| --- |
| base_model: google/gemma-2b |
| 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)) |
| |
