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