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--- |
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library_name: transformers |
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license: mit |
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base_model: bert-base-german-cased |
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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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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: ner_model |
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results: |
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- task: |
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name: Token Classification |
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type: 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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- name: Accuracy |
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type: accuracy |
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value: 0.9703112769708805 |
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- name: F1 |
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type: f1 |
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value: 0.8348410033576931 |
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- name: Precision |
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type: precision |
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value: 0.8311310366525091 |
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- name: Recall |
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type: recall |
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value: 0.8385842393460836 |
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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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# ner_model |
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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 germeval_14 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1446 |
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- Accuracy: 0.9703 |
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- F1: 0.8348 |
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- Precision: 0.8311 |
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- Recall: 0.8386 |
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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: 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.1274 | 1.0 | 3000 | 0.1132 | 0.9671 | 0.8144 | 0.8031 | 0.8260 | |
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| 0.065 | 2.0 | 6000 | 0.1382 | 0.9690 | 0.8301 | 0.8452 | 0.8155 | |
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| 0.0365 | 3.0 | 9000 | 0.1446 | 0.9703 | 0.8348 | 0.8311 | 0.8386 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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