---
license: mit
base_model: google-bert/bert-base-german-cased
tags:
- generated_from_trainer
model-index:
- name: bert-mapa-german
  results: []
language:
- de
---

# bert-mapa-german

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.
It's purpose is to discern private information within German texts.

It achieves the following results on the test set:

| Category      | Precision  | Recall     | F1         | Number |
|---------------|------------|------------|------------|--------|
| Address       | 0.5882     | 0.6667     | 0.625      | 15     |
| Age           | 0.0        | 0.0        | 0.0        | 3      |
| Amount        | 1.0        | 1.0        | 1.0        | 1      |
| Date          | 0.9455     | 0.9455     | 0.9455     | 55     |
| Name          | 0.7        | 0.9545     | 0.8077     | 22     |
| Organisation  | 0.5405     | 0.6452     | 0.5882     | 31     |
| Person        | 0.5385     | 0.5        | 0.5185     | 14     |
| Role          | 0.0        | 0.0        | 0.0        | 1      |
| Overall       | 0.7255     | 0.7817     | 0.7525     |        |

- Loss: 0.0325
- Overall Accuracy: 0.9912


## Intended uses & limitations

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.

## Training and evaluation data

Random split of the MAPA german dataset into 80% train, 10% valdiation and 10% test.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log        | 1.0   | 218  | 0.0607          | 0.6527            | 0.7786         | 0.7101     | 0.9859           |
| No log        | 2.0   | 436  | 0.0479          | 0.7355            | 0.8143         | 0.7729     | 0.9896           |
| 0.116         | 3.0   | 654  | 0.0414          | 0.7712            | 0.8429         | 0.8055     | 0.9908           |
| 0.116         | 4.0   | 872  | 0.0421          | 0.7857            | 0.8643         | 0.8231     | 0.9917           |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1