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20230822011214

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: 13.1424
  • Accuracy: 0.4729

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: 0.0005
  • 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 34.1366 0.4729
34.4899 2.0 624 31.6158 0.4982
34.4899 3.0 936 29.7502 0.4765
31.3598 4.0 1248 29.3626 0.5018
29.6767 5.0 1560 29.1220 0.4729
29.6767 6.0 1872 28.7672 0.5307
29.2217 7.0 2184 27.2268 0.5126
29.2217 8.0 2496 23.7819 0.4982
27.2285 9.0 2808 20.2651 0.5271
23.6907 10.0 3120 17.8350 0.5271
23.6907 11.0 3432 16.7909 0.4729
21.0475 12.0 3744 16.1897 0.4729
20.1309 13.0 4056 15.7234 0.4729
20.1309 14.0 4368 15.4084 0.4729
19.6553 15.0 4680 15.1657 0.4729
19.6553 16.0 4992 14.9716 0.5271
19.3496 17.0 5304 14.7880 0.5271
19.122 18.0 5616 14.6322 0.4729
19.122 19.0 5928 14.5424 0.4729
18.9517 20.0 6240 14.4178 0.5271
18.7994 21.0 6552 14.2725 0.4729
18.7994 22.0 6864 14.2138 0.5271
18.6835 23.0 7176 14.1064 0.5271
18.6835 24.0 7488 14.0401 0.4729
18.59 25.0 7800 13.9478 0.4729
18.504 26.0 8112 13.9156 0.4729
18.504 27.0 8424 13.8335 0.4729
18.4387 28.0 8736 13.7761 0.4729
18.3758 29.0 9048 13.7312 0.4729
18.3758 30.0 9360 13.6791 0.4729
18.3264 31.0 9672 13.6458 0.5271
18.3264 32.0 9984 13.5991 0.4729
18.2808 33.0 10296 13.5762 0.5271
18.2355 34.0 10608 13.5283 0.4729
18.2355 35.0 10920 13.4919 0.4729
18.2071 36.0 11232 13.4721 0.4729
18.1831 37.0 11544 13.4375 0.4729
18.1831 38.0 11856 13.4097 0.5271
18.1448 39.0 12168 13.4004 0.5271
18.1448 40.0 12480 13.3691 0.5271
18.1182 41.0 12792 13.3430 0.4729
18.1006 42.0 13104 13.3514 0.4729
18.1006 43.0 13416 13.3017 0.4729
18.0785 44.0 13728 13.2838 0.4729
18.0562 45.0 14040 13.2687 0.4729
18.0562 46.0 14352 13.2555 0.4729
18.0454 47.0 14664 13.2510 0.4729
18.0454 48.0 14976 13.2384 0.5271
18.0293 49.0 15288 13.2096 0.4729
18.0221 50.0 15600 13.2013 0.4729
18.0221 51.0 15912 13.1936 0.4729
17.9969 52.0 16224 13.1813 0.4729
17.9919 53.0 16536 13.1736 0.4729
17.9919 54.0 16848 13.1681 0.5271
17.9823 55.0 17160 13.1559 0.4729
17.9823 56.0 17472 13.1537 0.4729
17.9804 57.0 17784 13.1490 0.4729
17.9743 58.0 18096 13.1461 0.4729
17.9743 59.0 18408 13.1429 0.4729
17.9703 60.0 18720 13.1424 0.4729

Framework versions

  • Transformers 4.30.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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Dataset used to train Onutoa/20230822011214