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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.
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