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--- |
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language: en |
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datasets: |
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- Dizex/FoodBase |
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widget: |
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- text: "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!" |
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example_title: "Food example 1" |
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- text: "Tartufo Pasta with garlic flavoured butter and olive oil, egg yolk, parmigiano and pasta water." |
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example_title: "Food example 2" |
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tags: |
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- FoodBase |
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- NER |
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license: mit |
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--- |
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# FoiodBaseBERT |
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## Model description |
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**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). |
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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. |
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## Intended uses |
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#### How to use |
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You can use this model with Transformers *pipeline* for NER. |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("Dizex/FoodBaseBERT") |
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model = AutoModelForTokenClassification.from_pretrained("Dizex/FoodBaseBERT") |
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pipe = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!" |
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ner_entity_results = pipe(example) |
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print(ner_entity_results) |
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``` |
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