datasets 1.6 ignores cache
Great! Thanks, @stas00.

I am implementing your suggestion to turn off default value when set to `0`.

For the other suggestion (allowing different metric prefixes), I will discuss with @lhoestq to agree on its implementation.
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612 

Quoting @VictorSanh:

> 
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
> 
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-97cf4c813e6469c6.arrow'}]}`
> 
> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:
> > `{'train': [], 'validation': []}`
> 

I also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.

to reproduce:
```
USE_TF=0 python  examples/pytorch/language-modeling/run_clm.py \
    --model_name_or_path gpt2 \
    --dataset_name "stas/openwebtext-10k" \
    --output_dir output_dir \
    --overwrite_output_dir \
    --do_train \
    --do_eval \
    --max_train_samples 1000 \
    --max_eval_samples 200 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 4 \
    --num_train_epochs 1 \
    --warmup_steps 8 \
    --block_size 64 \
    --fp16 \
    --report_to none
```

the first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.

@lhoestq 

datasets 1.6 ignores cache 
 Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612 

Quoting @VictorSanh:

> 
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
> 
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-97cf4c813e6469c6.arrow'}]}`
> 
> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:
> > `{'train': [], 'validation': []}`
> 

I also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.

to reproduce:
```
USE_TF=0 python  examples/pytorch/language-modeling/run_clm.py \
    --model_name_or_path gpt2 \
    --dataset_name "stas/openwebtext-10k" \
    --output_dir output_dir \
    --overwrite_output_dir \
    --do_train \
    --do_eval \
    --max_train_samples 1000 \
    --max_eval_samples 200 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 4 \
    --num_train_epochs 1 \
    --warmup_steps 8 \
    --block_size 64 \
    --fp16 \
    --report_to none
```

the first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.

@lhoestq 
 
 Great! Thanks, @stas00.

I am implementing your suggestion to turn off default value when set to `0`.

For the other suggestion (allowing different metric prefixes), I will discuss with @lhoestq to agree on its implementation.