Instructions to use Dizex/FoodBaseBERT-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dizex/FoodBaseBERT-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Dizex/FoodBaseBERT-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Dizex/FoodBaseBERT-NER") model = AutoModelForTokenClassification.from_pretrained("Dizex/FoodBaseBERT-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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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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tags:
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- FoodBase
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- NER
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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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