https://github.com/google-research/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet.. For example, given the word “mouse” and the following sentence:
“A mouse consists of an object held in one's hand, with one or more buttons.”
we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
FlauBERT: Unsupervised Language Model Pre-training for French
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Incorporating Word Sense Disambiguation in Neural Language Models
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.