09Vaarun/sentiment-analyzer

Sentiment Analyzer (LoRA Fine-tuned Gemma-2B)

Model Summary

This repository contains a Sentiment Analysis model fine-tuned using LoRA (Low-Rank Adaptation) on top of Google’s Gemma-2B base model.
The model is designed for educational, research, and applied business analytics use cases, especially sentiment analysis of textual data such as customer feedback and social media content.

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Model Details

• Model Name: Sentiment Analyzer
• Developed by: Varun Agrawal
• Hugging Face Username: 09Vaarun
• Affiliation: IIRM Jaipur
• Model Type: Natural Language Processing (Sentiment Analysis / Text Generation)
• Base Model: google/gemma-2b
• Fine-tuning Technique: PEFT (LoRA)
• Language: English
• License: Apache 2.0

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Intended Use

✅ Direct Use

This model can be used for:

• Sentiment analysis of:
  • Customer reviews
  • Social media posts
  • Online feedback forms
  • Business and marketing text
• Academic demonstrations of:
  • Transformers
  • Parameter-Efficient Fine-Tuning (PEFT)
  • LoRA-based adaptation

🔄 Downstream Use

• Social media analytics projects
• Business intelligence dashboards
• NLP coursework and workshops
• Research experiments in sentiment analysis

❌ Out-of-Scope Use

• Medical, legal, or financial decision-making
• High-stakes automated systems without human review

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Bias, Risks, and Limitations

• The model may reflect biases present in the training data
• Performance may vary across domains and writing styles
• Not recommended for critical real-world decisions without further evaluation

Recommendations

• Perform domain-specific validation before deployment
• Use human oversight for business applications

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How to Use the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = "google/gemma-2b"
adapter_model = "09Vaarun/sentiment-analyzer"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_model)

text = "The service was excellent and the staff was very helpful."
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=50
)

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