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roberta-base-food-ner
carolanderson/roberta-base-food-ner
roberta-base-food-ner is a token classification model from carolanderson. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
Model for tagging mentions of food in the text of recipes. Trained by fine tuning RoBERTa base on a set of about 300 hand-labeled recipes derived from this dataset from Kaggle.. Achieves an F1 score 0f 0.96 on the cus…
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jul 6, 2023
- Updated
- Jul 6, 2023
- SHA
- b6878f8e
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Model card
From the Hugging Face model README
Model Card for Model ID carolanderson/roberta-base-food-ner
Model Details
Model Description
Model for tagging mentions of food in the text of recipes. Trained by fine tuning RoBERTa base on a set of about 300 hand-labeled recipes derived from this dataset from Kaggle.. Achieves an F1 score 0f 0.96 on the custom validation set.
- Developed by: Carol Anderson
- Shared by: Carol Anderson
- Language(s) (NLP): English
- License: MIT
- Finetuned from model: roberta-base
Model Sources
- Repository: carolmanderson/food
- Demo: food-ner
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
model = AutoModelForTokenClassification.from_pretrained('carolanderson/roberta-base-food-ner')
tokenizer = AutoTokenizer.from_pretrained("roberta-base", add_prefix_space=True)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Saute the onions in olive oil until browned."
results = nlp(example, aggregation_strategy="first")
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Languages
- en
- Created
- Jul 6, 2023
- Updated
- Jul 6, 2023