recifinegold-recipebert-trad

ReciFineGold RecipeBERT (Traditional Token Classification) recifinegold-recipebert-trad

This model is a BERT-base-uncased token-classification model trained on ReciFineGold for recipe-focused Named Entity Recognition (NER).

What this checkpoint is

• Backbone: BERT-base-uncased
• Task: Token classification (BIO-style tagging)

How to use

The ReciFine library provides a lightweight inference wrapper (ReciFineNER) that handles extraction and decoding.

pip install https://github.com/nuhu-ibrahim/ReciFine/archive/refs/tags/V1.zip

from recifine.inferencing.inference import ReciFineNER

ner = ReciFineNER.from_pretrained(
    model="recipebert",
    task_formulation="traditional",
)

text = "Add 2 cups of chopped onions and fry until golden ."

prediction = ner.process_text(
    text
)

print(prediction)

Quick links (documentation + notebook)

• ReciFine library (recommended): training scripts, inference wrappers, and full documentation
  https://github.com/nuhu-ibrahim/ReciFine/tree/main

• Colab (end-to-end usage demo):
  https://colab.research.google.com/drive/1CatH2YOhnOWf-VglprEgxONWRkrXRBXo?usp=sharing&authuser=1

Intended use

Use this model to extract fine-grained recipe entities from procedural recipe text (e.g., instructions), including ingredients, tools, quantities, durations, actions, and state descriptors.

Typical applications:

• Structured parsing of recipe steps
• Ingredient and action extraction for downstream cooking assistants
• Data normalisation and indexing for recipe search
• Entity-aware prompting and evaluation pipelines for recipe generation

Supported entity types

Research Papers

Knowledge-Augmented and Entity Type-Specific Token Classification

The knowledge-augmented and entity type-specific token classification model architecture is described in the paper Knowledge Augmentation Enhances Token Classification for Recipe Understanding.

@inproceedings{
  title     = {Knowledge Augmentation Enhances Token Classification for Recipe Understanding},
  author    = {Ibrahim, Nuhu and Stevens, Robert and Batista-Navarro, Riza},
  booktitle = {EACL},
  year      = {2026}
}

ReciFine Datasets and Controllable Recipe Generation

The ReciFine, ReciFineGold and ReciFineGen datasets are described in the paper ReciFine: Finely Annotated Recipe Dataset for Controllable Recipe Generation.

@inproceedings{
  title   = {ReciFine: Finely Annotated Recipe Dataset for Controllable Recipe Generation},
  author  = {Ibrahim, Nuhu and Ravikumar, Rishi and Stevens, Robert and Batista-Navarro, Riza},
  booktitle = {EACL},
  year    = {2026}
}