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--- |
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language: en |
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datasets: |
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- Dizex/InstaFoodSet |
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widget: |
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- text: "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!" |
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example_title: "Food example 1" |
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- text: "Tartufo Pasta with garlic flavoured butter and olive oil, egg yolk, parmigiano and pasta water." |
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example_title: "Food example 2" |
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tags: |
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- Instagram |
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- NER |
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- Named Entity Recognition |
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- Food Entity Extraction |
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- Social Media |
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- Informal text |
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- RoBERTa |
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license: mit |
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--- |
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# InstaFoodRoBERTa-NER |
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## Model description |
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**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). |
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Specifically, this model is a [*roberta-base*](https://huggingface.co/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. |
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## Intended uses |
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#### How to use |
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You can use this model with Transformers *pipeline* for NER. |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("Dizex/InstaFoodRoBERTa-NER") |
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model = AutoModelForTokenClassification.from_pretrained("Dizex/InstaFoodRoBERTa-NER") |
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pipe = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!" |
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ner_entity_results = pipe(example, aggregation_strategy="simple") |
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print(ner_entity_results) |
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``` |
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To get the extracted food entities as strings you can use the following code: |
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```python |
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def convert_entities_to_list(text, entities: list[dict]) -> list[str]: |
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ents = [] |
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for ent in entities: |
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e = {"start": ent["start"], "end": ent["end"], "label": ent["entity_group"]} |
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if ents and -1 <= ent["start"] - ents[-1]["end"] <= 1 and ents[-1]["label"] == e["label"]: |
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ents[-1]["end"] = e["end"] |
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continue |
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ents.append(e) |
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return [text[e["start"]:e["end"]] for e in ents] |
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print(convert_entities_to_list(example, ner_entity_results)) |
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``` |
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This will result in the following output: |
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```python |
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['olive poké bowl', 'chia seeds'] |
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``` |
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## Performance on [InstaFoodSet](https://huggingface.co/datasets/Dizex/InstaFoodSet) |
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metric|val |
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-|- |
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f1 |0.91 |
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precision |0.89 |
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recall |0.93 |
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