transformers safetensors bert text-classification code en base_model:prajjwal1/bert-mini base_model:finetune:prajjwal1/bert-mini license:mit autotrain_compatible endpoints_compatible region:us
---
base_model:
- prajjwal1/bert-mini
language:
- en
library_name: transformers
license: mit
pipeline_tag: text-classification
tags:
- code
---

# BERT Mini Sentiment Analysis – Emotion & Text Classification Model

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

## Model Details  
[BERT Mini Sentiment Analysis](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini) 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  

---

## 🚀 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 |

---

## Example Usage

Use the model easily with the **Hugging Face Transformers library**:

```python
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](https://huggingface.co/Varnikasiva/sentiment-classification-bert-mini)  

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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 |

---

## 📌 How to Fine-Tune Further?

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

```python
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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<!-- ## 🔖 Tags & SEO Keywords
```
#transformers #bert #nlp #sentiment-analysis #emotion-detection #huggingface #text-classification #machine-learning #open-source #ai #mental-health #customer-feedback #social-media-analysis
```

Adding these **hashtags** helps **increase visibility** on Hugging Face search results!

--- -->

## 💡 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.  

---

## 🔗 Additional Resources
- [Hugging Face Documentation](https://huggingface.co/docs/transformers)
- [BERT Mini Architecture](https://huggingface.co/prajjwal1/bert-mini)
- [MIT License Details](https://opensource.org/licenses/MIT)

---

## 🚀 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! 🚀**

---

## 🤝 Collaborate with Me

I'm open to collaborations, research projects, and ideas to extend this model or build similar applications.

📬 **Email:** [varnikas753@gmail.com](mailto:varnikas753@gmail.com)

