---
license: mit
language:
- en
library_name: transformers
---
# 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.](https://www.kaggle.com/hugodarwood/epirecipes). 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](https://huggingface.co/roberta-base)

### Model Sources
- **Repository:** [carolmanderson/food](https://github.com/carolmanderson/food/tree/master)
- **Demo:** [food-ner](https://huggingface.co/spaces/carolanderson/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")

```