https://github.com/openmatch/fit5
공식 코드Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
Papers with Code 아카이브
Document Ranking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 2
- 보존된 평가 기록
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Sort documents according to some criterion so that the "best" results appear early in the result list displayed to the user (Source: Wikipedia).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pxyu/llm-nle-for-calibration
공식 코드Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs
https://github.com/searchivarius/long_doc_rank_model_analysis
공식 코드https://github.com/sebastian-hofstaetter/fira-trec-19-dataset
공식 코드Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering
https://github.com/sebastian-hofstaetter/intra-document-cascade
공식 코드Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking
https://github.com/sebastian-hofstaetter/transformer-kernel-ranking
공식 코드Local Self-Attention over Long Text for Efficient Document Retrieval
https://github.com/seznam/dareczech
공식 코드Siamese BERT-based Model for Web Search Relevance Ranking Evaluated on a New Czech Dataset
https://github.com/slanglab/IndiaPoliceEvents
공식 코드https://github.com/smallporridge/socialformer
공식 코드Socialformer: Social Network Inspired Long Document Modeling for Document Ranking
https://github.com/smt-HS/DeepTileBars-release
공식 코드DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval
https://github.com/stanford-futuredata/ColBERT
공식 코드ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
https://github.com/thongnt99/dyvo
공식 코드DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
https://github.com/thunlp/OpenMatch
공식 코드https://github.com/yeziyi1998/brain-query-augmentation
공식 코드https://github.com/zhichaoxu-shufe/rankmamba
공식 코드RankMamba: Benchmarking Mamba's Document Ranking Performance in the Era of Transformers
https://github.com/zihangdai/xlnet
공식 코드XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-220/tree/main/tinybert
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
https://github.com/2miatran/Natural-Language-Processing
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/AdeDZY/K-NRM
https://github.com/Crysitna/CEDR_tpu
https://github.com/Georgetown-IR-Lab/curricula-neural-ir
https://github.com/IBM/ColBERT-practical
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
https://github.com/JoyDajunSpaceCraft/RAG-RLRC-LaySum
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
https://github.com/MS-P3/code7/tree/main/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/Mind23-2/MindCode-149
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
https://github.com/MindCode-4/code-5/tree/main/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/MindSpore-paper-code-3/code9/tree/main/tinybert
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
https://github.com/MulukenW/test
https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/SambhawDrag/XLNet.jl
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/TikaToka/CapstoneSpring
https://github.com/bmitra-msft/NDRM
Learning to Match Using Local and Distributed Representations of Text for Web Search
https://github.com/castorini/docTTTTTquery
https://github.com/castorini/duobert
https://github.com/castorini/pygaggle
Document Ranking with a Pretrained Sequence-to-Sequence Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.