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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_lrate10b8 |
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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_lrate10b8 |
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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: 0.6067 |
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- Precisions: 0.8082 |
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- Recall: 0.7813 |
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- F-measure: 0.7929 |
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- Accuracy: 0.9106 |
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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: 8 |
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- eval_batch_size: 8 |
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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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| 0.6356 | 1.0 | 471 | 0.4207 | 0.8357 | 0.6959 | 0.6907 | 0.8767 | |
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| 0.3636 | 2.0 | 942 | 0.3759 | 0.7587 | 0.7486 | 0.7497 | 0.8938 | |
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| 0.2131 | 3.0 | 1413 | 0.4114 | 0.8027 | 0.7381 | 0.7548 | 0.8966 | |
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| 0.1356 | 4.0 | 1884 | 0.4721 | 0.8141 | 0.7498 | 0.7682 | 0.9015 | |
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| 0.0768 | 5.0 | 2355 | 0.5470 | 0.7628 | 0.7637 | 0.7575 | 0.8969 | |
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| 0.0459 | 6.0 | 2826 | 0.5884 | 0.7864 | 0.7783 | 0.7807 | 0.9109 | |
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| 0.0267 | 7.0 | 3297 | 0.6067 | 0.8082 | 0.7813 | 0.7929 | 0.9106 | |
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| 0.0183 | 8.0 | 3768 | 0.6205 | 0.7964 | 0.7684 | 0.7786 | 0.9090 | |
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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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