--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230826083404' results: [] --- # 20230826083404 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5588 - Accuracy: 0.56 ## 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: 0.05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 25 | 0.6769 | 0.61 | | No log | 2.0 | 50 | 0.5349 | 0.59 | | No log | 3.0 | 75 | 0.6615 | 0.58 | | No log | 4.0 | 100 | 0.6596 | 0.64 | | No log | 5.0 | 125 | 0.5523 | 0.71 | | No log | 6.0 | 150 | 0.8447 | 0.67 | | No log | 7.0 | 175 | 0.7506 | 0.66 | | No log | 8.0 | 200 | 0.8463 | 0.68 | | No log | 9.0 | 225 | 0.9064 | 0.56 | | No log | 10.0 | 250 | 0.5533 | 0.58 | | No log | 11.0 | 275 | 0.5701 | 0.41 | | No log | 12.0 | 300 | 0.5593 | 0.51 | | No log | 13.0 | 325 | 0.5599 | 0.52 | | No log | 14.0 | 350 | 0.5619 | 0.37 | | No log | 15.0 | 375 | 0.5591 | 0.56 | | No log | 16.0 | 400 | 0.5569 | 0.55 | | No log | 17.0 | 425 | 0.5511 | 0.56 | | No log | 18.0 | 450 | 0.5599 | 0.52 | | No log | 19.0 | 475 | 0.5561 | 0.59 | | 1.4827 | 20.0 | 500 | 0.5577 | 0.57 | | 1.4827 | 21.0 | 525 | 0.5537 | 0.58 | | 1.4827 | 22.0 | 550 | 0.5616 | 0.43 | | 1.4827 | 23.0 | 575 | 0.5607 | 0.34 | | 1.4827 | 24.0 | 600 | 0.5616 | 0.39 | | 1.4827 | 25.0 | 625 | 0.5597 | 0.56 | | 1.4827 | 26.0 | 650 | 0.5623 | 0.41 | | 1.4827 | 27.0 | 675 | 0.5612 | 0.43 | | 1.4827 | 28.0 | 700 | 0.5573 | 0.57 | | 1.4827 | 29.0 | 725 | 0.5631 | 0.42 | | 1.4827 | 30.0 | 750 | 0.5594 | 0.51 | | 1.4827 | 31.0 | 775 | 0.5593 | 0.56 | | 1.4827 | 32.0 | 800 | 0.5646 | 0.43 | | 1.4827 | 33.0 | 825 | 0.5664 | 0.44 | | 1.4827 | 34.0 | 850 | 0.5597 | 0.56 | | 1.4827 | 35.0 | 875 | 0.5629 | 0.41 | | 1.4827 | 36.0 | 900 | 0.5610 | 0.43 | | 1.4827 | 37.0 | 925 | 0.5572 | 0.58 | | 1.4827 | 38.0 | 950 | 0.5592 | 0.6 | | 1.4827 | 39.0 | 975 | 0.5553 | 0.59 | | 1.1505 | 40.0 | 1000 | 0.5597 | 0.58 | | 1.1505 | 41.0 | 1025 | 0.5570 | 0.62 | | 1.1505 | 42.0 | 1050 | 0.5582 | 0.6 | | 1.1505 | 43.0 | 1075 | 0.5601 | 0.46 | | 1.1505 | 44.0 | 1100 | 0.5598 | 0.55 | | 1.1505 | 45.0 | 1125 | 0.5574 | 0.59 | | 1.1505 | 46.0 | 1150 | 0.5591 | 0.52 | | 1.1505 | 47.0 | 1175 | 0.5601 | 0.5 | | 1.1505 | 48.0 | 1200 | 0.5593 | 0.56 | | 1.1505 | 49.0 | 1225 | 0.5600 | 0.48 | | 1.1505 | 50.0 | 1250 | 0.5620 | 0.39 | | 1.1505 | 51.0 | 1275 | 0.5598 | 0.51 | | 1.1505 | 52.0 | 1300 | 0.5616 | 0.39 | | 1.1505 | 53.0 | 1325 | 0.5601 | 0.43 | | 1.1505 | 54.0 | 1350 | 0.5617 | 0.4 | | 1.1505 | 55.0 | 1375 | 0.5619 | 0.41 | | 1.1505 | 56.0 | 1400 | 0.5625 | 0.39 | | 1.1505 | 57.0 | 1425 | 0.5591 | 0.56 | | 1.1505 | 58.0 | 1450 | 0.5588 | 0.59 | | 1.1505 | 59.0 | 1475 | 0.5580 | 0.59 | | 0.9071 | 60.0 | 1500 | 0.5584 | 0.62 | | 0.9071 | 61.0 | 1525 | 0.5590 | 0.58 | | 0.9071 | 62.0 | 1550 | 0.5585 | 0.57 | | 0.9071 | 63.0 | 1575 | 0.5586 | 0.59 | | 0.9071 | 64.0 | 1600 | 0.5589 | 0.57 | | 0.9071 | 65.0 | 1625 | 0.5587 | 0.59 | | 0.9071 | 66.0 | 1650 | 0.5588 | 0.61 | | 0.9071 | 67.0 | 1675 | 0.5592 | 0.57 | | 0.9071 | 68.0 | 1700 | 0.5579 | 0.58 | | 0.9071 | 69.0 | 1725 | 0.5586 | 0.56 | | 0.9071 | 70.0 | 1750 | 0.5590 | 0.57 | | 0.9071 | 71.0 | 1775 | 0.5590 | 0.57 | | 0.9071 | 72.0 | 1800 | 0.5590 | 0.59 | | 0.9071 | 73.0 | 1825 | 0.5591 | 0.56 | | 0.9071 | 74.0 | 1850 | 0.5586 | 0.56 | | 0.9071 | 75.0 | 1875 | 0.5590 | 0.56 | | 0.9071 | 76.0 | 1900 | 0.5592 | 0.57 | | 0.9071 | 77.0 | 1925 | 0.5587 | 0.53 | | 0.9071 | 78.0 | 1950 | 0.5588 | 0.56 | | 0.9071 | 79.0 | 1975 | 0.5589 | 0.58 | | 0.7248 | 80.0 | 2000 | 0.5588 | 0.56 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3