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
1from transformers import pipeline
23classifier = pipeline("sentiment-analysis", model="Pavithrapn-01/sentiment-analyzer")4result = classifier("I really enjoyed using this application!")5print(result)
