Papers with Code 아카이브

Semantic Retrieval

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Phrase-in-Context/eval

공식 코드

PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search

https://github.com/TickleForce/ancient-greek-datasets

공식 코드

Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation

https://github.com/ant-uni-bremen/sinfony

공식 코드

Semantic Information Recovery in Wireless Networks

https://github.com/applicaai/contract-discovery

공식 코드

Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines

https://github.com/caiyinqiong/Semantic-Retrieval-Models

공식 코드

Semantic Models for the First-stage Retrieval: A Comprehensive Review

https://github.com/cas-siat-xinhai/aptness

공식 코드

APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation

https://github.com/chengzhipanpan/paser

공식 코드

Sentence Representation Learning with Generative Objective rather than Contrastive Objective

https://github.com/darkpromise98/iaa

공식 코드

Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning

https://github.com/easonnie/semanticRetrievalMRS

공식 코드

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

https://github.com/edoost/retro_bm25

공식 코드

Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models

https://github.com/gentaiscool/miners

공식 코드

MINERS: Multilingual Language Models as Semantic Retrievers

https://github.com/gissygonzalez/kleiogpt

공식 코드

If the Sources Could Talk: Evaluating Large Language Models for Research Assistance in History

https://github.com/kaelsunkiller/vat

공식 코드

Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image Captioning

https://github.com/kevinkrahn/ancient-greek-datasets

공식 코드

Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation

https://github.com/kwanwaichung/m4le

공식 코드

M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models

https://github.com/liyunqianggyn/Deep-Unsupervised-Image-Hashing

공식 코드

Deep Unsupervised Image Hashing by Maximizing Bit Entropy

https://github.com/lowlevelai/glare

공식 코드

GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval

https://github.com/michelebri/multi_agent_document_generation

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LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain

https://github.com/ncbi/biocpt

공식 코드

MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval

https://github.com/ncbi/medcpt

공식 코드

MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval

https://github.com/nick-zheng-q/hdlxgraph

공식 코드

HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databases

https://github.com/sdadas/polish-sentence-evaluation

공식 코드

Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases

https://github.com/wotchin/AnDB

공식 코드

AnDB: Breaking Boundaries with an AI-Native Database for Universal Semantic Analysis

https://github.com/xuandongzhao/hpd

공식 코드

Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation

https://github.com/dujiaxin/semanticRetrievalMRS

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

https://github.com/kamperh/flickr_semantic_qbe_eval

Semantic query-by-example speech search using visual grounding

https://github.com/khazarkhorrami/VGS_alignment

Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models

https://github.com/mayurbhangale/multimodal-retrieval

Multimodal semantic retrieval for product search

https://github.com/microsoft/MInference

SCBench: A KV Cache-Centric Analysis of Long-Context Methods

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.