T5, or Text-to-Text Transfer Transformer, is a Transformer based architecture that uses a text-to-text approach. Every task – including translation, question answering, and classification – is cast as feeding the model text as input and training it to generate some target text. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. The changes compared to BERT include:
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
---|---|---|
Language Modelling | 98 | 9.66% |
Question Answering | 65 | 6.40% |
Text Generation | 47 | 4.63% |
Sentence | 43 | 4.24% |
Retrieval | 31 | 3.05% |
Translation | 31 | 3.05% |
Machine Translation | 26 | 2.56% |
Natural Language Understanding | 22 | 2.17% |
Semantic Parsing | 19 | 1.87% |