FatimaHaroon/claim-denial-risk

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Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Fatima Haroon
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: BERT-based binary classifier
• Language(s) (NLP): English (en)
• License: Apache-2.0
• Finetuned from model [optional]: llmware/industry-bert-insurance-v0.1

Model Sources [optional]

• Repository: https://huggingface.co/fatimaharoon/claim-denial-risk)
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Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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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

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training Data Source: designfailure/my-agentic-InsurTech (“claim-rejected” split)

Split: 90/10 train-test, 557 train / 62 test examples

Training Procedure Preprocessing: Lowercased raw text; tokenized to max_length=256.

Hyperparameters:

Epochs: 3

Batch size: 16 (train), 32 (eval)

Learning rate: default 5e-5

Mixed precision (fp16) on GPU

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

Test Accuracy: 85.5%

Test F1-score: 90.3%

Denial Recall: 100% (42/42)

Approval Precision: 55% (11/20)

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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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Compute Infrastructure

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Hardware

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Software

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