| --- |
| library_name: transformers |
| tags: |
| - sentiment-analysis |
| - lora |
| - roberta |
| - fine-tuned |
| - insurance |
| --- |
| |
| # Model Card for RoBERTa LoRA Fine-Tuned for Insurance Review Rating |
|
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| 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). |
|
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| ## Model Details |
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| ### Model Description |
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| 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 |
|
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| ### Model Sources |
|
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| - **Repository:** [pujpuj/roberta-lora-token-classification](https://huggingface.co/pujpuj/roberta-lora-token-classification) |
| - **Demo:** N/A |
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| --- |
|
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| ## Uses |
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| ### Direct Use |
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| 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. |
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| ### Downstream Use |
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| The model can be used as a building block for larger applications, such as customer feedback analysis, satisfaction prediction, or insurance service improvement. |
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| ### Out-of-Scope Use |
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| - 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. |
|
|
| --- |
|
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| ## Bias, Risks, and Limitations |
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| ### Bias |
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| - 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). |
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| ### Risks |
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| - Predictions might not generalize well to other domains or review styles. |
| - Inconsistent predictions may occur for ambiguous or mixed reviews. |
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| ### Recommendations |
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| - Always validate model outputs before making decisions. |
| - Use the model in conjunction with other tools for a more comprehensive analysis. |
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| --- |
|
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| ## How to Get Started with the Model |
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| You can use the model with the following code snippet: |
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|
| ```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}") |
| |
