| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| datasets: |
| - conll2003 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| base_model: dbmdz/bert-base-german-cased |
| model-index: |
| - name: bert-german-ner |
| results: |
| - task: |
| type: token-classification |
| name: Token Classification |
| dataset: |
| name: conll2003 |
| type: conll2003 |
| config: conll2003 |
| split: validation |
| args: conll2003 |
| metrics: |
| - type: precision |
| value: 0.8333588604686782 |
| name: Precision |
| - type: recall |
| value: 0.8620088719898605 |
| name: Recall |
| - type: f1 |
| value: 0.8474417880227396 |
| name: F1 |
| - type: accuracy |
| value: 0.9292245320451997 |
| name: Accuracy |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # bert-german-ner |
|
|
| This model is a fine-tuned version of [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased) on the conll2003 dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3196 |
| - Precision: 0.8334 |
| - Recall: 0.8620 |
| - F1: 0.8474 |
| - Accuracy: 0.9292 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 8 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 300 | 0.3617 | 0.7310 | 0.7733 | 0.7516 | 0.8908 | |
| | 0.5428 | 2.0 | 600 | 0.2897 | 0.7789 | 0.8395 | 0.8081 | 0.9132 | |
| | 0.5428 | 3.0 | 900 | 0.2805 | 0.8147 | 0.8465 | 0.8303 | 0.9221 | |
| | 0.2019 | 4.0 | 1200 | 0.2816 | 0.8259 | 0.8498 | 0.8377 | 0.9260 | |
| | 0.1215 | 5.0 | 1500 | 0.2942 | 0.8332 | 0.8599 | 0.8463 | 0.9285 | |
| | 0.1215 | 6.0 | 1800 | 0.3053 | 0.8293 | 0.8619 | 0.8452 | 0.9287 | |
| | 0.0814 | 7.0 | 2100 | 0.3190 | 0.8249 | 0.8634 | 0.8437 | 0.9267 | |
| | 0.0814 | 8.0 | 2400 | 0.3196 | 0.8334 | 0.8620 | 0.8474 | 0.9292 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.26.0 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
