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# BERT-Base-Uncased Quantized Model for Sentiment Analysis for Customer Service 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 Customer Service Sentiment
- **Dataset:** Stanford Sentiment Treebank v2 (SST2)
- **Quantization:** Float16
- **Fine-tuning Framework:** Hugging Face Transformers
## Usage
### Installation
```sh
pip install transformers torch
```
### Loading the Model
```python
from transformers import BertForSequenceClassification, BertTokenizer
import torch
# Load quantized model
quantized_model_path = "AventIQ-AI/sentiment-analysis-for-customer-service-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 = "I reached out to customer support regarding a billing issue, and I was pleasantly surprised by how quickly the agent responded, explained everything clearly, and resolved the matter within minutes—truly exceptional service!"
# 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.
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