This is the embedding training code for the scientific embedding project: [2405.11461] DocReLM: Mastering Document Retrieval with Language Model (arxiv.org).
The dataset is from the Synthetic data build via veya2ztn/Synthetic-Science: Those script try to create Synthetic Science QA answer-question pair efficiently and reasoning (github.com) based on veya2ztn/uparxive: llm-friendly dataest for the whole arxiv .tex source. (github.com)
This repo integrate embedder training method
2 commits
Python
59.0%
Jupyter Notebook
36.0%
Shell
2.8%
C++
1.9%
This is the embedding training code for the scientific embedding project: [2405.11461] DocReLM: Mastering Document Retrieval with Language Model (arxiv.org).
The dataset is from the Synthetic data build via veya2ztn/Synthetic-Science: Those script try to create Synthetic Science QA answer-question pair efficiently and reasoning (github.com) based on veya2ztn/uparxive: llm-friendly dataest for the whole arxiv .tex source. (github.com)
This repo integrate embedder training method
2 commits
Python
59.0%
Jupyter Notebook
36.0%
Shell
2.8%
C++
1.9%