metadata

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 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/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

metric	val
f1	0.91
precision	0.89
recall	0.93