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--- |
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language: |
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- de |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- germeval_14 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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widget: |
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- text: Mein Name ist Wolfgang und ich lebe in Berlin |
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example_title: Example 1 |
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- text: Mein Name ist Sarah und ich lebe in London |
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example_title: Example 2 |
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- text: Mein Name ist Clara und ich lebe in Berkeley, California. |
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example_title: Example 3 |
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base_model: bert-base-uncased |
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model-index: |
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- name: bert-base-uncased-de-ner |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: germeval_14 |
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type: germeval_14 |
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config: germeval_14 |
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split: test |
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args: germeval_14 |
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metrics: |
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- type: precision |
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value: 0.8109431552054502 |
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name: Precision |
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- type: recall |
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value: 0.771990271584921 |
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name: Recall |
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- type: f1 |
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value: 0.7909874364032811 |
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name: F1 |
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- type: accuracy |
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value: 0.9786213727432309 |
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name: Accuracy |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-uncased-de-ner |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the germeval_14 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1374 |
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- Precision: 0.8109 |
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- Recall: 0.7720 |
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- F1: 0.7910 |
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- Accuracy: 0.9786 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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The model was trained on data that follows the [`IOB`](<https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)>) convention. Full tagset with indices: |
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```python |
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{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6} |
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``` |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 0 |
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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: 6 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.104 | 1.0 | 3000 | 0.0973 | 0.7027 | 0.7323 | 0.7172 | 0.9712 | |
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| 0.0597 | 2.0 | 6000 | 0.0942 | 0.8135 | 0.7172 | 0.7623 | 0.9766 | |
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| 0.0345 | 3.0 | 9000 | 0.1051 | 0.7924 | 0.7569 | 0.7742 | 0.9773 | |
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| 0.0172 | 4.0 | 12000 | 0.1170 | 0.8074 | 0.7628 | 0.7844 | 0.9779 | |
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| 0.0092 | 5.0 | 15000 | 0.1264 | 0.8068 | 0.7803 | 0.7933 | 0.9788 | |
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| 0.0035 | 6.0 | 18000 | 0.1374 | 0.8109 | 0.7720 | 0.7910 | 0.9786 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
