Question (potential issue?) related to datasets caching
State: open
Labels: name: question

I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():

    # disable caching in datasets
    set_caching_enabled(False)
```
However, in my log files I see messages like the following:

```
04/07/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877
04/07/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx/cache-transformers/datasets/csv/default-888a87931cbc5877/0.0.0/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93
```
Can you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!
.map() and distributed training
State: closed

Hi,
I have a question regarding distributed training and the `.map` call on a dataset.

I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_path=my_path)

[...]

def tokenize_function(examples):
    return tokenizer(examples[text_column_name])

logger.info("Mapping dataset to tokenized dataset.")
tokenized_datasets = datasets.map(
    tokenize_function,
    batched=True,
    num_proc=preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=True,
)
```
I am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path/train` (there is only a train split).
When I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.

Everything so far was done by launching a **single process script**.
I now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. 

I tried adding the `cache_file_name` argument: `cache_file_name={"train": my_path/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.

**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.

- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_clm.py) in my case)
- I am using 1.5.0 version of datasets if that matters.
Fix s3fs tests for py36 and py37+
State: closed

Recently several changes happened:
1. latest versions of `fsspec` require python>3.7 for async features
2. `s3fs` added a dependency on `aiobotocore`, which is not compatible with the `moto` s3 mock context manager

This PR fixes both issues, by pinning `fsspec` and `s3fs` for python 3.6, and by using `moto` in server mode to support running the tests on python>=3.7 with the latest version of `fsspec` and `s3fs`.

cc @philschmid
Set default in-memory value depending on the dataset size
State: closed
Labels: name: enhancement

Set a default value for `in_memory` depending on the size of the dataset to be loaded.

Close #2179.

TODO:
- [x] Add a section in the docs about this.
- ~Add a warning if someone tries to specify `cache_file_name=` in `map`, `filter` etc. on a dataset that is in memory, since the computation is not going to be cached in this case.~
Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)
State: closed

Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.
When loading a huge json file of 500GB, pyarrow complains as follows:
```
Traceback (most recent call last):
  File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 531, in incomplete_dir
    yield tmp_dir
  File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 573, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 650, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 1027, in _prepare_split
    for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
  File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
    for obj in iterable:
  File "/app/.cache/huggingface/modules/datasets_modules/datasets/json/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641/json.py", line 83, in _generate_tables
    parse_options=self.config.pa_parse_options,
  File "pyarrow/_json.pyx", line 247, in pyarrow._json.read_json
  File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```
When using only a small portion of the sample file, say first 100 lines, it works perfectly well..

I see that it is the error from pyarrow, but could you give me a hint or possible solutions?
#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!
Load small datasets in-memory instead of using memory map
State: closed
Labels: name: enhancement, name: generic discussion

Currently all datasets are loaded using memory mapping by default in `load_dataset`.
However this might not be necessary for small datasets. If a dataset is small enough, then it can be loaded in-memory and:
- its memory footprint would be small so it's ok
- in-memory computations/queries would be faster
- the caching on-disk would be disabled, making computations even faster (no I/O bound because of the disk)
- but running the same computation a second time would recompute everything since there would be no cached results on-disk. But this is probably fine since computations would be fast anyway + users should be able to provide a cache filename if needed.

Therefore, maybe the default behavior of `load_dataset` should be to load small datasets in-memory and big datasets using memory mapping.
Fix cast memory usage by using map on subtables
State: closed
Labels: name: enhancement

The `cast` operation on a pyarrow Table may create new arrays in memory.
This is an issue since users expect memory mapped datasets to not fill up the RAM.

To fix that I used `map` to write a new arrow file on disk when cast is used.
To make things more convenient I introduced the `arrow` formatting of a dataset, to make it return pyarrow tables instead of python dicts. This way one can use pyarrow transforms directly when using `map`.

edit: we'll use the same mechanism for `filter`
add social thumbnial
State: closed

# What does this PR do?

I added OpenGraph/ Twitter Card support to the docs to create nice social thumbnails.

![Bildschirmfoto 2021-04-07 um 08 36 50](https://user-images.githubusercontent.com/32632186/113821698-bac2ce80-977c-11eb-81aa-d8f16355857e.png)

To be able to add these I needed to install `sphinxext-opengraph`. I came across this [issue](https://github.com/readthedocs/readthedocs.org/issues/1758) on the readthedocs repo saying that since someone has built this plugin they are not integrating and providing documentation to it. That's why I added it for creating the documentation. The repository can be found [here](https://github.com/wpilibsuite/sphinxext-opengraph/tree/main).

P.S. It seemed that `make style` never ran for `docs/` i hope the changes are okay otherwise I'll revert it.
dataset.search_batch() function outputs all -1 indices sometime.
State: closed

I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, "IVF65536_HNSW32,Flat")**.

During the retrieval phase exactly in [this line of retrieval_rag.py](https://github.com/huggingface/transformers/blob/master/src/transformers/models/rag/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. 

![image](https://user-images.githubusercontent.com/16892570/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)


Here, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?

Is this a problem of the index, where the faiss can't find any similar vector?
Is there documentation on the output index being -1?

@lhoestq
Pin docutils for better doc
State: closed

The latest release of docutils make the navbar in the documentation weird and the Markdown wrongly interpreted:

![image](https://user-images.githubusercontent.com/35901082/113711773-5be55280-96b3-11eb-9b3b-9794f17709aa.png)

We had the same problem in Transformers and solved it by pinning docutils (a dep of sphinx).

You can see the version after the change [here](https://32769-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html).
Add OpenSLR dataset
State: closed

OpenSLR (https://openslr.org/) is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition. There are around 80 speech datasets listed in OpenSLR, currently this PR includes only 9 speech datasets SLR41, SLR42, SLR43, SLR44, SLR63, SLR64, SLR65, SLR66 and SLR69 (Javanese, Khmer, Nepali and Sundanese, Malayalam, Marathi, Tamil, Telugu and Catalan). I can add other speech datasets gradually next time.
Pin fsspec lower than 0.9.0
State: closed

Today's release of `fsspec` 0.9.0 implied a new release of `s3fs` 0.6.0 but this version breaks the CI (see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/5312/workflows/490f3240-cd1c-4dd1-bb60-b416771c5584/jobs/32734) for example)

I'm pinning `fsspec` until this has been resolved
Preserve split type when realoding dataset
State: closed

Fixes #2167 

Using `eval` is not ideal for security reasons (in web apps I assume), but without it the code would be much more complex IMO.

In terms of style, instead of explicitly importing a private member (`_RelativeInstruction`), we can add these imports at the top of the module:
```python
from . import arrow_reader  # gives us access to ReadInstruction and _RelativeInstruction
from . import splits # gives us access to NamedSplit
```

and then define the `eval` globals as follows:
```python
{**arrow_reader.__dict__, **splits.__dict__}
```
Split type not preserved when reloading the dataset
State: closed

A minimal reproducible example:
```python
>>> from datasets import load_dataset, Dataset
>>> dset = load_dataset("sst", split="train")
>>> dset.save_to_disk("sst")
>>> type(dset.split)
<class 'datasets.splits.NamedSplit'>
>>> dset = Dataset.load_from_disk("sst")
>>> type(dset.split)  # NamedSplit expected
<class 'str'>
```

It seems like this bug was introduced in #2025.
Regarding Test Sets for the GEM datasets
State: closed
Labels: name: Dataset discussion

@yjernite Hi, are the test sets for the GEM datasets scheduled to be [added soon](https://gem-benchmark.com/shared_task)? 

e.g.

```
from datasets import load_dataset
DATASET_NAME="common_gen"
data = load_dataset("gem", DATASET_NAME)
```

The test set doesn't have the target or references.

```
data['test'][0]
{'concept_set_id': 0, 'concepts': ['drill', 'field', 'run', 'team'], 'gem_id': 'common_gen-test-0', 'gem_parent_id': 'common_gen-test-0', 'references': [], 'target': ''}
```
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
State: closed

Hi,

I'm trying to pretraine deep-speed model using HF arxiv dataset like:
```
train_ds = nlp.load_dataset('scientific_papers', 'arxiv')
train_ds.set_format(
        type="torch",
        columns=["input_ids", "attention_mask", "global_attention_mask", "labels"],
    )
engine, _, _, _ = deepspeed.initialize(
    args=args,
    model=model,
    model_parameters=[p for p in model.parameters() if p.requires_grad],
    training_data=train_ds)
```
but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
data_args.preprocessing_num_workers almost freezes 
State: closed

Hi @lhoestq 

I am running this code from huggingface transformers https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py 

to speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during  tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up.

thanks
adding ccnet dataset
State: closed
Labels: name: dataset request

## Adding a Dataset
- **Name:**  ccnet

- **Description:** 
Common Crawl

- **Paper:** 
https://arxiv.org/abs/1911.00359

- **Data:** 
https://github.com/facebookresearch/cc_net

- **Motivation:**
this is one of the most comprehensive clean monolingual datasets across a variety of languages. Quite important for cross-lingual reseach

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

thanks
viewer "fake_news_english" error
State: closed
Labels: name: nlp-viewer

When I visit the [Huggingface - viewer](https://huggingface.co/datasets/viewer/) web site, under the dataset "fake_news_english" I've got this error:

> ImportError: To be able to use this dataset, you need to install the following dependencies['openpyxl'] using 'pip install # noqa: requires this pandas optional dependency for reading xlsx files' for instance'

as well as the error Traceback.
Adding the NorNE dataset for Norwegian POS and NER
State: closed

NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.

See #1720.
load_dataset ignoring features
State: closed
Labels: name: bug

First of all, I'm sorry if it is a repeated issue or the changes are already in master, I searched and I didn't find anything. 

I'm using datasets 1.5.0

![image](https://user-images.githubusercontent.com/37592763/113114369-8f376580-920b-11eb-900d-94365b59f04b.png)

As you can see, when I load the dataset, the ClassLabels are ignored, I have to cast the dataset in order to make it work.

Code to reproduce:

```python
import datasets
data_location = "/data/prueba_multiclase"
features = datasets.Features(
        {"texto": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["false", "true"])}
    )
dataset = datasets.load_dataset(
        "csv", data_files=data_location, delimiter="\t", features=features
    )
```

Dataset I used:


[prueba_multiclase.zip](https://github.com/huggingface/datasets/files/6235022/prueba_multiclase.zip) (it has to be unzipped)


Thank you! ❤️
Telugu subset missing for xtreme tatoeba dataset
State: closed

from nlp import load_dataset
train_dataset = load_dataset('xtreme', 'tatoeba.tel')['validation']
ValueError: BuilderConfig tatoeba.tel not found.

but language tel is actually included in xtreme:
https://github.com/google-research/xtreme/blob/master/utils_preprocess.py
def tatoeba_preprocess(args):
  lang3_dict = {
    'afr':'af', 'ara':'ar', 'bul':'bg', 'ben':'bn',
    'deu':'de', 'ell':'el', 'spa':'es', 'est':'et',
    'eus':'eu', 'pes':'fa', 'fin':'fi', 'fra':'fr',
    'heb':'he', 'hin':'hi', 'hun':'hu', 'ind':'id',
    'ita':'it', 'jpn':'ja', 'jav':'jv', 'kat':'ka',
    'kaz':'kk', 'kor':'ko', 'mal':'ml', 'mar':'mr',
    'nld':'nl', 'por':'pt', 'rus':'ru', 'swh':'sw',
    'tam':'ta', **_'tel':'te'_**, 'tha':'th', 'tgl':'tl', <----here
    'tur':'tr', 'urd':'ur', 'vie':'vi', 'cmn':'zh',
    'eng':'en',
  }
Add configurable options to `seqeval` metric
State: closed

Right now `load_metric("seqeval")` only works in the default mode of evaluation (equivalent to conll evaluation).

However, seqeval library [supports](https://github.com/chakki-works/seqeval#support-features) different evaluation schemes (IOB1, IOB2, etc.), which can be plugged in just by supporting additional kwargs in `Seqeval._compute`
https://github.com/huggingface/datasets/blob/85cf7ff920c90ca2e12bedca12b36d2a043c3da2/metrics/seqeval/seqeval.py#L109

Things that would be relevant are, for example, supporting `mode="strict", scheme=IOB2` to count only full entity match as a true positive and omit partial matches.

The only problem I see is that the spirit of `metrics` seems to not require additional imports from user. `seqeval` only supports schemes as objects, without any string aliases. 

It can be solved naively with mapping like `{"IOB2": seqeval.scheme.IOB2}`. Or just left as is and require user to explicitly import scheme from `seqeval` if he wants to configure it past the default implementation.

If that makes sense, I am happy to implement the change.
Dataset file size on disk is very large with 3D Array
State: open

Hi, 

I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. 

The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. 

`{
    "description": "",
    "citation": "",
    "homepage": "",
    "license": "",
    "features": {
        "image": {
            "shape": [224, 224, 3],
            "dtype": "uint8",
            "id": null,
            "_type": "Array3D",
        }
    },
    "post_processed": null,
    "supervised_keys": null,
    "builder_name": "shot_type_image_dataset",
    "config_name": "default",
    "version": {
        "version_str": "0.0.0",
        "description": null,
        "major": 0,
        "minor": 0,
        "patch": 0,
    },
    "splits": {
        "train": {
            "name": "train",
            "num_bytes": 520803408,
            "num_examples": 1479,
            "dataset_name": "shot_type_image_dataset",
        }
    },
    "download_checksums": {
        "": {
            "num_bytes": 16940447118,
            "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03",
        }
    },
    "download_size": 16940447118,
    "post_processing_size": null,
    "dataset_size": 520803408,
    "size_in_bytes": 17461250526,
}`

I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.

I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.

This might be a problem for large dataset. 

Thanks for your help.
TypeError when using save_to_disk in a dataset loaded with ReadInstruction split
State: closed

Hi,

Loading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`.

Here is the minimal reproducible example:

```python
from datasets import load_dataset
from datasets import ReadInstruction

data_1 = load_dataset(
    "wikiann",
    "en",
    split="validation",
)

data_1.save_to_disk("temporary_path_1")

print("Save with regular split works.")

data_2 = load_dataset(
    "wikiann",
    "en",
    split=ReadInstruction("validation", to=50, unit="%"),
)

data_2.save_to_disk("temporary_path_2")
```

and the corresponding output:

```
Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)
Save with regular split works.
Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)
Traceback (most recent call last):
  File "bug.py", line 20, in <module>
    data_2.save_to_disk("temporary_path_2")
  File "/xxxxx/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 645, in save_to_disk
    json.dump(state, state_file, indent=2, sort_keys=True)
  File "/usr/lib/python3.7/json/__init__.py", line 179, in dump
    for chunk in iterable:
  File "/usr/lib/python3.7/json/encoder.py", line 431, in _iterencode
    yield from _iterencode_dict(o, _current_indent_level)
  File "/usr/lib/python3.7/json/encoder.py", line 405, in _iterencode_dict
    yield from chunks
  File "/usr/lib/python3.7/json/encoder.py", line 438, in _iterencode
    o = _default(o)
  File "/usr/lib/python3.7/json/encoder.py", line 179, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type ReadInstruction is not JSON serializable
```

Let me know if there is some misuse from my end.

Thanks in advance.