---
language:
- en
license: cc-by-nc-4.0
tags:
- token-classification
- named-entity-recognition
- bert
- roberta
- recipes
- knowledge-augmented
pretty_name: ReciFineGold
size_categories:
- 1K<n<10K
task_categories:
- token-classification
---

# ReciFineGold Dataset
ReciFineGold is a finely annotated recipe dataset designed to support research in **controllable recipe generation, structured recipe understanding, and information extraction from procedural cooking text**. The dataset contains step-by-step cooking instructions drawn from 500 real-world recipes, each **manually annotated** with span-level semantic labels that capture the key components of culinary actions, ingredients, tools, and states.

Each recipe in the dataset consists of:

- A unique recipe identifier (`id`)  
- A natural language cooking procedure (`text`)  
- A list of character-level annotations (`label`), where each annotation specifies:  
  - the start and end offsets of a text span, and  
  - its corresponding semantic entity type  

## Performance of Encoder Models Trained on The ReciFineGold Dataset

<!-- |Model Type| Task Formulation    | Knowledge Type           | Precision (%) | Recall (%) | F1 (%) |
|----------|---------------------|--------------------------|---------------|------------|--------|
|          | Traditional         |           ---            | 89.51         | 90.26      | 89.88  |
|          |---------------------|--------------------------|---------------|------------|--------|
|          |                     | ETN                      | 95.41         | 94.37      | 94.88  | 
|  BERT    | Knowledge Augmented | QTN                      | 94.99         | 94.80      | 94.89  | 
|          |                     | DTN                      | 94.65         | 93.88      | 94.26  | 
|          |                     | EXT                      | 95.13         | 93.84      | 94.48  | 
|          |                     | CBD                      | 94.62         | 94.44      | 94.53  | 
|----------|---------------------|--------------------------|---------------|------------|--------|
|          | Traditional         |           ---            | 88.76         | 89.53      | 89.14  |
|          |---------------------|--------------------------|---------------|------------|--------|
|          |                     | ETN                      | 96.49         | 96.02      | 96.25  | 
| RoBERTa  | Knowledge Augmented | QTN                      | 96.62         | 96.15      | 96.39  | 
|          |                     | DTN                      | 96.27         | 95.92      | 96.09  | 
|          |                     | EXT                      | 96.59         | 96.08      | 96.34  | 
|          |                     | CBD                      | 90.54         | 89.33      | 89.93  | 
|----------|---------------------|--------------------------|---------------|------------|--------| -->


![Screenshot 2026-01-29 at 13.48.41](https://cdn-uploads.huggingface.co/production/uploads/6725049d93b72c9eaf7e075e/Pc4uuOpiuQEM7R387hjf5.png)

## Entity Types
The dataset defines the following semantic entity types that were adopted from the [Recipe Case Study Paper](https://www.dl.soc.i.kyoto-u.ac.jp/~tajima/papers/hmdata18yamakatawww.pdf), each representing a different aspects of the cooking process:
| 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*) |

## How to use models trained on this dataset
### Option 1 - Using the Knowledge Augmented and Entity Type-Specific (KAES) Models
The ReciFine library provides a lightweight inference wrapper (`ReciFineNER`) that handles the KAES prompting and decoding.

```python
# Install ReciFine (see repo for latest install instructions)
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", # recipebert, reciperoberta
    task_formulation="knowledge_guided",
    knowledge_type="question", # question, entity_type, example, definition, all
)

text = "Add 2 cups of chopped onions and fry until golden."
prediction = ner.process_text(
    text,
    entity_type="FOOD" # ACTION_BY_CHEF, ACTION_BY_CHEF_DISCONTINUOUS, ACTION_BY_FOOD, ACTION_BY_TOOL, DURATION, FOOD, FOOD_STATE, QUANTITY, TOOL, TOOL_STATE
)

print(prediction)
```

### Option 2 - Using the Traditional BIO-n NER
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="recipebert", # recipebert, 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

## 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}
}
```