huggingface/transformers#12276 introduced a new --log_level feature, which now allows users to set their desired log level via CLI or TrainingArguments.

run_translation.py was used as a "model" for other examples.

Now we need to replicate this to all other Trainer-based examples under examples/pytorch/, the 3 changes are

1. importing datasets
2. using `training

The Split class accepts SplitDelimiterBehavior which is really useful. The Punctuation however always uses SplitDelimiterBehavior::Isolated (and Whitespace on the other hand behaves like SplitDelimiterBehavior::Removed).

impl PreTokenizer for Punctuation {
    fn pre_tokenize(&self, pretokenized: &mut PreTokenizedString) -> Result<()> {
        pretokenized.split(|_, s| s.spl

I'm playing around with this wonderful code but I'm running into a curious issue when I try to train the model with my own data.

I replicated the personachat_self_original.json file structure and added my own data. I deleted dataset_cache_OpenAIGPTTokenizer file but when I try to train, I get this error:

INFO:train.py:Pad inputs and convert to Tensor
Traceback (most recent call last)