A DistilBERT tokenizer using WordPiece subword segmentation. This tokenizer class will tokenize raw strings into integer sequences and is based on keras_nlp. tokenizers.

DistilRoBERTa is a distilled version of the RoBERTa-base model, with 6 layers, 768 dimensions, and 12 heads, totaling 82M parameters. It is trained on OpenWebTextCorpus, a reproduction of OpenAI's WebText dataset, and achieves comparable performance to RoBERTa while being twice as fast.

DistilBERT is a distilled version of BERT that was trained in a knowledge distillation process from the BERT base model. This process aims to create a student model (DistilBERT) that learns from a teacher model (BERT), where the teacher model is more extensive and already trained.

Product Overview. This is a Sentence Pair Classification model built upon a Text Embedding model from [Hugging Face](https://huggingface.co/distilbert-base-uncased ). It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'.