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Performance-Optimization-and-Model-Compression: I used the bert-base-uncased to evaluate the model's power towards the Model distillation. The model( bert-base-uncased ) performed exceptionally in terms of performance(Accuracy, speed and size) while compressing the model.

** Conclusion: ** Model parameters were reduced to 2.8 times while achieving an accuracy increase of 3% over the original base model.

Observations from the experiment:

Base Model -F32: 110M params having Accuracy - {'Accuracy': 0.893}

Distilled Model(Reduced to half): 52.8M params having Accuracy - {'Accuracy': 0.924}

Quantized Model(Reduced to 2.8 times): 38.8M params having Accuracy - {'Accuracy': 0.922}

Details are below:

I trained two classifier models, one for the teacher(size and accuracy same as base) and the other for the student(model size reduced to half and accuracy increased than base model by 3%).

Teacher (validation) -Accuracy: 0.8933, Precision: 0.9078, Recall: 0.8756, F1 Score: 0.8914

Student (validation) -Accuracy: 0.9244, Precision: 0.9704, Recall: 0.8756, F1 Score: 0.9206

Further, I quantized the model to 4 bits and got the magical performance:

Pre-quantization Perf -Accuracy: 0.9244, Precision: 0.9704, Recall: 0.8756, F1 Score: 0.9206

Post-quantization Perf -Accuracy: 0.9222, Precision: 0.9703, Recall: 0.8711, F1 Score: 0.9180

GPU: Tesla T4

Dataset: shubh2ds/data-phishing-site-clf

Consumption:

Final Model: https://huggingface.co/shubh2ds/bert-base-uncased-phishing-classifier_student_4bit

https://wandb.ai/shubh2ds/huggingface/runs/yt0pcw58/workspace?nw=nwusershubh2ds

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Developed by: Shubham Kumar (AMD)
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