File size: 2,994 Bytes
e3fde1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | # BERT-Base-Uncased Quantized Model for customer feedback sentiment analysis
This repository hosts a quantized version of the **bert-base-uncased** model, fine-tuned for social media sentiment analysis 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:** Social Media Sentiment Analysis
- **Dataset:** Social Media Sentiments Analysis Dataset [Kaggle]
- **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
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Load quantized model
model_name = "AventIQ-AI/sentiment_analysis_for_customer_feedback"
model = BertForSequenceClassification.from_pretrained(model_name).to(device)
tokenizer = BertTokenizer.from_pretrained(model_name)
#Function to make analysis
def predict_sentiment(text):
# Tokenize input text
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
# Move tensors to GPU if available
inputs = {key: val.to(device) for key, val in inputs.items()}
# Get model prediction
with torch.no_grad():
outputs = model(**inputs)
# Get predicted class
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
# Map back to sentiment labels
sentiment_labels = {0: "Negative", 1: "Neutral", 2: "Positive"}
return sentiment_labels[predicted_class]
# Define a test sentence
test_sentence = "Spending time with family always brings me so much joy."
print(f"Predicted Sentiment: {predict_sentiment(text)}")
```
## Performance Metrics
- **Accuracy:** 0.82
- **Precision:** 0.68
- **Recall:** 0.82
- **F1 Score:** 0.73
## Fine-Tuning Details
### Dataset
The dataset is taken from Kaggle Social Media Sentiment Analysis.
### Training
- Number of epochs: 6
- Batch size: 8
- Evaluation strategy: epoch
- Learning rate: 3e-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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