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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_lrate7.5b4 |
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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_lrate7.5b4 |
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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: 0.4079 |
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- Precisions: 0.7671 |
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- Recall: 0.7305 |
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- F-measure: 0.7455 |
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- Accuracy: 0.8904 |
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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: 7.5e-05 |
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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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| 0.7027 | 1.0 | 942 | 0.4843 | 0.8628 | 0.6861 | 0.6969 | 0.8785 | |
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| 0.3645 | 2.0 | 1884 | 0.4079 | 0.7671 | 0.7305 | 0.7455 | 0.8904 | |
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| 0.238 | 3.0 | 2826 | 0.5338 | 0.7981 | 0.7422 | 0.7486 | 0.8957 | |
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| 0.1894 | 4.0 | 3768 | 0.6026 | 0.8327 | 0.7495 | 0.7668 | 0.9049 | |
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| 0.0825 | 5.0 | 4710 | 0.5781 | 0.7825 | 0.7859 | 0.7833 | 0.9055 | |
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| 0.0523 | 6.0 | 5652 | 0.6107 | 0.8084 | 0.7591 | 0.7712 | 0.9124 | |
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| 0.0388 | 7.0 | 6594 | 0.6455 | 0.8090 | 0.7802 | 0.7894 | 0.9107 | |
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| 0.0253 | 8.0 | 7536 | 0.6711 | 0.8168 | 0.7825 | 0.7937 | 0.9135 | |
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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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