Instafoodroberta NER

🚀 InstaFoodRoBERTa-NER

InstaFoodRoBERTa-NER is a fine - tuned BERT model designed for Named Entity Recognition of food entities in social media informal text.

🚀 Quick Start

You can use this model with Transformers pipeline for NER.

Basic Usage

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, aggregation_strategy="simple")
print(ner_entity_results)

Advanced Usage

To get the extracted food entities as strings you can use the following code:

def convert_entities_to_list(text, entities: list[dict]) -> list[str]:
        ents = []
        for ent in entities:
            e = {"start": ent["start"], "end": ent["end"], "label": ent["entity_group"]}
            if ents and -1 <= ent["start"] - ents[-1]["end"] <= 1 and ents[-1]["label"] == e["label"]:
                ents[-1]["end"] = e["end"]
                continue
            ents.append(e)

        return [text[e["start"]:e["end"]] for e in ents]

print(convert_entities_to_list(example, ner_entity_results))

This will result in the following output:

['olive poké bowl', 'chia seeds']

✨ Features

• Named Entity Recognition: Specifically designed for recognizing food entities in social media informal text.
• Single Entity Focus: Trained to recognize a single entity type: food (FOOD).
• Fine - Tuned Model: Based on the roberta - base model and fine - tuned on a relevant dataset.

📚 Documentation

Model description

InstaFoodRoBERTa-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition of Food entities on social media like informal text (e.g. Instagram, X, Reddit). 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

You can use this model with Transformers pipeline for NER.

Performance on InstaFoodSet

📄 License

This project is licensed under the MIT license.