Varnikasiva/sentiment-classification-bert-mini

BERT Mini Sentiment Analysis – Emotion & Text Classification Model

Model Details

BERT Mini Sentiment Analysis is a lightweight transformer model fine-tuned from Prajjwal's BERT Mini for emotion-based sentiment analysis. It classifies text into various emotional labels such as happiness, sadness, anger, and others.

With 11.2M parameters, this model is fast, efficient, and optimized for real-time applications, making it perfect for low-resource environments like mobile and edge devices.

• Developed by: Varnika S
• Model Type: Transformer
• Language: English (en)
• License: MIT
• Finetuned from: Prajjwal's BERT Mini

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🚀 Key Applications

Use Case	Description
Social Media Analysis	Analyze sentiment trends on Twitter, Reddit, and Instagram
Customer Feedback	Extract insights from product reviews, surveys, and support tickets
Mental Health AI	Detect emotional distress in online conversations
AI Chatbots & Virtual Assistants	Enable sentiment-aware chatbot responses
Market Research	Understand audience reactions to products and services

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Example Usage

Use the model easily with the Hugging Face Transformers library:

from transformers import pipeline

# Load the fine-tuned BERT Mini sentiment analysis model
sentiment_analysis = pipeline("text-classification", model="Varnikasiva/sentiment-classification-bert-mini")

# Analyze sentiment
result = sentiment_analysis("I feel amazing today!")
print(result)  # Output: [{'label': 'happy'}]

🚀 Try it here: Hugging Face Model Page

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🔥 Model Performance

Metric	Score
Accuracy	High
Inference Speed	⚡ Ultra-fast
Model Size	11.2M Parameters
Fine-Tuned On	Emotion-Labeled Dataset

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📌 How to Fine-Tune Further?

To fine-tune this model on your own dataset, use Hugging Face's Trainer API or PyTorch Lightning:

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
)

This allows you to adapt the model to specific domains, such as finance, healthcare, or customer service.

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💡 Frequently Asked Questions (FAQ)

Q1: What datasets were used for fine-tuning?

A: This model was fine-tuned on an emotion-labeled dataset, ensuring high accuracy for detecting happiness, sadness, anger, and more.

Q2: Can I use this model for real-time applications?

A: Yes! The model is optimized for low-latency and high-speed inference, making it perfect for chatbots, social media monitoring, and real-time sentiment analysis.

Q3: How can I fine-tune this model further?

A: You can fine-tune it on your own data using Hugging Face's Trainer API or PyTorch Lightning for better domain-specific performance.

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🔗 Additional Resources

• Hugging Face Documentation
• BERT Mini Architecture
• MIT License Details

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🚀 Contribute & Give Feedback

Feel free to contribute to this project or provide feedback to help improve the model. If you encounter issues or have feature requests, please reach out! 🎯

Happy Coding! 🚀