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

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.