| | --- |
| | language: en |
| | tags: |
| | - token-classification |
| | - ner |
| | - named-entity-recognition |
| | - roberta |
| | - restaurant |
| | - mit-restaurant |
| | datasets: |
| | - mit_restaurant |
| | metrics: |
| | - f1 |
| | - precision |
| | - recall |
| | - accuracy |
| | widget: |
| | - text: "I want a reservation at an italian restaurant with outdoor seating" |
| | example_title: "Restaurant query" |
| | - text: "Find me a cheap chinese place near downtown" |
| | example_title: "Restaurant search" |
| | --- |
| | |
| | # RoBERTa Large for MIT Restaurant NER |
| |
|
| | This model is a fine-tuned version of RoBERTa Large on the MIT Restaurant dataset for Named Entity Recognition (NER). |
| |
|
| | ## Model Description |
| |
|
| | - **Model type:** Token Classification (NER) |
| | - **Base model:** roberta-large |
| | - **Dataset:** MIT Restaurant NER dataset |
| | - **Languages:** English |
| | - **Task:** Named Entity Recognition for restaurant domain |
| |
|
| | ## Entity Types |
| |
|
| | The model can identify the following entity types: |
| | ['O', 'B-Amenity', 'I-Amenity', 'B-Cuisine', 'I-Cuisine', 'B-Dish', 'I-Dish', 'B-Hours', 'I-Hours', 'B-Location', 'I-Location', 'B-Price', 'I-Price', 'B-Rating', 'I-Rating', 'B-Restaurant_Name', 'I-Restaurant_Name'] |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("niruthiha/roberta-large-mit-restaurant-ner") |
| | model = AutoModelForTokenClassification.from_pretrained("niruthiha/roberta-large-mit-restaurant-ner") |
| | |
| | # Using pipeline |
| | nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
| | result = nlp("I want a reservation at an italian restaurant with outdoor seating") |
| | print(result) |
| | |
| | # Manual usage |
| | inputs = tokenizer("I want a reservation at an italian restaurant", return_tensors="pt") |
| | outputs = model(**inputs) |
| | ``` |
| |
|
| | ## Training Details |
| |
|
| | - Fine-tuned on MIT Restaurant NER dataset |
| | - Training epochs: 5 |
| | - Learning rate: 1e-5 |
| | - Batch size: 16 |
| | - Gradient accumulation steps: 2 |
| |
|
| | ## Dataset |
| |
|
| | The MIT Restaurant dataset contains restaurant-related queries with entity annotations. |
| | Dataset source: https://groups.csail.mit.edu/sls/downloads/restaurant/ |
| |
|
| | ## Performance |
| |
|
| | The model achieves good performance on restaurant domain NER tasks. Specific metrics will be updated after evaluation. |
| |
|
