Papers with Code 아카이브
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/karpathy/build-nanogpt
https://github.com/karpathy/llama2.c
https://github.com/karpathy/llm.c
https://github.com/karpathy/makemore
https://github.com/karpathy/minGPT
https://github.com/karta282950/bert-multiclass
https://github.com/kasys-lab/anserini-kasys
https://github.com/katamb/tree-of-thought-llm-ca
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
https://github.com/kathrynchapman/QA_project
Attentive History Selection for Conversational Question Answering
https://github.com/kaushaltrivedi/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kaushikb258/Habitat_RL
https://github.com/kayvr/token-hawk
https://github.com/kbressem/medalpaca
https://github.com/keiyamashita111/aaproject
https://github.com/kelly2016/multi-label-bert
https://github.com/kelvinguu/qanli
Transforming Question Answering Datasets Into Natural Language Inference Datasets
https://github.com/keonlee9420/Parallel-Tacotron2
https://github.com/ketranm/neuralHMM
Adding Gradient Noise Improves Learning for Very Deep Networks
https://github.com/kevin-rn/Efficient-Fact-checking
https://github.com/kevindeangeli/capsuleNetwork
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
https://github.com/kevinkwl/AoAReader
Attention-over-Attention Neural Networks for Reading Comprehension
https://github.com/kevinscaria/feasibilityqa
"John is 50 years old, can his son be 65?" Evaluating NLP Models' Understanding of Feasibility
https://github.com/kexinyi/ns-vqa
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
https://github.com/kh-kim/simple-nmt
https://github.com/khalidsaifullaah/bertify
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/khanhnguyen21006/fldocvqa
https://github.com/khushsi/Finetuning_BART_for_FACET_Summarization
https://github.com/khwajawisal/Attention-augmented-Convolutional-Neural-Networks
https://github.com/kiko441500/google_bert
https://github.com/kimhc6028/relational-networks
https://github.com/kingcheng2000/bert
https://github.com/kingcong/gpu_albert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kingoflolz/mesh-transformer-jax
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/kinimod23/ATS_Project
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
https://github.com/kinimod23/NMT_Project
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.