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sh7vashrestha/
BertBaseUncased-SenetimentAnalysis

Name: BertBaseUncased-SenetimentAnalysis
Availability: InStock

pytorch

bert

text-classification

google-bert/bert-base-uncased

finetune

mit

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KDLLM Teacher Model — Fine-tuned BERT (IMDb Sentiment Classification)

This repository hosts the Teacher Model used in the KDLLM framework from:

KDLLM: Knowledge Distillation for Compressed and Copyright-Safe Large Language Model Sharing
Shiva Shrestha et al.
Tsinghua Science and Technology, 2025
Manuscript ID: TST-2025-0253

📄 Overview

The teacher model is based on bert-base-uncased, fine-tuned on IMDb sentiment classification dataset for binary classification (positive/negative). This model serves as the high-capacity reference for training compact student models using knowledge distillation.

🔧 Model Architecture

Base Model: bert-base-uncased
Fine-tuning Task: Sentiment Classification (IMDb dataset)
Layers: 12
Hidden Size: 768
Attention Heads: 12
Total Parameters: ~110M
File Size: ~418MB

📊 Performance

Dataset: IMDb (50,000 movie reviews)
Accuracy: 92.40%
F1 Score: 92.44%

🚀 Inference Example

python

from transformers import BertForSequenceClassification, BertTokenizer

tokenizer = BertTokenizer.from_pretrained("sh7vashrestha/BertBaseUncased-SenetimentAnalysis")
model = BertForSequenceClassification.from_pretrained("sh7vashrestha/BertBaseUncased-SenetimentAnalysis")

inputs = tokenizer("The movie was absolutely wonderful!", return_tensors="pt")
outputs = model(**inputs)
prediction = outputs.logits.argmax(dim=1)
print(prediction)
label_mapping = {0: "negative", 1: "positive"}
prediction_label = label_mapping[prediction.item()]
print(prediction_label)