Papers with Code 아카이브
Common Sense Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 24
- 보존된 평가 기록
- 397
Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yahah100/text_summarization
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/yangletliu/finlora
https://github.com/yangyucheng000/University/tree/main/model-3/roberta
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/yanzhitech/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yifding/hetseq
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yikangshen/megablocks
ST-MoE: Designing Stable and Transferable Sparse Expert Models
https://github.com/yinzhangyue/AoR
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/yinzhangyue/eot
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/yiyc-kor/bert-study
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yiyixuxu/n-grammer-flax
https://github.com/yizhongw/tk-instruct
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ymcui/LAMB_Optimizer_TF
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ymcui/chinese-mixtral
https://github.com/yoshitomo-matsubara/torchdistill
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/young-geng/easylm
https://github.com/ypeleg/MinimalIsAllYouNeed
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yuanmu97/secure-transformer-inference
https://github.com/yuelinan/c3vg
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yuhangT/tf_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yusanshi/NewsRecommendation
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/zach-zhiling-zheng/reticular_chemist
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/zhangyezhuo/mamca
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.