---
base_model: google/gemma-2b
library_name: peft
pipeline_tag: text-generation
tags:
- sentiment-analysis
- nlp
- lora
- peft
- transformers
- business-analytics
- social-media-analytics
---

# Sentiment Analyzer (LoRA Fine-tuned Gemma-2B)

## Model Summary

This repository contains a **Sentiment Analysis model** fine-tuned using **LoRA (Low-Rank Adaptation)** on top of **Google’s Gemma-2B** base model.  
The model is designed for **educational, research, and applied business analytics use cases**, especially sentiment analysis of textual data such as customer feedback and social media content.

---

## Model Details

- **Model Name:** Sentiment Analyzer  
- **Developed by:** Varun Agrawal  
- **Hugging Face Username:** `09Vaarun`  
- **Affiliation:** IIRM Jaipur  
- **Model Type:** Natural Language Processing (Sentiment Analysis / Text Generation)  
- **Base Model:** google/gemma-2b  
- **Fine-tuning Technique:** PEFT (LoRA)  
- **Language:** English  
- **License:** Apache 2.0  

---

## Intended Use

### ✅ Direct Use

This model can be used for:

- Sentiment analysis of:
  - Customer reviews  
  - Social media posts  
  - Online feedback forms  
  - Business and marketing text  
- Academic demonstrations of:
  - Transformers  
  - Parameter-Efficient Fine-Tuning (PEFT)  
  - LoRA-based adaptation  

### 🔄 Downstream Use

- Social media analytics projects  
- Business intelligence dashboards  
- NLP coursework and workshops  
- Research experiments in sentiment analysis  

### ❌ Out-of-Scope Use

- Medical, legal, or financial decision-making  
- High-stakes automated systems without human review  

---

## Bias, Risks, and Limitations

- The model may reflect biases present in the training data  
- Performance may vary across domains and writing styles  
- Not recommended for critical real-world decisions without further evaluation  

### Recommendations

- Perform domain-specific validation before deployment  
- Use human oversight for business applications  

---

## How to Use the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = "google/gemma-2b"
adapter_model = "09Vaarun/sentiment-analyzer"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_model)

text = "The service was excellent and the staff was very helpful."
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=50
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))