readme: ---
  language: en
  datasets:
  - Dizex/FoodBase
  widget:
  - text: "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!"
    example_title: "Food example 1"
  - text: "Tartufo Pasta with garlic flavoured butter and olive oil, egg yolk, parmigiano and pasta water."
    example_title: "Food example 2"  
  tags:
  - FoodBase
  - NER
  license: mit
  ---
  # FoodBaseBERT
  
  ## Model description
  
  **FoodBaseBERT** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** of Food entities. It has been trained to recognize one entity: food (FOOD).
  
  Specifically, this model is a *bert-base-cased* model that was fine-tuned on the [FoodBase NER](https://academic.oup.com/database/article/doi/10.1093/database/baz121/5611291) dataset. 
  
  
  ## Intended uses
  
  #### How to use
  
  You can use this model with Transformers *pipeline* for NER.
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("Dizex/FoodBaseBERT")
  model = AutoModelForTokenClassification.from_pretrained("Dizex/FoodBaseBERT")
  
  pipe = pipeline("ner", model=model, tokenizer=tokenizer)
  example = "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!"
  
  ner_entity_results = pipe(example)
  print(ner_entity_results)
  ```
  