Papers with Code 아카이브
Question Answering
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- 평가표
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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/1171-jpg/brainteaser
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
https://github.com/1230113202/NV-JM-DD
https://github.com/1rsh/raptor-rag
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
https://github.com/1wy/bert
https://github.com/2003pro/Graph2Tree
https://github.com/2023-MindSpore-1/ms-code-11
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-12
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Pangu_alpha
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Transformer
https://github.com/2023-MindSpore-1/ms-code-145
METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation
https://github.com/2023-MindSpore-1/ms-code-146
LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning
https://github.com/2023-MindSpore-1/ms-code-149
https://github.com/2023-MindSpore-1/ms-code-150
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-152
https://github.com/2023-MindSpore-1/ms-code-154
https://github.com/2023-MindSpore-1/ms-code-155
https://github.com/2023-MindSpore-1/ms-code-156
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/2023-MindSpore-1/ms-code-159
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
https://github.com/2023-MindSpore-1/ms-code-161
https://github.com/2023-MindSpore-1/ms-code-162
https://github.com/2023-MindSpore-1/ms-code-163
https://github.com/2023-MindSpore-1/ms-code-164
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-1/ms-code-166
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/2023-MindSpore-1/ms-code-190
https://github.com/2023-MindSpore-1/ms-code-218/tree/main/sdne
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/emotect
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/ernie
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/luke
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
https://github.com/2023-MindSpore-1/ms-code-56
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-98
Community Question Answering Entity Linking via Leveraging Auxiliary Data
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm2
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/llama
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.