| --- |
| license: mit |
| base_model: pdelobelle/robbert-v2-dutch-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - recall |
| - accuracy |
| model-index: |
| - name: RobBERTBestModelOct11 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # robbert0410_lrate7.5b16 |
| |
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5355 |
| - Precisions: 0.8523 |
| - Recall: 0.8173 |
| - F-measure: 0.8307 |
| - Accuracy: 0.9209 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 7.5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 16 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
| | 0.6127 | 1.0 | 236 | 0.3656 | 0.8687 | 0.6888 | 0.7011 | 0.8817 | |
| | 0.3078 | 2.0 | 472 | 0.3390 | 0.8253 | 0.7452 | 0.7612 | 0.8947 | |
| | 0.1742 | 3.0 | 708 | 0.3899 | 0.7602 | 0.7560 | 0.7469 | 0.8957 | |
| | 0.1242 | 4.0 | 944 | 0.4402 | 0.8560 | 0.7678 | 0.7861 | 0.9055 | |
| | 0.0749 | 5.0 | 1180 | 0.4206 | 0.8163 | 0.8139 | 0.8127 | 0.9121 | |
| | 0.0533 | 6.0 | 1416 | 0.4824 | 0.8257 | 0.7936 | 0.8060 | 0.9124 | |
| | 0.0366 | 7.0 | 1652 | 0.4927 | 0.8506 | 0.7956 | 0.8158 | 0.9176 | |
| | 0.0273 | 8.0 | 1888 | 0.5638 | 0.8631 | 0.7855 | 0.8093 | 0.9202 | |
| | 0.0206 | 9.0 | 2124 | 0.5507 | 0.8322 | 0.7957 | 0.8096 | 0.9141 | |
| | 0.0154 | 10.0 | 2360 | 0.5355 | 0.8523 | 0.8173 | 0.8307 | 0.9209 | |
| | 0.0105 | 11.0 | 2596 | 0.5812 | 0.8301 | 0.7961 | 0.8088 | 0.9162 | |
| | 0.0086 | 12.0 | 2832 | 0.6084 | 0.8357 | 0.8065 | 0.8192 | 0.9130 | |
| | 0.0046 | 13.0 | 3068 | 0.6035 | 0.8310 | 0.7948 | 0.8104 | 0.9137 | |
| | 0.0036 | 14.0 | 3304 | 0.6034 | 0.8223 | 0.7980 | 0.8074 | 0.9134 | |
| | 0.0043 | 15.0 | 3540 | 0.6146 | 0.8198 | 0.7869 | 0.7999 | 0.9120 | |
| | 0.0018 | 16.0 | 3776 | 0.6070 | 0.8244 | 0.7894 | 0.8029 | 0.9134 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.34.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.14.0 |
| |
