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ECHR_test_2_task_B
This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the lex_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.2092
- Macro-f1: 0.5250
- Micro-f1: 0.6190
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Macro-f1 | Micro-f1 |
|---|---|---|---|---|---|
| 0.2119 | 0.44 | 500 | 0.2945 | 0.2637 | 0.4453 |
| 0.1702 | 0.89 | 1000 | 0.2734 | 0.3246 | 0.4843 |
| 0.1736 | 1.33 | 1500 | 0.2633 | 0.3725 | 0.5133 |
| 0.1571 | 1.78 | 2000 | 0.2549 | 0.3942 | 0.5417 |
| 0.1476 | 2.22 | 2500 | 0.2348 | 0.4187 | 0.5649 |
| 0.1599 | 2.67 | 3000 | 0.2427 | 0.4286 | 0.5606 |
| 0.1481 | 3.11 | 3500 | 0.2210 | 0.4664 | 0.5780 |
| 0.1412 | 3.56 | 4000 | 0.2542 | 0.4362 | 0.5617 |
| 0.1505 | 4.0 | 4500 | 0.2249 | 0.4728 | 0.5863 |
| 0.1425 | 4.44 | 5000 | 0.2311 | 0.4576 | 0.5845 |
| 0.1461 | 4.89 | 5500 | 0.2261 | 0.4590 | 0.5832 |
| 0.1451 | 5.33 | 6000 | 0.2248 | 0.4738 | 0.5901 |
| 0.1281 | 5.78 | 6500 | 0.2317 | 0.4641 | 0.5896 |
| 0.1354 | 6.22 | 7000 | 0.2366 | 0.4639 | 0.5946 |
| 0.1204 | 6.67 | 7500 | 0.2311 | 0.4875 | 0.5877 |
| 0.1229 | 7.11 | 8000 | 0.2083 | 0.4815 | 0.6020 |
| 0.1368 | 7.56 | 8500 | 0.2170 | 0.5213 | 0.6021 |
| 0.1288 | 8.0 | 9000 | 0.2136 | 0.5336 | 0.6176 |
| 0.1275 | 8.44 | 9500 | 0.2180 | 0.5204 | 0.6082 |
| 0.1232 | 8.89 | 10000 | 0.2147 | 0.5334 | 0.6083 |
| 0.1319 | 9.33 | 10500 | 0.2121 | 0.5312 | 0.6186 |
| 0.1267 | 9.78 | 11000 | 0.2092 | 0.5250 | 0.6190 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
