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InstaFoodBERT-NER
Dizex/InstaFoodBERT-NER
InstaFoodBERT-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.
InstaFoodBERT-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition of Food entities on informal text (social media like). It has been trained to recognize a single entity: food (FOOD).
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Nov 6, 2022
- Updated
- Nov 17, 2022
- SHA
- 2aa39687
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Datasets
Model card
From the Hugging Face model README
InstaFoodBERT-NER
Model description
InstaFoodBERT-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition of Food entities on informal text (social media like). It has been trained to recognize a single entity: food (FOOD).
Specifically, this model is a bert-base-cased model that was fine-tuned on a dataset consisting of 400 English Instagram posts related to food. The dataset is open source.
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/InstaFoodBERT-NER")
model = AutoModelForTokenClassification.from_pretrained("Dizex/InstaFoodBERT-NER")
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
- Nov 6, 2022
- Updated
- Nov 17, 2022