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--- |
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license: mit |
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base_model: google-bert/bert-base-german-cased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bert-mapa-german |
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results: [] |
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language: |
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- de |
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--- |
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# bert-mapa-german |
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This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the MAPA german dataset. |
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It's purpose is to discern private information within German texts. |
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It achieves the following results on the test set: |
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| Category | Precision | Recall | F1 | Number | |
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|---------------|------------|------------|------------|--------| |
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| Address | 0.5882 | 0.6667 | 0.625 | 15 | |
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| Age | 0.0 | 0.0 | 0.0 | 3 | |
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| Amount | 1.0 | 1.0 | 1.0 | 1 | |
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| Date | 0.9455 | 0.9455 | 0.9455 | 55 | |
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| Name | 0.7 | 0.9545 | 0.8077 | 22 | |
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| Organisation | 0.5405 | 0.6452 | 0.5882 | 31 | |
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| Person | 0.5385 | 0.5 | 0.5185 | 14 | |
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| Role | 0.0 | 0.0 | 0.0 | 1 | |
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| Overall | 0.7255 | 0.7817 | 0.7525 | | |
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- Loss: 0.0325 |
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- Overall Accuracy: 0.9912 |
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## Intended uses & limitations |
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This model is engineered for the purpose of discerning private information within German texts. Its training corpus comprises only 1744 example sentences, thereby leading to a higher frequency of errors in its predictions. |
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## Training and evaluation data |
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Random split of the MAPA german dataset into 80% train, 10% valdiation and 10% test. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| No log | 1.0 | 218 | 0.0607 | 0.6527 | 0.7786 | 0.7101 | 0.9859 | |
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| No log | 2.0 | 436 | 0.0479 | 0.7355 | 0.8143 | 0.7729 | 0.9896 | |
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| 0.116 | 3.0 | 654 | 0.0414 | 0.7712 | 0.8429 | 0.8055 | 0.9908 | |
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| 0.116 | 4.0 | 872 | 0.0421 | 0.7857 | 0.8643 | 0.8231 | 0.9917 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
