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Question Answering
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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/lif31up/extended-BERT-for-low-rank-adaption
https://github.com/lighterswang/biomedclip-lora
https://github.com/lijierui/group-attention
https://github.com/lindermanlab/S5
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
https://github.com/lingjzhu/charsiug2p
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
https://github.com/linjieli222/hero_video_feature_extractor
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
https://github.com/linlei1214/SITS-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/linweizhedragon/knowledge-aware-graph-enhanced-gpt-2-for-dialogue-state-tracking
Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
https://github.com/liqichen6688/duo-attention
https://github.com/listenviolet/XLNet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/littell/NamedTransformer
https://github.com/littlesunlxy/fedpu-torch
FedMatch: Federated Learning Over Heterogeneous Question Answering Data
https://github.com/liu6zijian/simplified-gcn-model
https://github.com/liufly/narrative-modeling
https://github.com/liupeng0606/E
https://github.com/liuqiangict/lamb_optimizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/liuqihan/Image-Caption
What value do explicit high level concepts have in vision to language problems?
https://github.com/liuruiyang98/Jittor-MLP
https://github.com/liuyinan88/MCQA_project
Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
https://github.com/liyu10000/pneumoconiosis
https://github.com/liyuan24/nanodeepresearch
https://github.com/lkfo415579/MT-Readling-List
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/llStringll/Poly-encoders
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/llamafamily/llama-chinese
https://github.com/llamazing/numnet_plus
A Simple and Effective Model for Answering Multi-span Questions
https://github.com/llm-lab-org/CLASP
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
https://github.com/llx666/git_demo
https://github.com/lmarent/TranslateAlignRetrieve
MLQA: Evaluating Cross-lingual Extractive Question Answering
https://github.com/lmn-extracts/dcn_plus
https://github.com/lntzm/cvpr24track-longvideo
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
https://github.com/lonePatient/BERT-chinese-text-classification-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lonePatient/Bert-Multi-Label-Text-Classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lonePatient/ERNIE-text-classification-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lonePatient/albert_pytorch
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/lonePatient/bert-sentence-similarity-pytorch
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.