---
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract
pipeline_tag: text-classification
license: mit
library_name: transformers
tags:
- ignorance-classification
- knowledge-gap
- epilepsy
- biomedical
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

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

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- 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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- **Repository:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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. 

### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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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

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

```python
!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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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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