---
library_name: transformers
tags:
  - sentiment-analysis
  - lora
  - roberta
  - fine-tuned
  - insurance
---

# Model Card for RoBERTa LoRA Fine-Tuned for Insurance Review Rating

This model is a fine-tuned version of RoBERTa (`roberta-large`) using LoRA adapters. It is specifically designed to classify English insurance reviews and assign a rating (on a scale of 1 to 5).

## Model Details

### Model Description

This model uses RoBERTa (`roberta-large`) as its base architecture and was fine-tuned using Low-Rank Adaptation (LoRA) to adapt efficiently to the task of insurance review classification. The model predicts a rating from 1 to 5 based on the sentiment and context of a given review. LoRA fine-tuning reduces memory overhead and enables faster training compared to full fine-tuning.

- **Developed by:** Lapujpuj
- **Finetuned from model:** RoBERTa (`roberta-large`)
- **Language(s) (NLP):** English
- **License:** Apache-2.0
- **LoRA Configuration:** 
  - Rank (r): 2
  - LoRA Alpha: 16
  - LoRA Dropout: 0.1
- **Task:** Sentiment-based rating prediction for insurance reviews

### Model Sources

- **Repository:** [pujpuj/roberta-lora-token-classification](https://huggingface.co/pujpuj/roberta-lora-token-classification)
- **Demo:** N/A

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## Uses

### Direct Use

This model can be directly used to assign a sentiment-based rating to insurance reviews. Input text is expected to be a sentence or paragraph in English.

### Downstream Use

The model can be used as a building block for larger applications, such as customer feedback analysis, satisfaction prediction, or insurance service improvement.

### Out-of-Scope Use

- The model is not designed for reviews in languages other than English.
- It may not generalize well to domains outside of insurance-related reviews.
- Avoid using the model for biased or malicious predictions.

---

## Bias, Risks, and Limitations

### Bias

- The model is trained on a specific dataset of insurance reviews, which might include biases present in the training data (e.g., skewed ratings, linguistic or cultural biases).

### Risks

- Predictions might not generalize well to other domains or review styles.
- Inconsistent predictions may occur for ambiguous or mixed reviews.

### Recommendations

- Always validate model outputs before making decisions.
- Use the model in conjunction with other tools for a more comprehensive analysis.

---

## How to Get Started with the Model

You can use the model with the following code snippet:

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

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("roberta-large")
base_model = AutoModelForSequenceClassification.from_pretrained("roberta-large", num_labels=5)
model = PeftModel.from_pretrained(base_model, "pujpuj/roberta-lora-token-classification")

# Example prediction
review = "The insurance service was quick and reliable."
inputs = tokenizer(review, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
rating = torch.argmax(outputs.logits, dim=1).item() + 1
print(f"Predicted rating: {rating}")