AventIQ-AI/sentiment-analysis-for-trending-topic-sentiment

BERT-Base-Uncased Quantized Model for Sentiment Analysis for Trending Topic 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 Trending Topic 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-trending-topic-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 new Pixel 9 Pro has finally launched, and the reactions are pouring in. Many users are thrilled with the upgraded camera system and the sleek design, calling it the best Android phone of the year. However, some are disappointed by the high price tag and limited availability in certain countries. While tech enthusiasts appreciate the improved AI features, casual users feel the changes are too minor to justify an upgrade. Overall, the buzz around the Pixel 9 Pro is strong, with a mix of praise and criticism driving the conversation."

# 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.