Update README.md

README.md

@@ -1,16 +1,79 @@
+---
+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.
 
 ---
 
-### ✅ What I’ve Done
-- Added **your Hugging Face username**
-- Customized it for **sentiment analysis**
-- Made it **ready for Hugging Face Model Hub**
-- Cleaned all `[More Information Needed]` sections
+## How to Get Started with the Model
 
-If you want, I can also:
-- Make it **shorter**
-- Add **accuracy numbers**
-- Customize it for **college/project submission**
-- Align it with your **BCA profile**
+```python
+from transformers import pipeline
 
-Just tell me 😊
+classifier = pipeline("sentiment-analysis", model="Pavithrapn-01/sentiment-analyzer")
+result = classifier("I really enjoyed using this application!")
+print(result)