license:mit region:us
---
license: mit
---
Ultra-Lightweight Sentiment Analyzer
Welcome to the repository for the ultra-lightweight, real-time sentiment analysis model designed specifically for mobile applications. This model brings the power of BERT's transformer architecture to a form that's optimized for performance on mobile devices.

Model Details
Architecture: DistilBERT, a distilled version of the BERT model known for its balance between performance and efficiency.
Training Data: The model has been trained on a diverse semantic dataset, ensuring a robust understanding of language nuances.
Quantization: Post-training dynamic quantization is applied to reduce model size without a significant reduction in accuracy, making it ideal for on-device deployment.
Size: Approximately 130 MB, optimized for mobile environments.
Latency: Achieves real-time inference on mobile devices with reduced latency.
Usage
This model can be used directly for sentiment analysis tasks or further fine-tuned on domain-specific datasets. It's perfect for applications where quick, on-the-fly text analysis is needed, such as live customer feedback analysis or on-device content moderation.

python
Copy code
from transformers import pipeline
# Load the sentiment analysis pipeline
sentiment_pipeline = pipeline("sentiment-analysis", model="YOUR_MODEL_NAME")

# Analyze sentiment
result = sentiment_pipeline("I love using this mobile app, it's so fast and easy!")
print(result)
Applications
The sentiment analyzer is suited for a variety of real-world applications:

Customer Feedback Analysis: Quickly determine sentiment in user feedback or reviews.
Content Moderation: Maintain community guidelines by filtering inappropriate content in real-time.
Market Research: Analyze survey responses or social media posts to extract actionable insights.
Compliance Monitoring: Ensure communications adhere to industry regulations.
Performance Metrics
Refer to the attached performance graphs to understand the model's efficiency gains through quantization. The graphs illustrate latency improvements and training versus validation loss, showcasing the model's reliability and speed.

How to Contribute
Contributions to improve the model or expand its use cases are welcome. Please follow the contribution guidelines outlined in CONTRIBUTING.md.

License
This model is open-source and available under the MIT license, allowing for commercial and private use, modification, and distribution.

Acknowledgements



example usecase....
# Install required libraries
!pip install transformers torch
import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
from google.colab import files
uploaded = files.upload()
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
from torch.quantization import quantize_dynamic

# Load the tokenizer
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')

# Initialize the model structure
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', return_dict=True)

# Apply dynamic quantization to match the saved model structure
quantized_model = quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)

# Now load your quantized model state_dict
quantized_model.load_state_dict(torch.load('brain.pt', map_location=torch.device('cpu')))

# If you're using a GPU, move the model to GPU
quantized_model.to("cuda" if torch.cuda.is_available() else "cpu")
def ask_model(question):
    inputs = tokenizer(question, return_tensors="pt", padding=True, truncation=True, max_length=512)
    inputs = inputs.to("cuda" if torch.cuda.is_available() else "cpu")
    with torch.no_grad():
        outputs = quantized_model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return predictions  # Return the raw tensor of predictions
import torch

def simple_chatbot(input_text):
    # Classify the input text using your model
    predictions = ask_model(input_text)  # This should now return a tensor
    probabilities = torch.nn.functional.softmax(predictions, dim=-1)
    class_index = probabilities.argmax().item()  # Extract the predicted class index
    confidence = probabilities[0, class_index].item()  # Get the confidence of the prediction
    
    # Define your response logic based on the class index
    if class_index == 1:  # Assuming class 1 is positive sentiment
        response = f"I'm glad to hear that! How can I assist you further? (Confidence: {confidence:.2f})"
    elif class_index == 0:  # Assuming class 0 is negative sentiment
        response = f"I'm sorry to hear that. How can I help make things better? (Confidence: {confidence:.2f})"
    else:
        response = f"Could you tell me more about it? (Confidence: {confidence:.2f})"
    
    # Return both the input and the response including confidence
    return f"User said: \"{input_text}\"\nChatbot response: {response}\n"

# Example usage
print(simple_chatbot("I love this service!"))
print(simple_chatbot("This is terrible."))
print(simple_chatbot("Can you help me with my account?"))
print(simple_chatbot("Your app is amazing, saved me so much time!"))
print(simple_chatbot("I'm not happy with the service."))
print(simple_chatbot("How does the subscription model work?"))
