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Linguistic Acceptability
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Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical.
Image Source: Warstadt et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yuhangT/tf_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yxzwang/normalized-information-payload
https://github.com/yydai/bert_test
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zaaztt/bert_chinese_example
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zapplea/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zaradana/Fast_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zer0sh0t/artificial_intelligence/tree/master/language_models/bidirectional_encoder_representations_from_transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zfj1998/CodeBert-Code2Text
https://github.com/zhang-huihui/git-repository
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zhengcj1/ChID-Dataset
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zhiqic/chartreader
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/zixinzeng-jennifer/spanbert_trans
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/znhy1024/protoco
https://github.com/zsweet/BERT_zsw
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://gitlab.com/birder/birder
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.