09Vaarun
/

sentiment-analyzer

@@ -3,205 +3,100 @@ base_model: google/gemma-2b
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:google/gemma-2b
 - lora
 - transformers
 ---
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-- PEFT 0.18.1

 library_name: peft
 pipeline_tag: text-generation
 tags:
+- sentiment-analysis
+- nlp
 - lora
+- peft
 - transformers
+- business-analytics
+- social-media-analytics
 ---
+# 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.
+---
 ## 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
+---
+## 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
+---
 ## 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
+---
+## How to Use the Model
+```python
+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))