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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/cosmoquester/transformers-bart-pretrain
https://github.com/cospplay/bert-master
https://github.com/cove-adml/adml-anon
https://github.com/coxy1989/tfmr
https://github.com/cpm0722/transformer_pytorch
https://github.com/crazydigger/Callibration-of-GPT
https://github.com/crherlihy/clinical_nli_artifacts
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/crockwell/cap3d
https://github.com/crx934080895/Bert-CRF_New2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/csinva/imodelsX
https://github.com/csitfun/logiqa2.0
Transforming Question Answering Datasets Into Natural Language Inference Datasets
https://github.com/cstghitpku/GateMemN2N
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/cuber2460/bert
https://github.com/cuhksz-nlp/SAPar
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/cui0523/Code6/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/cui0523/Code6/tree/main/ernie
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/cvlab-stonybrook/gcdr-gaze
Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following
https://github.com/cvolcy/gmlp-tf
https://github.com/cybertronai/megatron-lm
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/cybertronai/pytorch-lamb
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/cyberzhg/keras-pos-embd
https://github.com/cypressd1999/FYP_2021
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/cyqiq/multicot
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
https://github.com/czero69/acomoeye-NN
https://github.com/czy1999/MultiTQ
https://github.com/czy1999/SubGTR
https://github.com/dahong67/ConvolutionalAnalysisOperatorLearning.jl
Convolutional Analysis Operator Learning: Dependence on Training Data
https://github.com/dahong67/ConvolutionalOperatorLearning.jl
Convolutional Analysis Operator Learning: Dependence on Training Data
https://github.com/damo-nlp-sg/inf-clip
VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
https://github.com/damo-nlp-sg/videollama3
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/dandelin/Dynamic-memory-networks-plus-Pytorch
Dynamic Memory Networks for Visual and Textual Question Answering
https://github.com/dangwalp/qg
https://github.com/daniel-furman/sft-demos
Training language models to follow instructions with human feedback
https://github.com/danielamassiceti/geneval_visdial
https://github.com/danieldeutsch/qaeval
Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.