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

https://github.com/FeiWang96/GTR

공식 코드

Retrieving Complex Tables with Multi-Granular Graph Representation Learning

https://github.com/Zyphra/TemporalMemoryDataset

공식 코드

Toward Conversational Agents with Context and Time Sensitive Long-term Memory

https://github.com/globosco/A-learned-sorted-table-search-library

공식 코드

Learned Sorted Table Search and Static Indexes in Small Model Space

https://github.com/globosco/An-implementation-of-Generic-Learned-Static-Sorted-Sets-Dictionaries

공식 코드

From Specific to Generic Learned Sorted Set Dictionaries: A Theoretically Sound Paradigm Yelding Competitive Data Structural Boosters in Practice

https://github.com/ibm/tabsketchfm

공식 코드

TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes

https://github.com/medtray/StruBERT

공식 코드

StruBERT: Structure-aware BERT for Table Search and Matching

https://github.com/northeastern-datalab/alt-gen

공식 코드

Generative Benchmark Creation for Table Union Search

https://github.com/thedatastation/pneuma

공식 코드

Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System

https://github.com/Zhiyu-Chen/SIGIR2020-BERT-Table-Search

Table Search Using a Deep Contextualized Language Model

https://github.com/microsoft/TUTA_table_understanding

Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks

https://github.com/microsoft/hitab

Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks

https://github.com/pwc-1/Paper-10/tree/main/tapex

Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks

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