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20230821214000

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: 11.6614
  • 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: 0.001
  • 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 30.4563 0.5162
31.8281 2.0 624 28.2683 0.4729
31.8281 3.0 936 22.4829 0.4729
26.6026 4.0 1248 17.2508 0.4729
20.7188 5.0 1560 15.6956 0.5271
20.7188 6.0 1872 14.7599 0.4729
18.7808 7.0 2184 14.4331 0.5271
18.7808 8.0 2496 13.9366 0.5271
18.0838 9.0 2808 13.6340 0.4729
17.722 10.0 3120 13.4379 0.4729
17.722 11.0 3432 13.4393 0.4729
17.4783 12.0 3744 13.1376 0.4729
17.2699 13.0 4056 12.9599 0.4729
17.2699 14.0 4368 12.8480 0.4729
17.0966 15.0 4680 12.7813 0.4729
17.0966 16.0 4992 12.6920 0.5271
16.9613 17.0 5304 12.5694 0.5271
16.848 18.0 5616 12.5194 0.5271
16.848 19.0 5928 12.4591 0.4729
16.7661 20.0 6240 12.3827 0.5271
16.6825 21.0 6552 12.3410 0.4729
16.6825 22.0 6864 12.3241 0.5271
16.5963 23.0 7176 12.3296 0.5271
16.5963 24.0 7488 12.2611 0.4729
16.5513 25.0 7800 12.1515 0.5271
16.4926 26.0 8112 12.1194 0.4729
16.4926 27.0 8424 12.1052 0.4729
16.4398 28.0 8736 12.0516 0.5271
16.399 29.0 9048 12.0210 0.4946
16.399 30.0 9360 12.0054 0.4729
16.3657 31.0 9672 11.9960 0.5271
16.3657 32.0 9984 11.9548 0.5271
16.3306 33.0 10296 11.9332 0.5271
16.294 34.0 10608 11.9148 0.4729
16.294 35.0 10920 11.9225 0.4729
16.2657 36.0 11232 11.8726 0.4765
16.2465 37.0 11544 11.8452 0.4729
16.2465 38.0 11856 11.8341 0.5271
16.208 39.0 12168 11.8232 0.4729
16.208 40.0 12480 11.7979 0.4729
16.191 41.0 12792 11.7895 0.4729
16.1729 42.0 13104 11.8391 0.4729
16.1729 43.0 13416 11.7619 0.5271
16.1571 44.0 13728 11.7502 0.4729
16.1268 45.0 14040 11.7520 0.4729
16.1268 46.0 14352 11.7539 0.4729
16.1194 47.0 14664 11.7541 0.4729
16.1194 48.0 14976 11.7130 0.5271
16.11 49.0 15288 11.7020 0.5271
16.0989 50.0 15600 11.6949 0.4729
16.0989 51.0 15912 11.7026 0.4729
16.0802 52.0 16224 11.7056 0.4729
16.0765 53.0 16536 11.6793 0.5271
16.0765 54.0 16848 11.6759 0.5271
16.0629 55.0 17160 11.6712 0.4729
16.0629 56.0 17472 11.6660 0.4946
16.0619 57.0 17784 11.6662 0.4729
16.0566 58.0 18096 11.6643 0.4729
16.0566 59.0 18408 11.6616 0.4729
16.0547 60.0 18720 11.6614 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/20230821214000