Create README.md

README.md

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+---
+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
+