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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: robbert0210_lrate5b32 |
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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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# robbert0210_lrate5b32 |
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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: 0.3497 |
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- Precisions: 0.8168 |
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- Recall: 0.7629 |
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- F-measure: 0.7745 |
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- Accuracy: 0.9044 |
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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: 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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| No log | 1.0 | 118 | 0.4100 | 0.8801 | 0.6728 | 0.6931 | 0.8747 | |
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| No log | 2.0 | 236 | 0.3638 | 0.7841 | 0.7186 | 0.7176 | 0.8871 | |
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| No log | 3.0 | 354 | 0.3533 | 0.8013 | 0.7568 | 0.7535 | 0.8967 | |
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| No log | 4.0 | 472 | 0.3497 | 0.8168 | 0.7629 | 0.7745 | 0.9044 | |
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| 0.3409 | 5.0 | 590 | 0.3781 | 0.7928 | 0.7789 | 0.7814 | 0.9046 | |
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| 0.3409 | 6.0 | 708 | 0.4072 | 0.8013 | 0.7836 | 0.7884 | 0.9073 | |
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| 0.3409 | 7.0 | 826 | 0.4193 | 0.8047 | 0.8026 | 0.8012 | 0.9082 | |
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| 0.3409 | 8.0 | 944 | 0.4197 | 0.8121 | 0.8021 | 0.8049 | 0.9103 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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