Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zihaow123/unimm
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/zion-king/Deep-Learning-for-Person-Re-identification
https://github.com/zooomg/m-jave-pytorch
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product
https://github.com/zorazrw/multilingual-conala
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
https://github.com/zveryansky/textvec
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
https://github.com/zxy14120448/Summary
Class-Weighted Convolutional Features for Visual Instance Search
https://github.com/zysszy/TreeGen
https://github.com/zzmshinnosuke/sketch
https://gitlab.com/birder/birder
https://gitlab.com/dprl/reactionminer_search
https://gitlab.com/hararticles/gs-ms-mt
https://gitlab.com/paloha/gabor-scattering
https://huggingface.co/lion-ai/MedImageInsights
MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.