roberta-base

roberta-base is a robustly optimized variant of BERT, pretrained on a significantly larger corpus of English text using dynamic masked language modeling. Developed by Facebook AI, RoBERTa improves on BERT by removing the Next Sentence Prediction objective, using longer training, larger batches, and more data, including BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories. It captures contextual representations of language by masking 15% of input tokens and predicting them. RoBERTa is designed to be fine-tuned for a wide range of NLP tasks such as classification, QA, and sequence labeling, achieving strong performance on the GLUE benchmark and other downstream applications.

Features

Pretrained on 160GB of English text from diverse sources
Uses dynamic token masking during training
No Next Sentence Prediction objective
125M parameters with 12 transformer layers
Supports sequence and token-level tasks (e.g., classification, QA)
Byte-Pair Encoding (BPE) tokenizer with 50K vocabulary
Available in PyTorch, TensorFlow, and JAX
Fine-tuned versions available for various NLP benchmarks

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Registered

2025-07-01

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