README.md

metadata

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 dataset.

Intended uses

How to use

You can use this model with Transformers pipeline for NER.

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)