| --- |
| library_name: transformers |
| pipeline_tag: text-classification |
| language: en |
| tags: |
| - sentiment-analysis |
| - roberta |
| - restaurant-reviews |
| --- |
| |
| # Restaurant Review RoBERTa Classifier |
|
|
| ## Model Description |
| This is a fine-tuned version of `roberta-base` trained specifically to analyze the sentiment of restaurant and hospitality reviews. It was trained to understand the specific nuances, slang, and context of food service feedback. |
|
|
| - **Developed by:** Almashtouly |
| - **Model type:** RoBERTa Sequence Classification |
| - **Language:** English |
| - **License:** MIT |
|
|
| ## Uses |
| This model is intended for analyzing customer feedback in the restaurant and hospitality industry. |
|
|
| ### Direct Use |
| Pass raw text reviews into the model to classify them into three categories: |
| * **LABEL_0:** Negative (e.g., "The steak was completely undercooked.") |
| * **LABEL_1:** Neutral (e.g., "The food was okay, nothing special.") |
| * **LABEL_2:** Positive (e.g., "The ambiance and service were absolutely incredible!") |
| |
| ### How to Get Started with the Model |
| You can easily use this model in your own applications via the Hugging Face pipeline: |
| ```python |
| from transformers import pipeline |
| |
| # Load the model |
| analyzer = pipeline("text-classification", model="Almashtouly/Restaurant_RoBERTa_Model") |
| |
| # Test a review |
| prediction = analyzer("The waitstaff was incredibly friendly, but the food took way too long.") |
| print(prediction) |
