visualization for cc100 is broken

visualization for cc100 is broken
Hi
visualization through dataset viewer for cc100 is broken
https://huggingface.co/datasets/viewer/

thanks a lot

Hi
loading works fine, but the viewer only is broken
thanks

On Wed, Apr 7, 2021 at 12:17 PM Quentin Lhoest ***@***.***>
wrote:

> This looks like an issue with the cc100 dataset itself but not sure
> Did you try loading cc100 on your machine ?
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/2162#issuecomment-814793809>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AS37NMRUO33JSOYGT6RETWLTHQWNLANCNFSM42IUOR6Q>
> .
>


any possibility to download part of large datasets only?
Hi
Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
Not yet but it’s on the short/mid-term roadmap (requested by many indeed).


any possibility to download part of large datasets only?
Hi
Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
oh, great, really awesome feature to have, thank you very much for the great, fabulous work


any possibility to download part of large datasets only?
Hi
Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
We'll work on dataset streaming soon. This should allow you to only load the examples you need ;)


any possibility to download part of large datasets only?
Hi
Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
thanks a lot Quentin, this would be really really a great feature to have

On Wed, Apr 7, 2021 at 12:14 PM Quentin Lhoest ***@***.***>
wrote:

> We'll work on dataset streaming soon. This should allow you to only load
> the examples you need ;)
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/2161#issuecomment-814791922>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AS37NMROD62QAKIJMAKWISTTHQWBVANCNFSM42IUI5JQ>
> .
>


any possibility to download part of large datasets only?
Hi
Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
Is streaming completed? On the 1.8.0 docs it is mentioned (https://huggingface.co/docs/datasets/dataset_streaming.html), but when following the example I get the following error:

```
>>> dataset2 = load_dataset("amazon_us_reviews", "Pet_Products_v1_00", split='train', streaming=True)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-21-1eedab26cff1> in <module>()
----> 1 en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)

3 frames
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
    339                 if value is not None:
    340                     if not hasattr(builder_config, key):
--> 341                         raise ValueError(f"BuilderConfig {builder_config} doesn't have a '{key}' key.")
    342                     setattr(builder_config, key, value)
    343

ValueError: BuilderConfig OscarConfig(name='unshuffled_deduplicated_en', version=1.0.0, data_dir=None, data_files=None, description='Unshuffled and deduplicated, English OSCAR dataset') doesn't have a 'streaming' key.
```

UPDATE: Managed to get streaming working by building from source and installing the additional `datasets[streaming]` package:

```
!pip install git+https://github.com/huggingface/datasets.git
!pip install datasets[streaming]
```


any possibility to download part of large datasets only?
Hi
Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
Hi ! Streaming is available on `master` only right now. We'll make a new release 1.9.0 on Monday :)


data_args.preprocessing_num_workers almost freezes
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
Hi.
I cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks

```
#3:  11%|███████████████▊                                                                                | 172/1583 [00:46<06:21,  3.70ba/s]
#4:   9%|█████████████▏                                                                                | 143/1583 [00:46<07:46,  3.09ba/s]
#7:   6%|█████████                                                                                | 98/1583 [00:45<11:34,  2.14ba/s]
#5:   8%|███████████▍                                                                                | 124/1583 [00:46<09:03,  2.68ba/s]
#6:   7%|██████████▏
```


data_args.preprocessing_num_workers almost freezes
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
closing since I cannot reproduce it again, thanks


adding ccnet dataset

adding ccnet dataset
## 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
closing since I think this is cc100, just the name has been changed. thanks


viewer "fake_news_english" error

viewer "fake_news_english" error
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.

Thanks for reporting !
The viewer doesn't have all the dependencies of the datasets. We may add openpyxl to be able to show this dataset properly


load_dataset ignoring features

load_dataset ignoring features
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! ❤

Hi ! Thanks for reporting. I opened a PR to fix this issue: #2201


load_dataset ignoring features

load_dataset ignoring features
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! ❤

Nice question which helped me a lot! I have wasted a lot of time to the `DatasetDict` creation from a csv file. Hope the document of this module add some simple examples.


load_dataset ignoring features

load_dataset ignoring features
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! ❤

Hi :) We're indeed working on tutorials that we will add to the docs !


Telugu subset missing for xtreme tatoeba dataset
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',
  }
Good catch ! Thanks for reporting

I just opened #2180 to fix this


Add configurable options to `seqeval` metric
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.
Hi @marrodion.

Thanks for pointing this out. It would be great to incorporate this metric-specific enhancement.

Another possibility would be to require the user to input the scheme as a string `mode="strict", scheme="IOB2"` and then dynamically import the corresponding module using Python `importlib`:
```python
if scheme:
    scheme = importlib.import_module(f"seqeval.scheme.{scheme}")
```

Feel free to create a Pull Request to make this contribution.


Dataset file size on disk is very large with 3D Array
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.

Hi ! In the arrow file we store all the integers as uint8.
So your arrow file should weigh around `height x width x n_channels x n_images` bytes.

What feature type do your TFDS dataset have ?

If it uses a `tfds.features.Image` type, then what is stored is the encoded data (as png or jpg for example). Since these encodings are made for compression, the resulting tfrecord is smaller that the arrow file.

We are working on adding a similar feature in `datasets`: the ability to store the encoded data instead of the raw integers for images, but also for audio data. This way, arrow files will have similar sizes as tfrecords for images.


Dataset file size on disk is very large with 3D Array
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.

Thanks for the prompt response. You're right about the encoding, I have the `tfds.features.Image` feature type you mentioned.
However, as described in the `dataset_info.json`, my dataset is made of 1479 (224x224x3) images. 1479 x 224 x 224 x 3 = 222630912 bytes which is far from the actual size 520803408 bytes.

Anyway I look forward to the Image feature type in `datasets`.


Dataset file size on disk is very large with 3D Array
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.

@lhoestq I changed the data structure so I have a  2D Array feature type instead of a 3D Array by grouping the two last dimensions ( a 224x672 2D Array instead of a 224x224x3 3D Array). The file size is now  223973964 bytes, nearly half the previous size! Which is around of what I would expect.
I found similar behavior in existing `datasets` collection, when comparing black and white vs color image, for example MNIST vs CIFAR.


Dataset file size on disk is very large with 3D Array
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.

Interesting !
This may be because of the offsets that are stored with the array data.

Currently the offsets are stored even if the `shape` of the arrays is fixed. This was needed because of some issues with pyarrow a few months ago. I think these issues have been addressed now, so we can probably try to remove them to make the file lighter.

Ideally in your case the floats data should be 220 MB for both Array2D and Array3D


Dataset file size on disk is very large with 3D Array
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.

Yeah for sure, can you be a bit more specific about where the offset is stored in the code base ? And any reference to pyarrow issues if you have some. I would be very interested in contributing to `datasets` by trying to fix this issue.


Dataset file size on disk is very large with 3D Array
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.

Pyarrow has two types of lists: variable length lists and fixed size lists.
Currently we store the ArrayXD data as variable length lists. They take more disk space because they must store both actual data and offsets.
In the `datasets` code this is done here:

https://github.com/huggingface/nlp/blob/dbac87c8a083f806467f5afc4ec9b401a7e4c15c/src/datasets/features.py#L346-L352

To use a fixed length list, one should use the `list_size` argument of `pyarrow.list_()`.
I believe this would work directly modulo some changes in the numpy conversion here:

https://github.com/huggingface/nlp/blob/dbac87c8a083f806467f5afc4ec9b401a7e4c15c/src/datasets/features.py#L381-L395


Loading wikipedia 20200501.en throws pyarrow related error
**Problem description**
I am getting the following error when trying to load wikipedia/20200501.en dataset.

**Error log**
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s]
Downloading:  59%|███████████████████████████████████████████████████████████████████████████████████████▊                                                              | 10.7G/18.3G [11:30<08:08, 15.5MB/s]
Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.
Traceback (most recent call last):
  File "load_wiki.py", line 2, in <module>
    ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')
  File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset
    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset
    map_tuple=True,
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp>
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested
    return function(data_struct)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read
    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files
    pa_table = self._read_files(files, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files
    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table
    pa_table = f.read_all()
  File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all
  File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
OSError: Expected to be able to read 9176784 bytes for message body, got 4918712

**Detailed version info**
datasets==1.5.0
  - dataclasses [required: Any, installed: 0.8]
  - dill [required: Any, installed: 0.3.3]
  - fsspec [required: Any, installed: 0.8.7]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
  - huggingface-hub [required: <0.1.0, installed: 0.0.7]
    - filelock [required: Any, installed: 3.0.12]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
    - requests [required: Any, installed: 2.24.0]
      - certifi [required: >=2017.4.17, installed: 2020.6.20]
      - chardet [required: >=3.0.2,<4, installed: 3.0.4]
      - idna [required: >=2.5,<3, installed: 2.6]
      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
    - tqdm [required: Any, installed: 4.49.0]
  - importlib-metadata [required: Any, installed: 1.7.0]
    - zipp [required: >=0.5, installed: 3.1.0]
  - multiprocess [required: Any, installed: 0.70.11.1]
    - dill [required: >=0.3.3, installed: 0.3.3]
  - numpy [required: >=1.17, installed: 1.17.0]
  - pandas [required: Any, installed: 1.1.5]
    - numpy [required: >=1.15.4, installed: 1.17.0]
    - python-dateutil [required: >=2.7.3, installed: 2.8.0]
      - six [required: >=1.5, installed: 1.15.0]
    - pytz [required: >=2017.2, installed: 2020.1]
  - pyarrow [required: >=0.17.1, installed: 3.0.0]
    - numpy [required: >=1.16.6, installed: 1.17.0]
  - requests [required: >=2.19.0, installed: 2.24.0]
    - certifi [required: >=2017.4.17, installed: 2020.6.20]
    - chardet [required: >=3.0.2,<4, installed: 3.0.4]
    - idna [required: >=2.5,<3, installed: 2.6]
    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]
  - xxhash [required: Any, installed: 2.0.0]

That's how I loaded the dataset
```python
from datasets import load_dataset
ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')
```


Loading wikipedia 20200501.en throws pyarrow related error
**Problem description**
I am getting the following error when trying to load wikipedia/20200501.en dataset.

**Error log**
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s]
Downloading:  59%|███████████████████████████████████████████████████████████████████████████████████████▊                                                              | 10.7G/18.3G [11:30<08:08, 15.5MB/s]
Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.
Traceback (most recent call last):
  File "load_wiki.py", line 2, in <module>
    ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')
  File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset
    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset
    map_tuple=True,
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp>
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested
    return function(data_struct)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read
    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files
    pa_table = self._read_files(files, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files
    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table
    pa_table = f.read_all()
  File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all
  File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
OSError: Expected to be able to read 9176784 bytes for message body, got 4918712

**Detailed version info**
datasets==1.5.0
  - dataclasses [required: Any, installed: 0.8]
  - dill [required: Any, installed: 0.3.3]
  - fsspec [required: Any, installed: 0.8.7]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
  - huggingface-hub [required: <0.1.0, installed: 0.0.7]
    - filelock [required: Any, installed: 3.0.12]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
    - requests [required: Any, installed: 2.24.0]
      - certifi [required: >=2017.4.17, installed: 2020.6.20]
      - chardet [required: >=3.0.2,<4, installed: 3.0.4]
      - idna [required: >=2.5,<3, installed: 2.6]
      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
    - tqdm [required: Any, installed: 4.49.0]
  - importlib-metadata [required: Any, installed: 1.7.0]
    - zipp [required: >=0.5, installed: 3.1.0]
  - multiprocess [required: Any, installed: 0.70.11.1]
    - dill [required: >=0.3.3, installed: 0.3.3]
  - numpy [required: >=1.17, installed: 1.17.0]
  - pandas [required: Any, installed: 1.1.5]
    - numpy [required: >=1.15.4, installed: 1.17.0]
    - python-dateutil [required: >=2.7.3, installed: 2.8.0]
      - six [required: >=1.5, installed: 1.15.0]
    - pytz [required: >=2017.2, installed: 2020.1]
  - pyarrow [required: >=0.17.1, installed: 3.0.0]
    - numpy [required: >=1.16.6, installed: 1.17.0]
  - requests [required: >=2.19.0, installed: 2.24.0]
    - certifi [required: >=2017.4.17, installed: 2020.6.20]
    - chardet [required: >=3.0.2,<4, installed: 3.0.4]
    - idna [required: >=2.5,<3, installed: 2.6]
    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]
  - xxhash [required: Any, installed: 2.0.0]

Hi ! It looks like the arrow file in the folder
`/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted.

Can you take a look and check that it's 18.3GB ?

If not, then maybe you need to redownload it:
```python
from datasets import load_dataset
ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache', download_mode="force_redownload")
```


Loading wikipedia 20200501.en throws pyarrow related error
**Problem description**
I am getting the following error when trying to load wikipedia/20200501.en dataset.

**Error log**
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s]
Downloading:  59%|███████████████████████████████████████████████████████████████████████████████████████▊                                                              | 10.7G/18.3G [11:30<08:08, 15.5MB/s]
Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.
Traceback (most recent call last):
  File "load_wiki.py", line 2, in <module>
    ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')
  File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset
    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset
    map_tuple=True,
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp>
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested
    return function(data_struct)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read
    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files
    pa_table = self._read_files(files, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files
    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table
    pa_table = f.read_all()
  File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all
  File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
OSError: Expected to be able to read 9176784 bytes for message body, got 4918712

**Detailed version info**
datasets==1.5.0
  - dataclasses [required: Any, installed: 0.8]
  - dill [required: Any, installed: 0.3.3]
  - fsspec [required: Any, installed: 0.8.7]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
  - huggingface-hub [required: <0.1.0, installed: 0.0.7]
    - filelock [required: Any, installed: 3.0.12]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
    - requests [required: Any, installed: 2.24.0]
      - certifi [required: >=2017.4.17, installed: 2020.6.20]
      - chardet [required: >=3.0.2,<4, installed: 3.0.4]
      - idna [required: >=2.5,<3, installed: 2.6]
      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
    - tqdm [required: Any, installed: 4.49.0]
  - importlib-metadata [required: Any, installed: 1.7.0]
    - zipp [required: >=0.5, installed: 3.1.0]
  - multiprocess [required: Any, installed: 0.70.11.1]
    - dill [required: >=0.3.3, installed: 0.3.3]
  - numpy [required: >=1.17, installed: 1.17.0]
  - pandas [required: Any, installed: 1.1.5]
    - numpy [required: >=1.15.4, installed: 1.17.0]
    - python-dateutil [required: >=2.7.3, installed: 2.8.0]
      - six [required: >=1.5, installed: 1.15.0]
    - pytz [required: >=2017.2, installed: 2020.1]
  - pyarrow [required: >=0.17.1, installed: 3.0.0]
    - numpy [required: >=1.16.6, installed: 1.17.0]
  - requests [required: >=2.19.0, installed: 2.24.0]
    - certifi [required: >=2017.4.17, installed: 2020.6.20]
    - chardet [required: >=3.0.2,<4, installed: 3.0.4]
    - idna [required: >=2.5,<3, installed: 2.6]
    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]
  - xxhash [required: Any, installed: 2.0.0]

> Hi ! It looks like the arrow file in the folder
> `/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted.
>
> Can you take a look and check that it's 18.3GB ?
>
> If not, then maybe you need to redownload it:
>
> ```python
> from datasets import load_dataset
> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache', download_mode="force_redownload")
> ```

Hi Ihoestq, thanks for the reply! Actually i think my issue is i couldn't download the dataset beyond 10.7G. It feels like the whole dataset is split into different volumes and after the first one was downloaded it crashed before proceeding to the next one. I did try 'force_redownload' mode but still got the same issue.


Loading wikipedia 20200501.en throws pyarrow related error
**Problem description**
I am getting the following error when trying to load wikipedia/20200501.en dataset.

**Error log**
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s]
Downloading:  59%|███████████████████████████████████████████████████████████████████████████████████████▊                                                              | 10.7G/18.3G [11:30<08:08, 15.5MB/s]
Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.
Traceback (most recent call last):
  File "load_wiki.py", line 2, in <module>
    ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')
  File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset
    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset
    map_tuple=True,
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp>
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested
    return function(data_struct)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read
    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files
    pa_table = self._read_files(files, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files
    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table
    pa_table = f.read_all()
  File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all
  File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
OSError: Expected to be able to read 9176784 bytes for message body, got 4918712

**Detailed version info**
datasets==1.5.0
  - dataclasses [required: Any, installed: 0.8]
  - dill [required: Any, installed: 0.3.3]
  - fsspec [required: Any, installed: 0.8.7]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
  - huggingface-hub [required: <0.1.0, installed: 0.0.7]
    - filelock [required: Any, installed: 3.0.12]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
    - requests [required: Any, installed: 2.24.0]
      - certifi [required: >=2017.4.17, installed: 2020.6.20]
      - chardet [required: >=3.0.2,<4, installed: 3.0.4]
      - idna [required: >=2.5,<3, installed: 2.6]
      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
    - tqdm [required: Any, installed: 4.49.0]
  - importlib-metadata [required: Any, installed: 1.7.0]
    - zipp [required: >=0.5, installed: 3.1.0]
  - multiprocess [required: Any, installed: 0.70.11.1]
    - dill [required: >=0.3.3, installed: 0.3.3]
  - numpy [required: >=1.17, installed: 1.17.0]
  - pandas [required: Any, installed: 1.1.5]
    - numpy [required: >=1.15.4, installed: 1.17.0]
    - python-dateutil [required: >=2.7.3, installed: 2.8.0]
      - six [required: >=1.5, installed: 1.15.0]
    - pytz [required: >=2017.2, installed: 2020.1]
  - pyarrow [required: >=0.17.1, installed: 3.0.0]
    - numpy [required: >=1.16.6, installed: 1.17.0]
  - requests [required: >=2.19.0, installed: 2.24.0]
    - certifi [required: >=2017.4.17, installed: 2020.6.20]
    - chardet [required: >=3.0.2,<4, installed: 3.0.4]
    - idna [required: >=2.5,<3, installed: 2.6]
    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]
  - xxhash [required: Any, installed: 2.0.0]

I just tried on my side and got no issues.
When downloading the dataset again, did it crash at 10.7GB as well ?


Loading wikipedia 20200501.en throws pyarrow related error
**Problem description**
I am getting the following error when trying to load wikipedia/20200501.en dataset.

**Error log**
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s]
Downloading:  59%|███████████████████████████████████████████████████████████████████████████████████████▊                                                              | 10.7G/18.3G [11:30<08:08, 15.5MB/s]
Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.
Traceback (most recent call last):
  File "load_wiki.py", line 2, in <module>
    ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')
  File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset
    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset
    map_tuple=True,
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp>
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested
    return function(data_struct)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read
    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files
    pa_table = self._read_files(files, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files
    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table
    pa_table = f.read_all()
  File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all
  File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
OSError: Expected to be able to read 9176784 bytes for message body, got 4918712

**Detailed version info**
datasets==1.5.0
  - dataclasses [required: Any, installed: 0.8]
  - dill [required: Any, installed: 0.3.3]
  - fsspec [required: Any, installed: 0.8.7]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
  - huggingface-hub [required: <0.1.0, installed: 0.0.7]
    - filelock [required: Any, installed: 3.0.12]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
    - requests [required: Any, installed: 2.24.0]
      - certifi [required: >=2017.4.17, installed: 2020.6.20]
      - chardet [required: >=3.0.2,<4, installed: 3.0.4]
      - idna [required: >=2.5,<3, installed: 2.6]
      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
    - tqdm [required: Any, installed: 4.49.0]
  - importlib-metadata [required: Any, installed: 1.7.0]
    - zipp [required: >=0.5, installed: 3.1.0]
  - multiprocess [required: Any, installed: 0.70.11.1]
    - dill [required: >=0.3.3, installed: 0.3.3]
  - numpy [required: >=1.17, installed: 1.17.0]
  - pandas [required: Any, installed: 1.1.5]
    - numpy [required: >=1.15.4, installed: 1.17.0]
    - python-dateutil [required: >=2.7.3, installed: 2.8.0]
      - six [required: >=1.5, installed: 1.15.0]
    - pytz [required: >=2017.2, installed: 2020.1]
  - pyarrow [required: >=0.17.1, installed: 3.0.0]
    - numpy [required: >=1.16.6, installed: 1.17.0]
  - requests [required: >=2.19.0, installed: 2.24.0]
    - certifi [required: >=2017.4.17, installed: 2020.6.20]
    - chardet [required: >=3.0.2,<4, installed: 3.0.4]
    - idna [required: >=2.5,<3, installed: 2.6]
    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]
  - xxhash [required: Any, installed: 2.0.0]

> I just tried on my side and got no issues.
> When downloading the dataset again, did it crash at 10.7GB as well ?

Yes i have tried it multiple times on different machines. I am wondering if you could share the screenshot of your dependency versions and i will try to make them the same as yours?


Loading wikipedia 20200501.en throws pyarrow related error
**Problem description**
I am getting the following error when trying to load wikipedia/20200501.en dataset.

**Error log**
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s]
Downloading:  59%|███████████████████████████████████████████████████████████████████████████████████████▊                                                              | 10.7G/18.3G [11:30<08:08, 15.5MB/s]
Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.
Traceback (most recent call last):
  File "load_wiki.py", line 2, in <module>
    ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')
  File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset
    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset
    map_tuple=True,
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp>
    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
  File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested
    return function(data_struct)
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset
    in_memory=in_memory,
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read
    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files
    pa_table = self._read_files(files, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files
    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
  File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table
    pa_table = f.read_all()
  File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all
  File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
OSError: Expected to be able to read 9176784 bytes for message body, got 4918712

**Detailed version info**
datasets==1.5.0
  - dataclasses [required: Any, installed: 0.8]
  - dill [required: Any, installed: 0.3.3]
  - fsspec [required: Any, installed: 0.8.7]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
  - huggingface-hub [required: <0.1.0, installed: 0.0.7]
    - filelock [required: Any, installed: 3.0.12]
    - importlib-metadata [required: Any, installed: 1.7.0]
      - zipp [required: >=0.5, installed: 3.1.0]
    - requests [required: Any, installed: 2.24.0]
      - certifi [required: >=2017.4.17, installed: 2020.6.20]
      - chardet [required: >=3.0.2,<4, installed: 3.0.4]
      - idna [required: >=2.5,<3, installed: 2.6]
      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
    - tqdm [required: Any, installed: 4.49.0]
  - importlib-metadata [required: Any, installed: 1.7.0]
    - zipp [required: >=0.5, installed: 3.1.0]
  - multiprocess [required: Any, installed: 0.70.11.1]
    - dill [required: >=0.3.3, installed: 0.3.3]
  - numpy [required: >=1.17, installed: 1.17.0]
  - pandas [required: Any, installed: 1.1.5]
    - numpy [required: >=1.15.4, installed: 1.17.0]
    - python-dateutil [required: >=2.7.3, installed: 2.8.0]
      - six [required: >=1.5, installed: 1.15.0]
    - pytz [required: >=2017.2, installed: 2020.1]
  - pyarrow [required: >=0.17.1, installed: 3.0.0]
    - numpy [required: >=1.16.6, installed: 1.17.0]
  - requests [required: >=2.19.0, installed: 2.24.0]
    - certifi [required: >=2017.4.17, installed: 2020.6.20]
    - chardet [required: >=3.0.2,<4, installed: 3.0.4]
    - idna [required: >=2.5,<3, installed: 2.6]
    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]
  - xxhash [required: Any, installed: 2.0.0]

I tried using `datasets` from `master` on macos with python 3.7.2
I also have `requests==2.23.0` and `tqdm==4.45.0`.


TypeError when using save_to_disk in a dataset loaded with ReadInstruction split
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.

Hi !
I think this has been fixed recently on `master`.
Can you try again by installing `datasets` from `master` ?
```
pip install git+https://github.com/huggingface/datasets.git
```


TypeError when using save_to_disk in a dataset loaded with ReadInstruction split
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.

Hi!

Using that version of the code solves the issue. Thanks!


en language data from MLQA dataset is missing
Hi
I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq  thank you for your help to fix this issue.
Hi ! Indeed only the languages of the `translate-train` data are included...
I can't find a link to download the english train set on https://github.com/facebookresearch/MLQA though, do you know where we can download it ?


en language data from MLQA dataset is missing
Hi
I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq  thank you for your help to fix this issue.
Hi @lhoestq
thank you very much for coming back to me, now I see, you are right, in the link you sent I see split of  {split}-context-{context_language}-question-{question_language}.json with context_language=question_language=en, TFDS most probably has extracted english ones from these files as en language files, but translate-train/test do not have en indeed. thanks a lot for the great explanations


en language data from MLQA dataset is missing
Hi
I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq  thank you for your help to fix this issue.
I close the ticket, since I do not see any en existing, they have trained on "SQuAD V1.1" instead. Thanks.


Saving large in-memory datasets with save_to_disk crashes because of pickling
Using Datasets 1.5.0 on Python 3.7.
Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.

 So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method.

When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).
```
Traceback (most recent call last):
  File "./tokenize_and_chunkify_in_memory.py", line 80, in <module>
    main()
  File "./tokenize_and_chunkify_in_memory.py", line 75, in main
    tokenize_and_chunkify(config)
  File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify
    contexts_dataset.save_to_disk(chunked_path)
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk
    self = pickle.loads(pickle.dumps(self))
OverflowError: cannot serialize a bytes object larger than 4 GiB
```
From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.

To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.

Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.
```
Traceback (most recent call last):
  File "./tokenize_and_chunkify_in_memory.py", line 94, in <module>
    main()
  File "./tokenize_and_chunkify_in_memory.py", line 89, in main
    tokenize_and_chunkify(config)
  File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify
    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads)
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map
    transformed_shards = [r.get() for r in results]
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp>
    transformed_shards = [r.get() for r in results]
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get
    raise self._value
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks
    put(task)
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps
    cls(buf, protocol, *args, **kwds).dump(obj)
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump
    StockPickler.dump(self, obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
    StockPickler.save_dict(pickler, obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
    self._batch_setitems(obj.items())
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
    save(v)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce
    save(state)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
    StockPickler.save_dict(pickler, obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
    self._batch_setitems(obj.items())
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
    save(v)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
    save(args)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
    save(x)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
    save(args)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
    save(tmp[0])
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
    save(args)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
    save(tmp[0])
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
    save(tmp[0])
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
    save(x)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
    save(args)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes
    self._write_large_bytes(BINBYTES + pack("<I", n), obj)
struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):
  File "./tokenize_and_chunkify_in_memory.py", line 94, in <module>
    main()
  File "./tokenize_and_chunkify_in_memory.py", line 89, in main
    tokenize_and_chunkify(config)
  File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify
    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads)
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map
    transformed_shards = [r.get() for r in results]
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp>
    transformed_shards = [r.get() for r in results]
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get
    raise self._value
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks
    put(task)
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps
    cls(buf, protocol, *args, **kwds).dump(obj)
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump
    StockPickler.dump(self, obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
    StockPickler.save_dict(pickler, obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
    self._batch_setitems(obj.items())
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
    save(v)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce
    save(state)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
    StockPickler.save_dict(pickler, obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
    self._batch_setitems(obj.items())
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
    save(v)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
    save(args)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
    save(x)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
    save(args)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
    save(tmp[0])
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
    save(args)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
    save(tmp[0])
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
    save(tmp[0])
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
    self._batch_appends(obj)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
    save(x)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
    self.save_reduce(obj=obj, *rv)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
    save(args)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
    save(element)
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
    f(self, obj) # Call unbound method with explicit self
  File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes
    self._write_large_bytes(BINBYTES + pack("<I", n), obj)
struct.error: 'I' format requires 0 <= number <= 4294967295
```
Hi !
Indeed `save_to_disk` doesn't call pickle anymore. Though the `OverflowError` can still appear for in-memory datasets bigger than 4GB. This happens when doing this for example:
```python
import pyarrow as pa
import pickle

arr = pa.array([0] * ((4 * 8 << 30) // 64))
table = pa.Table.from_arrays([a], names=["foo"])
pickle.dumps(table)  # fails with an OverflowError
pickle.dumps(table, 4)  # works !
```
We'll do the change to use `protocol=4`.

Moreover I've also seen other users complain about this error
```
struct.error: 'I' format requires 0 <= number <= 4294967295
```

It looks like something related to the 4GB limit as well but I'm not able to reproduce on my side.
Do you think you can provide a script that reproduces the issue ?
How big is your dataset ? (number of bytes, number of rows)