Update README.md

README.md

@@ -1,79 +1,16 @@
----
-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
+### ✅ 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
 
-```python
-from transformers import pipeline
+If you want, I can also:
+- Make it **shorter**
+- Add **accuracy numbers**
+- Customize it for **college/project submission**
+- Align it with your **BCA profile**
 
-classifier = pipeline("sentiment-analysis", model="Pavithrapn-01/sentiment-analyzer")
-result = classifier("I really enjoyed using this application!")
-print(result)
+Just tell me 😊