https://github.com/weidafeng/NLU2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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/weitunglin/pixmamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/weiwang31/icemamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/wenhuchen/HDSA-Dialog
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/whqwill/seq2seq-keyphrase-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/why2000/DuReader-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/writerai/fitbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wuzengqing001225/sabm_pricing_game
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
https://github.com/wuzengqing001225/sabm_shallwetalk
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
https://github.com/wuzengqing001225/sabm_shallweteamup
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
https://github.com/wyf-python/Bert-Pytorch-TextClassification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wzzzd/LM_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wzzzd/Pretrain_Bert_with_MaskLM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/x-lance/neusym-rag
https://github.com/xbtlin/All-about-Machine-Learning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xesdiny/test-bert-master
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xi-j/mamba-asr
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/xiangyue9607/CliniRC
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiaoman-zhang/PMC-VQA
https://github.com/xiaopingzhong/bert-finetune-for-classfier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiaopp123/bert_explain
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiaoqian19940510/text-classification-
https://github.com/xiaoqian19940510/text-classification-surveys
https://github.com/xinyooo/ALBERT4Rec
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/xitianxiaofeixue/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiuyu0000/new_papers_codes/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiuyu0000/new_papers_codes/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/xiuyu0000/tutorials/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xlang-ai/batch-prompting
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
https://github.com/xpixelgroup/rdsr
https://github.com/xuefuzhao/openmoe
ST-MoE: Designing Stable and Transferable Sparse Expert Models
https://github.com/xuetianci/pacit
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/xusenlinzy/api-for-open-llm
https://github.com/xuzhezhaozhao/bert_reading
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xvyaward/owq
https://github.com/xvyaward/qeft
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.