Question (potential issue?) related to datasets caching
Hi ! Currently disabling the caching means that all the dataset transform like `map`, `filter` etc. ignore the cache: it doesn't write nor read processed cache files.
However `load_dataset` reuses datasets that have already been prepared: it does reload prepared dataset files.

Indeed from the documentation:
> datasets.set_caching_enabled(boolean: bool)

> When applying transforms on a dataset, the data are stored in cache files. The caching mechanism allows to reload an existing cache file if it’s already been computed.
> Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated after each transform.
> If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets. More precisely, if the caching is disabled:
> - cache files are always recreated
> - cache files are written to a temporary directory that is deleted when session closes
> - cache files are named using a random hash instead of the dataset fingerprint - use datasets.Dataset.save_to_disk() to save a transformed dataset or it will be deleted when session closes
> - caching doesn’t affect datasets.load_dataset(). If you want to regenerate a dataset from scratch you should use the download_mode parameter in datasets.load_dataset().
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!
Question (potential issue?) related to datasets caching 
 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! 
 Hi ! Currently disabling the caching means that all the dataset transform like `map`, `filter` etc. ignore the cache: it doesn't write nor read processed cache files.
However `load_dataset` reuses datasets that have already been prepared: it does reload prepared dataset files.

Indeed from the documentation:
> datasets.set_caching_enabled(boolean: bool)

> When applying transforms on a dataset, the data are stored in cache files. The caching mechanism allows to reload an existing cache file if it’s already been computed.
> Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated after each transform.
> If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets. More precisely, if the caching is disabled:
> - cache files are always recreated
> - cache files are written to a temporary directory that is deleted when session closes
> - cache files are named using a random hash instead of the dataset fingerprint - use datasets.Dataset.save_to_disk() to save a transformed dataset or it will be deleted when session closes
> - caching doesn’t affect datasets.load_dataset(). If you want to regenerate a dataset from scratch you should use the download_mode parameter in datasets.load_dataset().