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

# Sentiment Analyzer Model

## Overview

This repository contains a **Sentiment Analysis model** fine-tuned using **LoRA (Low-Rank Adaptation)** on top of the **Google Gemma-2B** base model.  
The model is designed to analyze text input and determine the **sentiment polarity** (positive, negative, or neutral).

The model is hosted on Hugging Face and uploaded using the `huggingface_hub` Python API.

---

## Model Details

### Model Description

- **Developed by:** Archana S  
- **Hugging Face Username:** `archanaachu776`  
- **Model type:** Text Generation / Sentiment Analysis  
- **Language(s):** English  
- **Base Model:** google/gemma-2b  
- **Fine-tuning Method:** PEFT (LoRA)  
- **Library:** Transformers + PEFT  
- **License:** Apache 2.0  
- **Finetuned from:** google/gemma-2b  

---

## Model Sources

- **Repository:** https://huggingface.co/archanaachu776/sentiment-analyzer  
- **Base Model:** https://huggingface.co/google/gemma-2b  

---

## Intended Uses

### Direct Use

- Analyze sentiment of user reviews
- Customer feedback analysis
- Social media sentiment monitoring
- Text classification tasks requiring sentiment polarity

### Downstream Use

- Can be integrated into chatbots
- Can be used in recommendation systems
- Can be extended for domain-specific sentiment analysis (e.g., product, finance, healthcare)

### Out-of-Scope Use

- Not suitable for generating medical, legal, or financial advice
- Not trained for multilingual sentiment analysis
- Not designed for toxicity or hate-speech detection

---

## Bias, Risks, and Limitations

- The model may reflect biases present in the training data
- Performance may vary for informal, slang-heavy, or sarcastic text
- Accuracy depends on the domain similarity to training data

### Recommendations

Users should validate predictions before using them in critical decision-making systems and consider further fine-tuning for domain-specific applications.

---

## How to Get Started

### Installation

```bash
pip install transformers peft torch


from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "archanaachu776/sentiment-analyzer"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

text = "The product quality is excellent and I am very happy."

inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)

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