https://github.com/xiaolilaoli/tiny_bert_ms
TinyBERT: Distilling BERT for Natural Language Understanding
Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 보존된 평가 기록
- 42
Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.
Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xiaoman-zhang/kad
Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images
https://github.com/xiaopingzhong/bert-finetune-for-classfier
https://github.com/xiaopp123/bert_explain
https://github.com/xingjian-zhang/massw
https://github.com/xingniu/sockeye
https://github.com/xitianxiaofeixue/BERT
https://github.com/xiuyu0000/new_papers_codes/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiuyu0000/tutorials/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xmu-xiaoma666/External-Attention-pytorch
https://github.com/xuzhezhaozhao/bert_reading
https://github.com/xycforgithub/MultiTask-MRC
Multi-Task Deep Neural Networks for Natural Language Understanding
https://github.com/xydaytoy/BMI-NMT
https://github.com/yalsaffar/S-COLBERT
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
https://github.com/yanaiela/TNE
https://github.com/yangyucheng000/University/tree/main/model-1/rembert
Rethinking embedding coupling in pre-trained language models
https://github.com/yangyucheng000/University/tree/main/model-3/rembert
Rethinking embedding coupling in pre-trained language models
https://github.com/yangyucheng000/ascend_albert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/bert_Ascend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/bert_GPU
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yanqi1811/attention-is-all-you-need
https://github.com/yanzhitech/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yeeeqichen/Bert
https://github.com/yifding/hetseq
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yinghao1019/Joint_learn
https://github.com/yiyc-kor/bert-study
https://github.com/ykrmm/ICLR_2020
https://github.com/ykrmm/TREMBA
https://github.com/ylqi/count-anything
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ymcui/LAMB_Optimizer_TF
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yookyungkho/mav
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
https://github.com/yoongtr/Grammar-Error-Correction-with-OpenNMT-tf
https://github.com/yoshitomo-matsubara/torchdistill
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ypeleg/MinimalIsAllYouNeed
https://github.com/yuanmu97/secure-transformer-inference
https://github.com/yuelinan/c3vg
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.