albert_model
This model is a fine-tuned version of albert-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6560
- Accuracy: 0.9070
- F1: 0.8852
- Recall: 0.9122
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: 1e-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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall |
|---|---|---|---|---|---|---|
| No log | 1.0 | 167 | 0.3571 | 0.8351 | 0.8142 | 0.9198 |
| No log | 2.0 | 334 | 0.2670 | 0.8891 | 0.8683 | 0.9313 |
| 0.3358 | 3.0 | 501 | 0.2643 | 0.9115 | 0.8885 | 0.8969 |
| 0.3358 | 4.0 | 668 | 0.3804 | 0.9130 | 0.8910 | 0.9046 |
| 0.3358 | 5.0 | 835 | 0.4376 | 0.9070 | 0.8848 | 0.9084 |
| 0.1007 | 6.0 | 1002 | 0.4957 | 0.9100 | 0.8859 | 0.8893 |
| 0.1007 | 7.0 | 1169 | 0.6375 | 0.8801 | 0.8601 | 0.9389 |
| 0.1007 | 8.0 | 1336 | 0.5978 | 0.8996 | 0.8780 | 0.9198 |
| 0.012 | 9.0 | 1503 | 0.6101 | 0.9025 | 0.8816 | 0.9237 |
| 0.012 | 10.0 | 1670 | 0.6209 | 0.9085 | 0.8847 | 0.8931 |
| 0.012 | 11.0 | 1837 | 0.6485 | 0.9010 | 0.8787 | 0.9122 |
| 0.0007 | 12.0 | 2004 | 0.6480 | 0.9070 | 0.8852 | 0.9122 |
| 0.0007 | 13.0 | 2171 | 0.6527 | 0.9055 | 0.8835 | 0.9122 |
| 0.0007 | 14.0 | 2338 | 0.6557 | 0.9055 | 0.8835 | 0.9122 |
| 0.0002 | 15.0 | 2505 | 0.6560 | 0.9070 | 0.8852 | 0.9122 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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