Edit model card


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, aggregation_strategy="simple")

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"]

        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']

Performance on InstaFoodSet

metric val
f1 0.91
precision 0.89
recall 0.93
Downloads last month
Model size
124M params
Tensor type
Hosted inference API
Token Classification
This model can be loaded on the Inference API on-demand.

Dataset used to train Dizex/InstaFoodRoBERTa-NER