pujpuj
/

roberta-lora-token-classification

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 ---
 library_name: transformers
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 ---
 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
+---
 ## 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}")