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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: RobBERTBestModelOct11 |
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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_lrate7.5b16 |
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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: 0.5355 |
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- Precisions: 0.8523 |
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- Recall: 0.8173 |
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- F-measure: 0.8307 |
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- Accuracy: 0.9209 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 16 |
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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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| 0.6127 | 1.0 | 236 | 0.3656 | 0.8687 | 0.6888 | 0.7011 | 0.8817 | |
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| 0.3078 | 2.0 | 472 | 0.3390 | 0.8253 | 0.7452 | 0.7612 | 0.8947 | |
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| 0.1742 | 3.0 | 708 | 0.3899 | 0.7602 | 0.7560 | 0.7469 | 0.8957 | |
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| 0.1242 | 4.0 | 944 | 0.4402 | 0.8560 | 0.7678 | 0.7861 | 0.9055 | |
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| 0.0749 | 5.0 | 1180 | 0.4206 | 0.8163 | 0.8139 | 0.8127 | 0.9121 | |
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| 0.0533 | 6.0 | 1416 | 0.4824 | 0.8257 | 0.7936 | 0.8060 | 0.9124 | |
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| 0.0366 | 7.0 | 1652 | 0.4927 | 0.8506 | 0.7956 | 0.8158 | 0.9176 | |
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| 0.0273 | 8.0 | 1888 | 0.5638 | 0.8631 | 0.7855 | 0.8093 | 0.9202 | |
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| 0.0206 | 9.0 | 2124 | 0.5507 | 0.8322 | 0.7957 | 0.8096 | 0.9141 | |
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| 0.0154 | 10.0 | 2360 | 0.5355 | 0.8523 | 0.8173 | 0.8307 | 0.9209 | |
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| 0.0105 | 11.0 | 2596 | 0.5812 | 0.8301 | 0.7961 | 0.8088 | 0.9162 | |
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| 0.0086 | 12.0 | 2832 | 0.6084 | 0.8357 | 0.8065 | 0.8192 | 0.9130 | |
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| 0.0046 | 13.0 | 3068 | 0.6035 | 0.8310 | 0.7948 | 0.8104 | 0.9137 | |
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| 0.0036 | 14.0 | 3304 | 0.6034 | 0.8223 | 0.7980 | 0.8074 | 0.9134 | |
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| 0.0043 | 15.0 | 3540 | 0.6146 | 0.8198 | 0.7869 | 0.7999 | 0.9120 | |
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| 0.0018 | 16.0 | 3776 | 0.6070 | 0.8244 | 0.7894 | 0.8029 | 0.9134 | |
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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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