mschrimpf/neural-nlp
[PNAS'21] The neural architecture of language: Integrative modeling converges on predictive processing
This project helps cognitive scientists and neuroscientists evaluate how well different computational language models predict human brain responses and behavior during language processing. You input a language model (like GPT-2) and a human neuroscience dataset, and it outputs a score indicating how closely the model's representations align with human brain activity or behavioral data. This is for researchers studying the neural basis of language.
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Use this if you are a cognitive scientist or neuroscientist who wants to rigorously compare various computational language models against empirical human language processing data.
Not ideal if you are looking for a general-purpose natural language processing library for tasks like text generation or sentiment analysis.
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Oct 25, 2023
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