ReciFineGold RecipeRoBERTa (Traditional Token Classification) recifinegold-reciperoberta-trad

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

What this checkpoint is

• Backbone: RoBERTa-base
• 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="reciperoberta",
    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

Entity Type	Definition
FOOD	Edible items, including both raw ingredients and intermediate products
TOOL	Cooking tools such as knives, bowls, pans
DURATION	Time durations in cooking (e.g., 20 minutes)
QUANTITY	Quantities associated with ingredients
ACTION_BY_CHEF	Verbs for deliberate cook actions (e.g., bring in “Bring the mixture to a boil”)
ACTION_BY_CHEF_DISCONTINUOUS	Non-contiguous parts of compound chef actions (e.g., to a boil)
ACTION_BY_FOOD	Verbs where food is the agent (e.g., melt, boil)
ACTION_BY_TOOL	Verbs denoting tool actions (e.g., grind, beat)
FOOD_STATE	Descriptions of food’s state (e.g., chopped, soft)
TOOL_STATE	Descriptions of tool readiness (e.g., preheated, greased, covered)

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}
}