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FoodBaseBERT-NER
Dizex/FoodBaseBERT-NER
FoodBaseBERT-NER is a token classification model from Dizex. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
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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From the Hugging Face model README
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)
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Languages
- en
- Created
- Oct 31, 2022
- Updated
- May 14, 2023