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Open Source Science

https://github.com/csinva/tpr-fmri

https://github.com/csinva/tpr-fmri

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
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    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
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    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.8%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: csinva
  • Language: Python
  • Default Branch: main
  • Size: 47.7 MB
Statistics
  • Stars: 3
  • Watchers: 4
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme

readme.md

Setup

  • clone the repo and run pip install -e ., resulting in a package named tpr that can be imported
  • download the linear encoding weights
    • OPT: download the weights here and move to the folder tpr-embeddings/fmri_voxel_data/llama_model/model_weights
    • rename the weights in that folder to wt_UTS01.jbl, wt_UTS01.jbl, wt_UTS03.jbl
    • LLaMA: download the weights here and move to the folder tpr-embeddings/fmri_voxel_data/llama_model/model_weights
    • rename the weights in that folder to wt_UTS01.jbl, wt_UTS01.jbl, wt_UTS03.jbl
  • if everything is set up properly, you should be able to run the notebooks/01moduleexample.ipynb notebook without any issues

Organization

  • data: contains text and scripts for text to evaluate the models on
    • data/fmri: shows a sample test story of the type that the models were trained on
  • voxel_data: contains metadata on the fMRI experiments
  • tpr: contains main code for modeling (e.g. model architecture)
  • notebooks: experiments in jupyter notebooks

Reference

This repo copies a lot of code from encoding-model-scaling-laws, which is the repo for the paper "Scaling laws for language encoding models in fMRI" (antonello, vaidya, & huth, 2023). See the cool results there! It also copies a lot of code from the repo for SASC.

Owner

  • Name: Chandan Singh
  • Login: csinva
  • Kind: user
  • Location: Microsoft research
  • Company: Senior researcher

Senior researcher @Microsoft interpreting ML models in science and medicine. PhD from UC Berkeley.

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Dependencies

setup.py pypi