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bert-large-cased-sigir-support-no-label-40
This model is a fine-tuned version of bert-large-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1107
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: 4e-05
- train_batch_size: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.7638 | 1.0 | 246 | 2.2805 |
2.1924 | 2.0 | 492 | 1.9602 |
1.8921 | 3.0 | 738 | 1.7992 |
1.7412 | 4.0 | 984 | 1.7229 |
1.6311 | 5.0 | 1230 | 1.6165 |
1.5421 | 6.0 | 1476 | 1.5400 |
1.4619 | 7.0 | 1722 | 1.5001 |
1.3846 | 8.0 | 1968 | 1.4381 |
1.3414 | 9.0 | 2214 | 1.4285 |
1.2894 | 10.0 | 2460 | 1.4108 |
1.2467 | 11.0 | 2706 | 1.3460 |
1.1992 | 12.0 | 2952 | 1.3434 |
1.1612 | 13.0 | 3198 | 1.2951 |
1.1266 | 14.0 | 3444 | 1.2518 |
1.0933 | 15.0 | 3690 | 1.2825 |
1.0625 | 16.0 | 3936 | 1.2523 |
1.0386 | 17.0 | 4182 | 1.2251 |
1.0066 | 18.0 | 4428 | 1.2339 |
0.9755 | 19.0 | 4674 | 1.1887 |
0.9656 | 20.0 | 4920 | 1.2288 |
0.9517 | 21.0 | 5166 | 1.1391 |
0.9207 | 22.0 | 5412 | 1.1718 |
0.8964 | 23.0 | 5658 | 1.1850 |
0.8891 | 24.0 | 5904 | 1.1306 |
0.8564 | 25.0 | 6150 | 1.1956 |
0.851 | 26.0 | 6396 | 1.1263 |
0.8331 | 27.0 | 6642 | 1.1060 |
0.8143 | 28.0 | 6888 | 1.0689 |
0.7972 | 29.0 | 7134 | 1.0772 |
0.7857 | 30.0 | 7380 | 1.1103 |
0.7687 | 31.0 | 7626 | 1.1635 |
0.7653 | 32.0 | 7872 | 1.0736 |
0.777 | 33.0 | 8118 | 1.1103 |
0.741 | 34.0 | 8364 | 1.0830 |
0.7408 | 35.0 | 8610 | 1.0809 |
0.736 | 36.0 | 8856 | 1.0894 |
0.7362 | 37.0 | 9102 | 1.0691 |
0.727 | 38.0 | 9348 | 1.0519 |
0.715 | 39.0 | 9594 | 1.0919 |
0.7286 | 40.0 | 9840 | 1.1107 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2