Roberta-based

BERT was trained on two objectives simultaneously: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). In NSP, the model received two sentences and had to predict if the second sentence logically followed the first.

model_name = "cardiffnlp/twitter-roberta-base-sentiment-latest" tokenizer = RobertaTokenizer.from_pretrained(model_name) model = RobertaForSequenceClassification.from_pretrained(model_name) roberta-based

: Changes which words are hidden in every single training epoch to force better contextual learning. BERT was trained on two objectives simultaneously: Masked

Localized versions that outperform multilingual models in their specific languages. Implementing RoBERTa: The Developer’s Choice roberta-based

Because the RoBERTa recipe is so effective, the community has created specialized "based" models for almost every industry: Optimized for medical and biological text.