So let’s say if I do
GPT2TokenizerFast.from_pretrained('gpt2-medium')
vs GPT2TokenizerFast.from_pretrained('distilgpt2')
Is there actually any differences in their tokenized output?
So let’s say if I do
GPT2TokenizerFast.from_pretrained('gpt2-medium')
vs GPT2TokenizerFast.from_pretrained('distilgpt2')
Is there actually any differences in their tokenized output?
In that particular case, I don’t think so, but there are definitely cases where tokenizers from the same model type but different pretrained configurations are different. bert-base-uncased
vs bert-base-cased
would be one clear example.
Thank you for your clarification