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---
license: openrail
language:
- de
metrics:
- f1
- accuracy
- precision
- recall
pipeline_tag: token-classification
tags:
- recipe
- cooking
- entity_recognition
---
Weakly supervised token classification model for German recipe texts based on bert-base-german-cased.
Code available: https://github.com/chefkoch24/weak-ingredient-recognition
Dataset: https://www.kaggle.com/datasets/sterby/german-recipes-dataset
Recognizes the following entities:
'O': 0, <br>
'B-INGREDIENT': 1,<br>
'I-INGREDIENT': 2,<br>
'B-UNIT': 3,<br>
'I-UNIT': 4,<br>
'B-QUANTITY': 5,<br>
'I-QUANTITY': 6<br>
**Training:**
epochs: 2<br>
optimizer: Adam<br>
learning rate: 2e-5<br>
max length: 512<br>
recipes: 7801<br>
The model was trained on single Geforce RTX2080 with 11GB GPU
**Metrics on test set (weakly supervised)**:
accuracy_token 0.9965656995773315<br>
f1_token 0.9965656995773315<br>
precision_token 0.9965656995773315<br>
recall_token 0.9965656995773315<br> |