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.

bibtex

@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.

bibtex

@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}
}

nuhuibrahim/recifinegold-reciperoberta-trad

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

What this checkpoint is

How to use