---
datasets:
- nuhuibrahim/recifinegold
language:
- en
base_model:
- FacebookAI/roberta-base
license: cc-by-nc-4.0
tags:
  - token-classification
  - named-entity-recognition
  - roberta
  - recipes
  - knowledge-augmented
  - entity-conditioned
---

# 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.

```python
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}
}
```