README.md

---
language:
- en

pipeline_tag: text-classification

tags:
- sentiment-analysis
- text-classification
- opinion-mining
- emotion-detection
- nlp
- natural-language-processing
- transformers
- peft
- lora
- adapter
- fine-tuning
- gemma
- gemma-2b
- base_model:google/gemma-2b
- base_model:adapter:google/gemma-2b

library_name: peft

base_model: google/gemma-2b

license: apache-2.0

model-index:
- name: Sentiment Analyzer (LoRA Gemma-2B)
  results:
  - task:
      type: text-classification
      name: Sentiment Analysis
    metrics:
    - type: accuracy
      value: not-reported
---

# Sentiment Analyzer (LoRA Fine-Tuned Gemma-2B)

## Model Overview

**Sentiment Analyzer** is a **LoRA fine-tuned Gemma-2B transformer model** for **sentiment analysis and text classification** tasks.  
It uses **PEFT (Parameter-Efficient Fine-Tuning)** to deliver strong performance while keeping memory and compute requirements low.

This model is well-suited for:
- Sentiment analysis
- Opinion mining
- Review classification
- Emotion-aware text generation
- Lightweight NLP deployments

---

## Tasks

- Text Classification  
- Sentiment Analysis  

---

## Model Details

- **Developed by:** `mysmmurf12`
- **Shared by:** `mysmmurf12`
- **Model type:** Transformer-based Language Model
- **Base model:** `google/gemma-2b`
- **Fine-tuning method:** LoRA (Low-Rank Adaptation)
- **Library:** PEFT + Transformers
- **Language:** English
- **License:** Apache 2.0 (inherits from base model)

---

## Model Sources

- **Hugging Face Repository:**  
  https://huggingface.co/mysmmurf12/sentiment-analyzer

- **Base Model:**  
  https://huggingface.co/google/gemma-2b

---

## Intended Uses

### ✅ Direct Use

- Sentiment classification (positive / negative / neutral)
- Customer feedback and review analysis
- Social media sentiment monitoring
- Sentiment-aware chatbots

### 🔁 Downstream Use

- Integration into RAG pipelines
- Domain-specific sentiment fine-tuning
- Deployment via APIs, Streamlit apps, or dashboards

### 🚫 Out-of-Scope Use

- Medical, legal, or financial decision-making
- High-risk automated moderation
- Multilingual sentiment tasks (English-focused)

---

## Bias, Risks, and Limitations

- May reflect biases present in training data
- Less reliable on sarcasm or ambiguous language
- Not evaluated on standardized sentiment benchmarks

**Recommendation:**  
Use human validation for high-impact applications.

---

## How to Use the Model

### Load with Transformers + PEFT

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "google/gemma-2b"
adapter_model = "mysmmurf12/sentiment-analyzer"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)

model = PeftModel.from_pretrained(model, adapter_model)

text = "The product quality is amazing!"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    temperature=0.7
)

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