metadata
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,
'B-INGREDIENT': 1,
'I-INGREDIENT': 2,
'B-UNIT': 3,
'I-UNIT': 4,
'B-QUANTITY': 5,
'I-QUANTITY': 6
Training:
epochs: 2
optimizer: Adam
learning rate: 2e-5
max length: 512
recipes: 7801
The model was trained on single Geforce RTX2080 with 11GB GPU
Metrics on test set (weakly supervised):
accuracy_token 0.9965656995773315
f1_token 0.9965656995773315
precision_token 0.9965656995773315
recall_token 0.9965656995773315