Pavithrapn-01/sentiment-analyzer

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.

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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

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Model Sources

• Repository: Pavithrapn-01/sentiment-analyzer
• Base Model: google/gemma-2b

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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)

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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.

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How to Get Started with the Model

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="Pavithrapn-01/sentiment-analyzer")
result = classifier("I really enjoyed using this application!")
print(result)