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--- |
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license: mit |
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base_model: pdelobelle/robbert-v2-dutch-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- recall |
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- accuracy |
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model-index: |
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- name: robbert0410_lrate10b4 |
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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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# robbert0410_lrate10b4 |
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This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6818 |
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- Precisions: 0.7943 |
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- Recall: 0.7761 |
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- F-measure: 0.7846 |
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- Accuracy: 0.9080 |
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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: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
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| 0.6936 | 1.0 | 942 | 0.5273 | 0.8577 | 0.7062 | 0.7069 | 0.8731 | |
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| 0.4407 | 2.0 | 1884 | 0.4780 | 0.7487 | 0.7080 | 0.7142 | 0.8898 | |
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| 0.3023 | 3.0 | 2826 | 0.5526 | 0.7743 | 0.7209 | 0.7150 | 0.8904 | |
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| 0.2057 | 4.0 | 3768 | 0.5627 | 0.7815 | 0.7405 | 0.7559 | 0.8998 | |
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| 0.1333 | 5.0 | 4710 | 0.5509 | 0.7959 | 0.7521 | 0.7680 | 0.9010 | |
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| 0.0896 | 6.0 | 5652 | 0.6215 | 0.7844 | 0.7583 | 0.7699 | 0.9053 | |
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| 0.0538 | 7.0 | 6594 | 0.6694 | 0.7851 | 0.7723 | 0.7766 | 0.9025 | |
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| 0.0316 | 8.0 | 7536 | 0.6818 | 0.7943 | 0.7761 | 0.7846 | 0.9080 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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