| --- |
| license: mit |
| base_model: pdelobelle/robbert-v2-dutch-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - recall |
| - accuracy |
| model-index: |
| - name: robbert_dataaugmentation |
| 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. --> |
|
|
| # robbert_dataaugmentation |
| |
| 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: 0.7814 |
| - Precisions: 0.8515 |
| - Recall: 0.8094 |
| - F-measure: 0.8265 |
| - Accuracy: 0.9039 |
| |
| ## 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: 14 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
| | 0.5813 | 1.0 | 285 | 0.4311 | 0.7695 | 0.7413 | 0.7537 | 0.8704 | |
| | 0.2533 | 2.0 | 570 | 0.4952 | 0.8339 | 0.7501 | 0.7745 | 0.8801 | |
| | 0.1216 | 3.0 | 855 | 0.5067 | 0.8403 | 0.7968 | 0.8148 | 0.8932 | |
| | 0.0685 | 4.0 | 1140 | 0.6121 | 0.8041 | 0.7972 | 0.7963 | 0.8886 | |
| | 0.0478 | 5.0 | 1425 | 0.6603 | 0.8239 | 0.7820 | 0.7983 | 0.8893 | |
| | 0.0294 | 6.0 | 1710 | 0.7029 | 0.8190 | 0.8029 | 0.8083 | 0.8954 | |
| | 0.0147 | 7.0 | 1995 | 0.7219 | 0.8332 | 0.8198 | 0.8227 | 0.8991 | |
| | 0.0142 | 8.0 | 2280 | 0.7702 | 0.8330 | 0.7953 | 0.8109 | 0.8961 | |
| | 0.0099 | 9.0 | 2565 | 0.7670 | 0.8340 | 0.7943 | 0.8086 | 0.8972 | |
| | 0.0044 | 10.0 | 2850 | 0.8132 | 0.8434 | 0.8026 | 0.8193 | 0.9025 | |
| | 0.0058 | 11.0 | 3135 | 0.7757 | 0.8468 | 0.8100 | 0.8253 | 0.9033 | |
| | 0.0046 | 12.0 | 3420 | 0.7814 | 0.8515 | 0.8094 | 0.8265 | 0.9039 | |
| | 0.0029 | 13.0 | 3705 | 0.8057 | 0.8494 | 0.8046 | 0.8229 | 0.9029 | |
| | 0.0012 | 14.0 | 3990 | 0.7994 | 0.8492 | 0.8047 | 0.8230 | 0.9031 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.34.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.14.1 |
| |
