Roberta

RoBERTa is a family of transformer-based language models developed by Facebook AI. It stands for Robustly Optimized BERT Pretraining Approach and is a refinement of the BERT model designed to improve pretraining for natural language understanding. RoBERTa preserves the general encoder architecture of BERT and is available in base and large configurations for a range of downstream tasks.

The RoBERTa pretraining recipe introduces several changes. It trains on substantially more text from diverse sources, removing the Next Sentence Prediction objective, and employing dynamic masking. It also uses longer training times, larger minibatches, and optimized hyperparameters to better exploit the data. The tokenizer remains WordPiece with a vocabulary similar to BERT, typically around 30,000 tokens.

In evaluation, RoBERTa achieved strong results on a variety of benchmarks, including GLUE, SQuAD, and RACE, often surpassing BERT under similar conditions. The work emphasized that pretraining data scale and training strategy can provide substantial gains independent of model size. Since its release, RoBERTa has become a widely used baseline and starting point for natural language understanding systems, and its approach has influenced subsequent pretraining methods.

RoBERTa is available in open-source implementations, and pretrained weights are widely used via libraries such as Hugging Face Transformers and fairseq. Researchers and practitioners fine-tune RoBERTa models for a broad range of NLP tasks, from sentiment analysis to question answering.