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BERT-Base-Uncased Quantized Model for social media 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
pip install transformers torch
Loading the Model
from transformers import BertForSequenceClassification, BertTokenizer
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Load quantized model
model_name = "AventIQ-AI/bert-social-media-sentiment-analysis"
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
.
βββ model/ # Contains the quantized model files
βββ tokenizer_config/ # Tokenizer configuration and vocabulary files
βββ model.safensors/ # Fine Tuned Model
βββ 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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