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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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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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model-index: |
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- name: bert-base-german-cased-20000-ner |
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results: [] |
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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-german-cased-20000-ner |
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This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0368 |
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- Precision: 0.8221 |
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- Recall: 0.875 |
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- F1: 0.8478 |
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- Accuracy: 0.9920 |
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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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More information needed |
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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: 32 |
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- eval_batch_size: 32 |
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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: 10 |
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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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| No log | 0.1 | 64 | 0.0427 | 0.7796 | 0.8714 | 0.8229 | 0.9893 | |
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| No log | 0.19 | 128 | 0.0472 | 0.5471 | 0.85 | 0.6657 | 0.9831 | |
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| No log | 0.29 | 192 | 0.0384 | 0.7897 | 0.8179 | 0.8035 | 0.9899 | |
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| No log | 0.38 | 256 | 0.0488 | 0.4970 | 0.8786 | 0.6348 | 0.9793 | |
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| No log | 0.48 | 320 | 0.0412 | 0.7548 | 0.8464 | 0.7980 | 0.9895 | |
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| No log | 0.58 | 384 | 0.0437 | 0.8373 | 0.8821 | 0.8591 | 0.9914 | |
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| No log | 0.67 | 448 | 0.0399 | 0.7727 | 0.85 | 0.8095 | 0.9899 | |
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| 0.0914 | 0.77 | 512 | 0.0394 | 0.7859 | 0.8786 | 0.8297 | 0.9899 | |
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| 0.0914 | 0.86 | 576 | 0.0368 | 0.8221 | 0.875 | 0.8478 | 0.9920 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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