BERT-Base-Uncased Quantized Model for Sentiment Analysis for Healthcare Policy Sentiment

This repository hosts a quantized version of the BERT model, fine-tuned for stock-market-analysis-sentiment-classification tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.

Model Details

Model Architecture: BERT Base Uncased
Task: Sentiment Analysis for Healthcare Policy Sentiment
Dataset: Stanford Sentiment Treebank v2 (SST2)
Quantization: Float16
Fine-tuning Framework: Hugging Face Transformers

Usage

Installation

pip install transformers torch

Loading the Model


from transformers import BertForSequenceClassification, BertTokenizer
import torch

# Load quantized model
quantized_model_path = "AventIQ-AI/sentiment-analysis-for-healthcare-policy-sentiment"
quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path)
quantized_model.eval()  # Set to evaluation mode
quantized_model.half()  # Convert model to FP16

# Load tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

# Define a test sentence
test_sentence = "The recent changes in the national healthcare policy have sparked mixed reactions. While many applaud the government's efforts to expand access to affordable healthcare, some critics argue that the implementation lacks clarity and may strain existing hospital infrastructure. Patients in rural areas are hopeful about improved services, but healthcare workers worry about increased workload without additional support."

# Tokenize input
inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Ensure input tensors are in correct dtype
inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type
inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

# Make prediction
with torch.no_grad():
    outputs = quantized_model(**inputs)

# Get predicted class
predicted_class = torch.argmax(outputs.logits, dim=1).item()
print(f"Predicted Class: {predicted_class}")


label_mapping = {0: "very_negative", 1: "nagative", 2: "neutral", 3: "Positive", 4: "very_positive"}  # Example

predicted_label = label_mapping[predicted_class]
print(f"Predicted Label: {predicted_label}")

Performance Metrics

Accuracy: 0.82

Fine-Tuning Details

Dataset

The dataset is taken from Kaggle Stanford Sentiment Treebank v2 (SST2).

Training

Number of epochs: 3
Batch size: 8
Evaluation strategy: epoch
Learning rate: 2e-5

Quantization

Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.

Repository Structure

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation

Limitations

The model may not generalize well to domains outside the fine-tuning dataset.
Quantization may result in minor accuracy degradation compared to full-precision models.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.