README.md

---
license: apache-2.0
language:
- en
metrics:
- precision
- recall
- f1
- accuracy
new_version: v1.1
datasets:
- custom
- chatgpt
pipeline_tag: text-classification
library_name: transformers
tags:
- emotion
- classification
- text-classification
- bert
- emojis
- emotions
- v1.0
- sentiment-analysis
- nlp
- lightweight
- chatbot
- social-media
- mental-health
- short-text
- emotion-detection
- transformers
- real-time
- expressive
- ai
- machine-learning
- english
- inference
- edge-ai
- smart-replies
- tone-analysis
base_model:
- boltuix/bert-lite
- boltuix/bert-mini
---

# BERT Mini Sentiment Analysis – Emotion & Text Classification Model

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Transformers](https://img.shields.io/badge/Library-Transformers-orange)](https://huggingface.co/docs/transformers)
[![Machine Learning](https://img.shields.io/badge/Machine%20Learning-NLP-brightgreen)](https://en.wikipedia.org/wiki/Natural_language_processing)
[![Sentiment Analysis](https://img.shields.io/badge/Task-Sentiment%20Analysis-blue)](https://huggingface.co/tasks/text-classification)
[![Model: BERT](https://img.shields.io/badge/Model-BERT%20Mini-lightgrey)](https://huggingface.co/boltuix/bert-mini)
[![Language: English](https://img.shields.io/badge/Language-English-blue)](https://en.wikipedia.org/wiki/English_language)
[![Version: v1.1](https://img.shields.io/badge/Version-v1.1-yellow)](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini)


![Banner](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhvBb9EaSNBPVkqAU-0WBSb37cKqsdY83ygXDDuFphRELsOYGOanbOD2W-y5JYRfnJV-ni7ZtAZoZzms72NZFQn9HLQ4j14zRI8OB3S40MI1NZq2ldcJ81k_uTHsTs1ltT2c2bdt0oIpoHFQUZuJp9Zl-pexTS6nW3uDW-o7Wkf9lwYK0e_h_cmyiCZY3w/s1080/ml%20(1).png)

---

## 🌟 Overview

The **[BERT Mini Sentiment Analysis](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini)** model is a **lightweight, high-performance transformer** fine-tuned from **[Boltuix's BERT Mini](https://huggingface.co/boltuix/bert-mini)** for **emotion-based sentiment analysis**. It excels at classifying text into emotional categories such as **happiness**, **sadness**, **anger**, and more, making it ideal for understanding human emotions in text.

With only **11.2M parameters**, this model is **fast, efficient**, and tailored for **low-resource environments** like mobile devices, edge computing, and real-time applications. Whether you're analyzing social media trends, customer feedback, or building sentiment-aware chatbots, this model delivers **robust performance** with minimal computational overhead.

---

## 🛠️ Model Details

- **Model Name:** BERT Mini Sentiment Analysis
- **Developed by:** Varnika S
- **Model Type:** Transformer (BERT-based)
- **Base Model:** [Boltuix BERT Mini](https://huggingface.co/boltuix/bert-mini)
- **Language:** English (en)
- **License:** [MIT](https://opensource.org/licenses/MIT)
- **Parameters:** 11.2M
- **Pipeline Tag:** Text Classification
- **Library:** Transformers (Hugging Face)

This model is fine-tuned on an **emotion-labeled dataset**, ensuring high accuracy in detecting nuanced emotional states. Its compact size and optimized architecture make it perfect for **real-time applications** and **resource-constrained environments**.

---

## 🚀 Key Applications

Explore the versatile use cases of this model:

| **Use Case** | **Description** |
|--------------|-----------------|
| **Social Media Monitoring** | Track sentiment trends on platforms like Twitter, Reddit, and Instagram to understand audience emotions. |
| **Customer Feedback Analysis** | Extract actionable insights from product reviews, surveys, and support tickets. |
| **Mental Health AI** | Detect emotional distress or mood patterns in online conversations for proactive interventions. |
| **AI Chatbots & Assistants** | Build sentiment-aware chatbots that respond empathetically to user emotions. |
| **Market Research** | Analyze audience reactions to products, campaigns, or services for data-driven decisions. |

---

## 💻 Example Usage

Get started with the model using the **Hugging Face Transformers** library. Below is a simple example to classify text sentiment:

```python
from transformers import pipeline

# Initialize the sentiment analysis pipeline
sentiment_analyzer = pipeline("text-classification", model="Varnikasiva/sentiment-classification-bert-mini")

# Analyze text
text = "I feel amazing today!"
result = sentiment_analyzer(text)
print(result)  # Output: [{'label': 'happy', 'score': 0.98}]
```

🔗 **Try it now**: [Hugging Face Model Page](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini)

For more advanced usage, check out the [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers).

---

## 📊 Model Performance

The model delivers **high accuracy** and **ultra-fast inference**, making it a top choice for real-time applications.

| **Metric** | **Score** |
|------------|-----------|
| **Accuracy** | High (fine-tuned on emotion-labeled dataset) |
| **Inference Speed** | ⚡ Ultra-fast (optimized for low-latency) |
| **Model Size** | 11.2M Parameters |
| **Training Data** | Emotion-Labeled Dataset |

The model's lightweight design ensures **low memory usage** and **high throughput**, even on edge devices.

---

## 🛠️ Fine-Tuning Guide

Want to adapt the model for your specific domain (e.g., finance, healthcare, or customer service)? You can fine-tune it further using **Hugging Face's Trainer API** or **PyTorch Lightning**. Here's a sample setup:

```python
from transformers import Trainer, TrainingArguments

# Define training arguments
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,
    save_strategy="epoch",
    logging_dir="./logs",
)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

# Start fine-tuning
trainer.train()
```

This setup allows you to **customize the model** for domain-specific tasks with minimal effort.

---

## ❓ Frequently Asked Questions (FAQ)

### **Q1: What datasets were used for fine-tuning?**
The model was fine-tuned on a **curated emotion-labeled dataset**, enabling it to accurately detect emotions like happiness, sadness, anger, and more.

### **Q2: Is this model suitable for real-time applications?**
Absolutely! Its **compact size** and **optimized inference speed** make it ideal for real-time use cases like chatbots, social media monitoring, and live sentiment analysis.

### **Q3: Can I fine-tune this model for my own use case?**
Yes! Use the **Hugging Face Trainer API** or **PyTorch Lightning** to fine-tune the model on your dataset for enhanced performance in specific domains.

### **Q4: What makes this model different from other BERT models?**
This model is based on **Boltuix's BERT Mini**, a lightweight version of BERT with only 11.2M parameters, fine-tuned specifically for **emotion-based sentiment analysis**. It balances performance and efficiency, making it perfect for resource-constrained environments.

---

## 🔗 Additional Resources

- 📚 [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers)
- 🧠 [Boltuix BERT Mini Model](https://huggingface.co/boltuix/bert-mini)
- 📜 [MIT License](https://opensource.org/licenses/MIT)
- 📖 [Guide to Fine-Tuning BERT Models](https://huggingface.co/docs/transformers/training)

---

## 🤝 Contribute & Collaborate

We welcome contributions, feedback, and ideas to enhance this model! Whether it's improving performance, adding new features, or exploring new applications, your input is valuable.

- **Report Issues:** Open an issue on the [Hugging Face model page](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini).
- **Suggest Features:** Share your ideas for extending the model's capabilities.
- **Collaborate:** Interested in research or building applications? Reach out!

📬 **Contact:** [[email protected]](mailto:[email protected])

---

## 🌟 Why Choose This Model?

- **Lightweight & Efficient:** Only 11.2M parameters for fast inference on low-resource devices.
- **Emotion-Focused:** Fine-tuned for nuanced emotion detection, not just positive/negative sentiment.
- **Open-Source:** Licensed under MIT for flexible use in commercial and research projects.
- **Easy to Use:** Seamless integration with Hugging Face's Transformers library.
- **Versatile:** Applicable to social media, customer feedback, mental health, and more.

---

## 🎯 Get Started Today!

Ready to dive into emotion-based sentiment analysis? Head over to the [Hugging Face Model Page](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini) to explore the model, try the demo, or download it for your project.

**Happy Coding! 🚀**

---

*Tags: #transformers #bert #nlp #sentiment-analysis #emotion-detection #huggingface #text-classification #machine-learning #open-source #ai #mental-health #customer-feedback #social-media-analysis*