hunter-lab/sentence-level-ignorance-classifier

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This model is a binary classifier that determines whether a sentence is a statement of ignorance or not.

Model Details

Model Description

• This model consists of a linear classification head on top of BioMedBERT, which is a BERT-based encoder (110 million parameters, 12 layers, 768 hidden).

• All layers besides 10, 11, and 12, the pooler, and classificaton head were frozen to reduce overfitting on the small training set.

• Developed by: Nathan Gelfand, Darya Shlyk

• Funded by: Larry Hunter

• Model type: Supervised fine-tune of a transformer-encoder for text classification

• Language(s) (NLP): English

• License: MIT

• Finetuned from model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract

Model Sources [optional]

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Uses

Direct Use

A statement of ignorance, or knowledge gap statement, is a piece of text indicating a lack of knoweldge on some subject. In the field of biomedical research, knowledge gap statements often indicate unanswered questions, which can be useful for researchers looking for new topics of study. We built this sentence-level binary classifier so that researchers could parse through biomedical papers to identify research directives.

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Out-of-Scope Use

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Bias, Risks, and Limitations

This model was trained on a dataset of 937 annotated statements from articles about monogenic epilepsies. Due to the small dataset size, it is possible that the classifier will perform better on statements related to monogenic epilepsies.

The statements

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

!pip install transformers 
from transformers import pipeline

classifier = pipeline("text-classification",model="hunter-lab/sentence-level-ignorance-classifier")

sentence = "Consequently, there is a pressing need to enhance basic mechanistic and clinical studies, with a focus on individual differences, to advance the exploration of BCAAs in treating neurological disorders and to provide more robust evidence supporting their clinical applications."

result = classifier(sentence)

print(result)

Training Details

Training Data

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Training Procedure

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Evaluation

Testing Data, Factors & Metrics

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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