README.md

---
language: en
tags:
- ner
- named-entity-recognition
- recipes
- food
- cooking
- token-classification
license: mit
datasets:
- TASTEset
---

# Recipe NER Model

This is a Named Entity Recognition (NER) model trained to extract entities from recipe text. The model can identify food ingredients, quantities, units, cooking processes, and physical qualities from recipe instructions.

## Model Details

### Model Description
- **Model type**: Token Classification (NER)
- **Base model**: ModernBERT-base
- **Training data**: TASTEset (recipe NER dataset)
- **Number of labels**: Unknown

### Entity Types
The model recognizes the following entity types:

- **FOOD**: Food items and ingredients (e.g., "chicken breast", "olive oil")
- **QUANTITY**: Numerical quantities (e.g., "2", "1/2", "three")
- **UNIT**: Measurement units (e.g., "tablespoons", "cups", "pounds")
- **PROCESS**: Cooking processes and methods (e.g., "heat", "stir", "bake")
- **PHYSICAL_QUALITY**: Physical qualities and descriptors (e.g., "hot", "chopped", "fresh")

### Label Format
The model uses BIO (Beginning-Inside-Outside) tagging scheme:
- `B-ENTITY`: Beginning of an entity
- `I-ENTITY`: Inside/continuation of an entity
- `O`: Outside any entity (not an entity)

## Usage

### Basic Usage with Transformers

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

# Load model and tokenizer
model_name = "rgonzale/recipe-ner-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Example text
text = "Heat 2 tablespoons olive oil in a large pan"

# Tokenize
tokens = tokenizer(text, return_tensors="pt", truncation=True)

# Make prediction
with torch.no_grad():
    outputs = model(**tokens)

# Get predictions
predictions = torch.argmax(outputs.logits, dim=-1)
predicted_labels = [model.config.id2label[pred.item()] for pred in predictions[0]]

print("Tokens:", tokenizer.convert_ids_to_tokens(tokens["input_ids"][0]))
print("Labels:", predicted_labels)
```

### Usage with Pipeline

```python
from transformers import pipeline

# Load NER pipeline
ner_pipeline = pipeline("ner", model="rgonzale/recipe-ner-model", aggregation_strategy="simple")

# Example text
text = "Mix 1 cup flour with 2 eggs and bake at 350 degrees for 25 minutes"

# Get entities
entities = ner_pipeline(text)
for entity in entities:
    print(f"{entity['word']}: {entity['entity_group']} (confidence: {entity['score']:.2f})")
```

### Command Line Usage

```bash
# Quick inference
uv run python scripts/infer_ner.py --text "Heat 2 tablespoons olive oil in a large pan"

# Interactive mode
uv run python scripts/infer_ner.py --interactive
```

## Training Details

### Training Data
- **Dataset**: TASTEset - A specialized dataset for recipe NER
- **Task**: Named Entity Recognition for recipes
- **Preprocessing**: Simple tokenization with whitespace/punctuation splitting

### Training Parameters
- **Learning rate**: 3e-5
- **Batch size**: 8 (train), 16 (eval)
- **Max sequence length**: 512 tokens
- **Optimizer**: AdamW with weight decay
- **Scheduler**: Cosine annealing

## Evaluation

### Performance Metrics
The model was evaluated on the TASTEset validation set with the following results:

- **Sequence-level F1**: 0.90
- **Token-level F1**: 0.90
- **Entity-level metrics**:
  - FOOD: F1 = 0.85
  - QUANTITY: F1 = 0.97
  - UNIT: F1 = 0.97
  - PROCESS: F1 = 0.81
  - PHYSICAL_QUALITY: F1 = 0.79

### Example Predictions

```
Input: "Heat 2 tablespoons olive oil in a large pan over medium heat"

Predicted entities:
- QUANTITY: 2
- UNIT: tablespoons
- FOOD: olive oil
- PROCESS: Heat
```

## Limitations

- The model may not recognize very uncommon ingredients or measurements
- Performance is optimized for recipe text similar to the TASTEset training data
- The model uses simple tokenization and may not handle complex punctuation well

## Ethical Considerations

This model is intended for research and educational purposes. Users should be aware that:
- The model's predictions may not always be accurate
- Food-related predictions should not be used for dietary or health decisions
- The training data may contain biases present in recipe datasets

## Citation

If you use this model, please cite:

```bibtex
@misc{recipe-ner-model,
  title={Recipe NER Model},
  author={Generated by Recipe Archaeology Project},
  year={2024},
  howpublished={\url{https://huggingface.co/rgonzale/recipe-ner-model}},
}
```

## Contact

For questions or feedback about this model, please create an issue on the model's repository.