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--- |
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language: en |
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datasets: |
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- Dizex/InstaFoodSet |
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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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- Instagram |
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- NER |
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- Named Entity Recognition |
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- Food Entity Extraction |
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- Social Media |
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- Informal text |
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- RoBERTa |
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license: mit |
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--- |
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# InstaFoodRoBERTa-NER |
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## Model description |
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**InstaFoodRoBERTa-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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Specifically, this model is a *roberta-base* model that was fine-tuned on a dataset consisting of 400 English Instagram posts related to food. The [dataset](https://huggingface.co/datasets/Dizex/InstaFoodSet) is open source. |
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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/InstaFoodRoBERTa-NER") |
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model = AutoModelForTokenClassification.from_pretrained("Dizex/InstaFoodRoBERTa-NER") |
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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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## Performance on [InstaFoodSet](https://huggingface.co/datasets/Dizex/InstaFoodSet) |
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metric|val |
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-|- |
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f1 |0.91 |
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precision |0.89 |
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recall |0.93 |
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