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Model Details
This is a tinyBERT based classification model trained on banking77 dataset
Model Description
This model is the distilled version of a BERTClassifier which was 483MB in size and achieved 92+% accuracy after fine tuning using banking77 dataset
The distilled model is based on tinyBERT and is approx 54MB in size and is ~86.5% accurate on the banking77 dataset classification
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Model Sources [optional]
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Uses
Direct Use
Can be used for text classification of incoming bank customer queries
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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
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
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Factors
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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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