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RoBERTa has the same architecture as BERT but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a different pretraining scheme. RoBERTa doesn’t have token_type_ids, so you don’t need to indicate which token belongs to which segment.
- BART
BART is particularly effective when fine tuned for text...
- HerBERT
HerBERT Overview. The HerBERT model was proposed in KLEJ:...
- RetriBERT
Parameters . vocab_size (int, optional, defaults to 30522) —...
- FSMT
FSMT Overview. FSMT (FairSeq MachineTranslation) models were...
- Realm
Parameters . vocab_size (int, optional, defaults to 30522) —...
- FacebookAI/roberta-base
RoBERTa base model Pretrained model on English language...
- BART
Jul 26, 2019 · View a PDF of the paper titled RoBERTa: A Robustly Optimized BERT Pretraining Approach, by Yinhan Liu and 9 other authors. Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke...
- arXiv:1907.11692 [cs.CL]
- 2019
- Computation and Language (cs.CL)
Jan 10, 2023 · RoBERTa (short for “Robustly Optimized BERT Approach”) is a variant of the BERT (Bidirectional Encoder Representations from Transformers) model, which was developed by researchers at Facebook AI.
Jul 29, 2019 · Facebook AI’s RoBERTa is a new training recipe that improves on BERT, Google’s self-supervised method for pretraining natural language processing systems. By training longer, on more data, and dropping BERT’s next-sentence prediction RoBERTa topped the GLUE leaderboard.
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RoBERTa base model Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository .
What is RoBERTa: A robustly optimized method for pretraining natural language processing (NLP) systems that improves on Bidirectional Encoder Representations from Transformers, or BERT, the self-supervised method released by Google in 2018.