https://github.com/yynil/rwkvinside
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 19
- 평가표
- 153
- 보존된 평가 기록
- 1,781
Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/zankner/Transformer
https://github.com/zapplea/bert
https://github.com/zaradana/Fast_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/zchuz/timebench
https://github.com/zeaver/multifactor
QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation
https://github.com/zebangcheng/emotion-llama
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
https://github.com/zenanz/ChemPatentEmbeddings
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/zer0sh0t/artificial_intelligence/tree/master/multimodal_models/gmlp
https://github.com/zer0sh0t/artificial_intelligence/tree/master/transformer
https://github.com/zfj1998/CodeBert-Code2Text
https://github.com/zhang-huihui/git-repository
https://github.com/zhangbo2008/GAT_network
https://github.com/zhangbo2008/best_pytorch_transformer
https://github.com/zhanglab-aim/cancer-net
https://github.com/zhanglab-aim/lingo
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
https://github.com/zhao-tong/GNNs-easy-to-use
https://github.com/zhaojinglong/Attentive-Pooling-Networks
https://github.com/zhaoolee/garss
https://github.com/zhaopku/LSTM-Jump-for-text-skimming
https://github.com/zhaoyue-zephyrus/avion
https://github.com/zhaoyuesun/phee
https://github.com/zhaozhen2333/iflytek2021
https://github.com/zhayefei/off-topic-GCBiA
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
https://github.com/zhdbwe/Paper-DailyReading
https://github.com/zhengcj1/ChID-Dataset
https://github.com/zhengxujosh/awesome-rag-vision
Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook
https://github.com/zhenv5/atp
ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation
https://github.com/zhihu/SERank
SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network
https://github.com/zhiqic/chartreader
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/zhiweihu1103/qe-temp
https://github.com/zhixinlai/llmboostmedical
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
https://github.com/zhoucz97/Splinter-paddle
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.