https://github.com/google-deepmind/xtr
공식 코드Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Papers with Code 아카이브
Retrieval
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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/google-research-datasets/KELM-corpus
공식 코드Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
https://github.com/google-research-datasets/MultiReQA
공식 코드MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models
https://github.com/google-research-datasets/Objectron
공식 코드Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations
https://github.com/google-research-datasets/cpcd
공식 코드https://github.com/google-research-datasets/mave
공식 코드MAVE: A Product Dataset for Multi-source Attribute Value Extraction
https://github.com/google-research-datasets/poem-sentiment
공식 코드Investigating Societal Biases in a Poetry Composition System
https://github.com/google-research-datasets/swim-ir
공식 코드Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval
https://github.com/google-research-datasets/wiki-translated-clusters-nli
공식 코드Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters
https://github.com/google-research-datasets/wit
공식 코드WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
https://github.com/google-research/arxiv-latex-cleaner
공식 코드Computational Job Market Analysis with Natural Language Processing
https://github.com/google-research/big_vision
공식 코드https://github.com/google-research/composed_image_retrieval
공식 코드Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
https://github.com/google-research/dialog-inpainting
공식 코드https://github.com/google-research/fool-me-twice
공식 코드https://github.com/google-research/google-research
공식 코드View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose
https://github.com/google-research/google-research/tree/master/dot_vs_learned_similarity
공식 코드Neural Collaborative Filtering vs. Matrix Factorization Revisited
https://github.com/google-research/google-research/tree/master/fvlm/rovit
공식 코드Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers
https://github.com/google-research/google-research/tree/master/smith
공식 코드https://github.com/google-research/google-research/tree/master/socraticmodels
공식 코드Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
https://github.com/google-research/google-research/tree/master/table_rag
공식 코드TableRAG: Million-Token Table Understanding with Language Models
https://github.com/google-research/google-research/tree/master/vatt
공식 코드VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/google-research/language
공식 코드QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations
https://github.com/google-research/language/tree/master/language/casper
공식 코드https://github.com/google-research/language/tree/master/language/multivec
공식 코드Sparse, Dense, and Attentional Representations for Text Retrieval
https://github.com/google-research/language/tree/master/language/orqa
공식 코드Latent Retrieval for Weakly Supervised Open Domain Question Answering
https://github.com/google-research/language/tree/master/language/realm
공식 코드https://github.com/google-research/perch
공식 코드BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics
https://github.com/google-research/scenic
공식 코드https://github.com/google-research/scenic/tree/main/scenic/projects/knowledge_visual_language
공식 코드https://github.com/google-research/tapas
공식 코드Open Domain Question Answering over Tables via Dense Retrieval
https://github.com/google-research/vision_transformer
공식 코드https://github.com/google-research/vmf_embeddings
공식 코드von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning
https://github.com/google-research/xtreme
공식 코드XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization
https://github.com/google/n-digit-mnist
공식 코드https://github.com/google/retrieval-qa-eval
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.