https://github.com/yoshitomo-matsubara/torchdistill
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/young-geng/easylm
https://github.com/young-zonglin/bilm-tf-extended
https://github.com/youngerous/qtree
https://github.com/younggns/comparative-abusive-lang
https://github.com/youngjie-cho/csci1470final
https://github.com/ypeleg/MinimalIsAllYouNeed
https://github.com/ysmiao/nvdm
https://github.com/ytchx1999/GCN_res-FLAG
Robust Optimization as Data Augmentation for Large-scale Graphs
https://github.com/yuanjing-zhu/elmo
https://github.com/yuanmu97/secure-transformer-inference
https://github.com/yuanxiaosc/ELMo
https://github.com/yuchenlin/LLM-Blender
WebGPT: Browser-assisted question-answering with human feedback
https://github.com/yuchenlin/swiftsage
Agent Lumos: Unified and Modular Training for Open-Source Language Agents
https://github.com/yuelinan/c3vg
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yueyongjiao/Transformer-Contextual
https://github.com/yueyongjiao/attention_is_all_you_need_pytorch
https://github.com/yugrocks/Question-Answering-with-BIDAF
https://github.com/yuh-zha/align
Text Alignment Is An Efficient Unified Model for Massive NLP Tasks
https://github.com/yuhangT/tf_bert
https://github.com/yukexing/3dgs-ieval-15k
LMM4LMM: Benchmarking and Evaluating Large-multimodal Image Generation with LMMs
https://github.com/yulangz/End-to-End-Memory-Networks-in-Paddle
https://github.com/yulong-XJTU/SimVLM
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
https://github.com/yumoh/speech-keras
https://github.com/yunzeman/lexicon3d
Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding
https://github.com/yuriak/PPDAI
https://github.com/yururyuru00/GAT_pyG
https://github.com/yushi-y/dpo-toxic-neurons
https://github.com/yuweijiang/HybridNet
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
https://github.com/yuwfan/FILTER
FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding
https://github.com/yuxiaw/openfactcheck
RARR: Researching and Revising What Language Models Say, Using Language Models
https://github.com/yuyuz/Variational-Reasoning-Networks
Variational Reasoning for Question Answering with Knowledge Graph
https://github.com/ywjawmw/shennong-tcm-evaluation-benchmark
https://github.com/ywu94/Code-Notes
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
https://github.com/yxzwang/normalized-information-payload
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/yydai/bert_test
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.