---
language:
- en
library_name: transformers
pipeline_tag: token-classification
tags:
- ner
- recipes
- ingredients
- bert
metrics:
- precision
- recall
- f1
---

# Recipe NER

A compact BERT token-classification model trained from scratch on BIO-tagged
English recipe ingredient lines. Its WordPiece tokenizer is trained on the same
training split, so the repository is self-contained.

## Entity labels

- `NAME`: ingredient name
- `QTY`: quantity
- `UNIT`: unit of measure
- `COMMENT`: preparation/state comment
- `RANGE_END`: upper end of a range
- `INDEX`: rare source-dataset label

The source label `OTHER` is normalized to the standard outside label `O`.

## Test metrics

The split is deterministic: 39,878 train / 4,985
validation / 4,985 test examples (seed 42).

| Metric | Value |
|---|---:|
| Entity precision | 0.8715 |
| Entity recall | 0.8820 |
| Entity F1 | 0.8767 |
| Token accuracy | 0.8923 |

## Usage

```python
from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="fastyBOOM/recipe-ner",
    aggregation_strategy="simple",
)

recipe = '3 large eggs, lightly beaten'
entities = ner(recipe)
for entity in entities:
    print(entity["entity_group"], entity["word"], entity["start"], entity["end"], entity["score"])
```

Example produced during the final smoke test:

```json
[
  {
    "entity_group": "QTY",
    "score": 0.997479,
    "word": "3",
    "start": 0,
    "end": 1
  },
  {
    "entity_group": "COMMENT",
    "score": 0.949855,
    "word": "large",
    "start": 2,
    "end": 7
  },
  {
    "entity_group": "NAME",
    "score": 0.937473,
    "word": "eggs",
    "start": 8,
    "end": 12
  },
  {
    "entity_group": "COMMENT",
    "score": 0.945924,
    "word": ", lightly beaten",
    "start": 12,
    "end": 28
  }
]
```

The model accepts raw recipe text. `start` and `end` offsets can be used to
highlight each detected entity in the original input.

## Reproducibility

`recipe_ner_training.ipynb` contains the commented workflow. The standalone
`train_recipe_ner.py` script exposes the same parameters for command-line runs.
The exact training settings and label mapping are stored in
`training_config.json` and `label_schema.json`.

## Limitations

The training corpus is English and mostly contains short ingredient lines.
Results on Russian text, long cooking instructions, or unrelated domains are not
validated. The very rare `INDEX` and `RANGE_END` classes have limited support.