readme: ---
  language: en
  datasets:
  - Dizex/InstaFoodSet
  widget:
  - text: "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!"
    example_title: "Food example 1"
  - text: "Tartufo Pasta with garlic flavoured butter and olive oil, egg yolk, parmigiano and pasta water."
    example_title: "Food example 2"  
  tags:
  - Instagram
  - NER
  - Named Entity Recognition
  - Food Entity Extraction
  - Social Media
  - Informal text
  - RoBERTa
  license: mit
  ---
  # InstaFoodRoBERTa-NER
  
  ## Model description
  
  **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).
  
  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.
  
  
  ## Intended uses
  
  #### How to use
  
  You can use this model with Transformers *pipeline* for NER.
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("Dizex/InstaFoodRoBERTa-NER")
  model = AutoModelForTokenClassification.from_pretrained("Dizex/InstaFoodRoBERTa-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)
  ```
  
  ## Performance on [InstaFoodSet](https://huggingface.co/datasets/Dizex/InstaFoodSet)
  metric|val
  -|-
  f1 |0.91
  precision |0.89
  recall |0.93
  
  