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robbert_dataaugmentation
This model is a fine-tuned version of 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
