This model is a fine-tuned version of RoBERTa Large on the MIT Restaurant dataset for Named Entity Recognition (NER).
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']
1from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
2
3tokenizer = AutoTokenizer.from_pretrained("niruthiha/roberta-large-mit-restaurant-ner")
4model = AutoModelForTokenClassification.from_pretrained("niruthiha/roberta-large-mit-restaurant-ner")
5
6# Using pipeline
7nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
8result = nlp("I want a reservation at an italian restaurant with outdoor seating")
9print(result)
10
11# Manual usage
12inputs = tokenizer("I want a reservation at an italian restaurant", return_tensors="pt")
13outputs = model(**inputs)
The MIT Restaurant dataset contains restaurant-related queries with entity annotations.
Dataset source:
https://groups.csail.mit.edu/sls/downloads/restaurant/The model achieves good performance on restaurant domain NER tasks. Specific metrics will be updated after evaluation.
