20230824064723
This model is a fine-tuned version of bert-large-cased on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6742
- Accuracy: 0.7076
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: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 312 | 1.0968 | 0.5307 |
0.8903 | 2.0 | 624 | 0.9977 | 0.4729 |
0.8903 | 3.0 | 936 | 0.6500 | 0.5415 |
0.813 | 4.0 | 1248 | 0.8148 | 0.4729 |
0.7606 | 5.0 | 1560 | 0.6263 | 0.5993 |
0.7606 | 6.0 | 1872 | 0.7920 | 0.6245 |
0.7342 | 7.0 | 2184 | 1.2811 | 0.5884 |
0.7342 | 8.0 | 2496 | 0.5840 | 0.6462 |
0.6906 | 9.0 | 2808 | 0.5715 | 0.6751 |
0.6551 | 10.0 | 3120 | 0.5806 | 0.6859 |
0.6551 | 11.0 | 3432 | 0.5498 | 0.6823 |
0.6197 | 12.0 | 3744 | 0.6886 | 0.6968 |
0.5972 | 13.0 | 4056 | 1.1724 | 0.4477 |
0.5972 | 14.0 | 4368 | 0.6682 | 0.6101 |
0.7875 | 15.0 | 4680 | 0.6779 | 0.5560 |
0.7875 | 16.0 | 4992 | 0.9667 | 0.6354 |
0.6467 | 17.0 | 5304 | 0.9092 | 0.6606 |
0.5892 | 18.0 | 5616 | 0.6701 | 0.4621 |
0.5892 | 19.0 | 5928 | 0.6021 | 0.6643 |
0.6056 | 20.0 | 6240 | 0.8808 | 0.6787 |
0.5409 | 21.0 | 6552 | 0.5458 | 0.6751 |
0.5409 | 22.0 | 6864 | 0.5723 | 0.6859 |
0.5387 | 23.0 | 7176 | 0.9638 | 0.6679 |
0.5387 | 24.0 | 7488 | 0.7176 | 0.6968 |
0.511 | 25.0 | 7800 | 0.6557 | 0.6895 |
0.4744 | 26.0 | 8112 | 0.5338 | 0.7148 |
0.4744 | 27.0 | 8424 | 0.5646 | 0.7076 |
0.4743 | 28.0 | 8736 | 0.5423 | 0.7040 |
0.4598 | 29.0 | 9048 | 0.6324 | 0.7076 |
0.4598 | 30.0 | 9360 | 0.7069 | 0.7004 |
0.4485 | 31.0 | 9672 | 0.6809 | 0.6859 |
0.4485 | 32.0 | 9984 | 0.5675 | 0.7076 |
0.442 | 33.0 | 10296 | 0.8006 | 0.6895 |
0.4141 | 34.0 | 10608 | 0.5902 | 0.7112 |
0.4141 | 35.0 | 10920 | 0.6252 | 0.7148 |
0.4054 | 36.0 | 11232 | 0.8398 | 0.7112 |
0.3819 | 37.0 | 11544 | 0.7482 | 0.7004 |
0.3819 | 38.0 | 11856 | 0.6538 | 0.7112 |
0.3825 | 39.0 | 12168 | 0.7720 | 0.6968 |
0.3825 | 40.0 | 12480 | 0.6094 | 0.6931 |
0.379 | 41.0 | 12792 | 0.5863 | 0.7040 |
0.3701 | 42.0 | 13104 | 0.6197 | 0.7040 |
0.3701 | 43.0 | 13416 | 0.5795 | 0.7112 |
0.3576 | 44.0 | 13728 | 0.6484 | 0.7076 |
0.3454 | 45.0 | 14040 | 0.6623 | 0.6968 |
0.3454 | 46.0 | 14352 | 0.6562 | 0.7220 |
0.3455 | 47.0 | 14664 | 0.5921 | 0.7184 |
0.3455 | 48.0 | 14976 | 0.6980 | 0.7112 |
0.3344 | 49.0 | 15288 | 0.6210 | 0.7004 |
0.3285 | 50.0 | 15600 | 0.5674 | 0.7184 |
0.3285 | 51.0 | 15912 | 0.6134 | 0.7040 |
0.3295 | 52.0 | 16224 | 0.7118 | 0.7148 |
0.3181 | 53.0 | 16536 | 0.6978 | 0.7040 |
0.3181 | 54.0 | 16848 | 0.6851 | 0.7112 |
0.3021 | 55.0 | 17160 | 0.7702 | 0.7040 |
0.3021 | 56.0 | 17472 | 0.7319 | 0.7040 |
0.3044 | 57.0 | 17784 | 0.6459 | 0.7076 |
0.2938 | 58.0 | 18096 | 0.6386 | 0.7076 |
0.2938 | 59.0 | 18408 | 0.6550 | 0.7004 |
0.2991 | 60.0 | 18720 | 0.6742 | 0.7076 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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