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Model Details

Model Description

This is the an implementation of the Token Classification as mentioned here. A PEFT model has been fine tuned to a token classification task for Bio Entity recognition from base model of roberta-large. Objective is to identify BIO Named Entity Recognition.

Given a statement [ "During", "treatment", "with", "Hm", ",", "K562", "cells", "constitutively", "expressed", "c-myb", "mRNA", ",", "and", "50", "%", "of", "them", "began", "to", "synthesize", "hemoglobin", "(", "Hb", ")", "." ]

it would generate the tags [ 0, 0, 0, 3, 0, 7, 8, 0, 0, 9, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0 ]

And the label id categories are { "O": 0, "B-DNA": 1, "I-DNA": 2, "B-protein": 3, "I-protein": 4, "B-cell_type": 5, "I-cell_type": 6, "B-cell_line": 7, "I-cell_line": 8, "B-RNA": 9, "I-RNA": 10 }

More details can be found here

Developed by: PEFT Example
Model type: Token Classification using LLM
Finetuned from: model roberta-large

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