---
base_model: google/gemma-2b
library_name: peft
pipeline_tag: text-generation
tags:
- sentiment-analysis
- lora
- transformers
- peft
---

# Sentiment Analyzer

A fine-tuned sentiment analysis model developed and shared by **Pavithrapn-01**.  
This model is designed to analyze text and classify sentiment efficiently using a lightweight fine-tuning approach.

---

## Model Details

### Model Description
This model is a **sentiment analysis system** built by fine-tuning the **google/gemma-2b** base model using **LoRA (Low-Rank Adaptation)**.  
It is optimized for understanding emotional polarity in text such as **positive, negative, or neutral sentiment**.

- **Developed by:** Pavithra PN  
- **Shared by:** Pavithrapn-01  
- **Model type:** Text Generation / Sentiment Analysis  
- **Language(s):** English  
- **License:** Open-source (same as base model)  
- **Finetuned from model:** google/gemma-2b  

---

## Model Sources

- **Repository:** Pavithrapn-01/sentiment-analyzer  
- **Base Model:** google/gemma-2b  

---

## Uses

### Direct Use
- Sentiment analysis of user reviews
- Opinion mining from social media text
- Feedback and survey analysis
- Educational and academic projects

### Downstream Use
- Can be integrated into chatbots
- Can be used in recommendation systems
- Can be further fine-tuned for domain-specific sentiment tasks

### Out-of-Scope Use
- Medical or legal decision-making
- High-risk or safety-critical applications
- Multilingual sentiment analysis (English only)

---

## Bias, Risks, and Limitations

- The model may reflect biases present in the training data
- Performance may vary on slang, sarcasm, or ambiguous text
- Best suited for short to medium-length text inputs

### Recommendations
Users should validate outputs before deploying the model in real-world applications and avoid using it for sensitive decision-making.

---

## How to Get Started with the Model

```python
from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="Pavithrapn-01/sentiment-analyzer")
result = classifier("I really enjoyed using this application!")
print(result)