text: 
  
  TITLE
  Unique names for dataset cache for each tokenizer 
  
  COMMENTS
  1
  
  REACTIONS
  +1: 0
  -1: 0
  laugh: 0
  hooray: 0
  heart: 0
  rocket: 0
  eyes: 0
  
  BODY
  # 🚀 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. 
  
  
  