Unique names for dataset cache for each tokenizer 
# 🚀 Feature request

Currently in the examples, dataset caches are named for the family of tokenizer used. For example, 'cached_train_BertTokenizer_128'. This may lead to unexpected behavior when running multiple models/tokenizers within the same model type, but different variations on the model/tokenizer. 

In this colab notebook, NER training is used run using scibert and then bert-base-cased. Even though the old data files are removed, the code still uses the old cache, resulting in an indexing error due to the mismatched token indices. 

https://colab.research.google.com/drive/1q4uBFm81yBWVNzG3Si2ByBh1nw8fk-Q5?usp=sharing

In this colab notebook, NER training is run on scibert-cased and then scibert-uncased. In this notebook, no explicit error occurs since there isn't an indexing error, but it seems that the wrong dataset is being used. In the output of scibert-uncased, there are many warnings of unpredicted tokens, and a lower than expected score. Both of these do not occur if scibert-cased is not run before scibert-cased

https://colab.research.google.com/drive/1pnpWfRqX4nknc0RRe9A2CArbVok3NbhC?usp=sharing


## Motivation

Prevent unexpected behavior when testing on multiple variations on the same transformer architecture. 



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