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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/surancy/nlp-classification-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/surekhamedapati/NLPA_NEO
https://github.com/suryaiyer/squad-bidaf
https://github.com/suryanshkumar/online-joint-depthfusion-and-semantic
https://github.com/suzhiba/SimpleRecurrentUnits-SRU-
https://github.com/svakulenk0/response_eval
https://github.com/swarnahub/explanationhardness
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/swimmingCreative/DeepSpeechRecognition-master
https://github.com/sy-sunmoon/Clever-Commenter-Let-s-Try-More-Apps
https://github.com/sydney-machine-learning/sentimentanalysis-uselections
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/synthlabsai/big-math
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
https://github.com/taishan1994/pytorch_gat
https://github.com/tangg555/sabart
https://github.com/tanjeffreyz/attention-is-all-you-need
https://github.com/tanny411/llm-reliability-and-consistency-evaluation
A Study on Large Language Models' Limitations in Multiple-Choice Question Answering
https://github.com/tanveer81/rgnet
https://github.com/tanyuqian/aspect-based-summarization
https://github.com/tanyuqian/cappy
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
https://github.com/tashchowdhury/FloodNet-Supervised_v1.0
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/tatp22/linformer-pytorch
https://github.com/tatp22/multidim-positional-encoding
https://github.com/tatp22/pytorch-fast-GAT
https://github.com/tatsu-lab/alpaca_farm
Training language models to follow instructions with human feedback
https://github.com/tatsu-lab/stanford_alpaca
https://github.com/taylorkwan111/NLPMACQ
Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
https://github.com/tbmoon/LANL_Earthquake_Prediction
https://github.com/tchebonenko/Automated-Topic_Modeling-and-NER
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/tcnguyen/bert
https://github.com/tcxdgit/MachineTranslation_t2t
https://github.com/tdd-ai/data-collection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/techit-limtiyakul/bidirectional-attention-flow
https://github.com/teelinsan/camoscio
https://github.com/teganmaharaj/movieFIB
https://github.com/teghub/TurkishNER-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tejas-gokhale/vqa_mutant
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.