Issue: NonMatchingSplitsSizesError when loading blog_authorship_corpus

Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. 

```
raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', 
num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 
'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, 
dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', 
num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 
'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, 
dataset_name='blog_authorship_corpus')}]
```

Upon checking it seems like there is a disparity between the information in `datasets/blog_authorship_corpus/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.

Comments:
I just ran it on colab and got this
```
[{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812,
dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train',
num_bytes=611607465, num_examples=533285, dataset_name='blog_authorship_corpus')},
{'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277,
dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation',
num_bytes=35652716, num_examples=30804, dataset_name='blog_authorship_corpus')}]
```
which is different from the `dataset_infos.json` and also different from yours.

It looks like the script for generating examples is not consistent

The files provided by the authors are corrupted and the script seems to ignore the xml files that can't be decoded (it does `try:... except UnicodeDecodeError`). Maybe depending of the environment some files can be opened and some others don't but not sure why

Feel free to do `ignore_verifications=True` for now... The verifications only include a check on the checksums of the downloaded files, and a check on the number of examples in each splits.

I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset("imdb")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset. 

> I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset("imdb")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset.

When the checksums don't match, it may mean that the file you downloaded is corrupted. In this case you can try to load the dataset again `load_dataset("imdb", download_mode="force_redownload")`

Also I just checked on my side and it worked fine:

```python
from datasets import load_dataset

dataset = load_dataset("imdb")
print(len(dataset["train"]))
# 25000
```

Let me know if redownloading fixes your issue @EmilyAlsentzer .
If not, feel free to open a separate issue.

It doesn't seem to fix the problem. I'll open a separate issue. Thanks. 

I wasn't aware of the "force_redownload" option and manually removed the '/home/me/.cache/huggingface/datasets/' dir, this worked for me (dataset 'cnn_dailymail')

Yes I think this might not be documented well enough. Let’s add it to the doc @lhoestq @SBrandeis.
And everything on how to control the cache behavior better (removing, overriding, changing the path, etc)

Already fixed:
```python
In [1]: from datasets import load_dataset

In [2]: ds = load_dataset("blog_authorship_corpus")

In [3]: ds
Out[3]: 
DatasetDict({
    train: Dataset({
        features: ['text', 'date', 'gender', 'age', 'horoscope', 'job'],
        num_rows: 689793
    })
    validation: Dataset({
        features: ['text', 'date', 'gender', 'age', 'horoscope', 'job'],
        num_rows: 37919
    })
})


In my case, I had to remove the cache datasets directory completely as @putssander suggested, the download_mode='forced_redownload' was insufficient.

I had a private repository with data files that I loaded with a loading script. It was working fine until I pushed a new version of the data files and then the NonMatchingSplitsSizesError was raised.


I had the same issue. Adding` verification_mode="no_checks"` in laod_dataset() as param works for me 

i had the same issue, solved it by "
dataset = load_dataset(
    "cnn_dailymail",
    "3.0.0",
    download_mode="force_redownload",
    ignore_verifications=True
)
" and the dataset downloaded without a problem: DatasetDict({
    train: Dataset({
        features: ['article', 'highlights', 'id'],
        num_rows: 861339
    })
    validation: Dataset({
        features: ['article', 'highlights', 'id'],
        num_rows: 40104
    })
    test: Dataset({
        features: ['article', 'highlights', 'id'],
        num_rows: 34470
    })
})