--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: tryv3_16epochs results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue config: rte split: validation args: rte metrics: - name: Accuracy type: accuracy value: 0.6498194945848376 --- # tryv3_16epochs This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 1.0256 - Accuracy: 0.6498 ## 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: 2e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | No log | 1.0 | 78 | 0.6907 | 0.5126 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | No log | 1.0 | 78 | 0.6698 | 0.6318 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | No log | 2.0 | 156 | 0.7006 | 0.5884 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | No log | 2.0 | 156 | 0.6683 | 0.6715 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | No log | 3.0 | 234 | 0.7282 | 0.5957 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | No log | 3.0 | 234 | 0.7189 | 0.7004 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | No log | 4.0 | 312 | 0.7194 | 0.6534 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | No log | 4.0 | 312 | 0.7805 | 0.7004 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | No log | 5.0 | 390 | 0.8791 | 0.6354 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | No log | 5.0 | 390 | 0.8328 | 0.7076 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | No log | 6.0 | 468 | 1.0037 | 0.6173 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | No log | 6.0 | 468 | 0.8701 | 0.6895 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.5371 | 7.0 | 546 | 0.9121 | 0.6245 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.5371 | 7.0 | 546 | 0.8220 | 0.6859 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.5371 | 8.0 | 624 | 1.0092 | 0.6245 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.5371 | 8.0 | 624 | 0.8341 | 0.6823 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.5371 | 9.0 | 702 | 0.9687 | 0.6426 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.5371 | 9.0 | 702 | 0.8538 | 0.6643 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.5371 | 10.0 | 780 | 1.0111 | 0.6354 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.5371 | 10.0 | 780 | 0.8117 | 0.6968 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.5371 | 11.0 | 858 | 0.9616 | 0.6498 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.5371 | 11.0 | 858 | 0.8113 | 0.6895 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.5371 | 12.0 | 936 | 0.9934 | 0.6462 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.5371 | 12.0 | 936 | 0.8179 | 0.6895 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.1174 | 13.0 | 1014 | 1.0097 | 0.6318 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.1174 | 13.0 | 1014 | 0.8191 | 0.7004 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.1174 | 14.0 | 1092 | 1.0019 | 0.6462 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.1174 | 14.0 | 1092 | 0.8157 | 0.7004 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.1174 | 15.0 | 1170 | 1.0127 | 0.6318 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.1174 | 15.0 | 1170 | 0.8178 | 0.6895 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | | 0.1174 | 16.0 | 1248 | 1.0095 | 0.6462 | OrderedDict([(, {0: 576, 1: 448, 2: 576, 3: 768, 4: 704, 5: 704, 6: 768, 7: 576, 8: 704, 9: 704, 10: 512, 11: 640, 12: 608, 13: 571, 14: 589, 15: 542, 16: 576, 17: 589, 18: 568, 19: 537, 20: 562, 21: 453, 22: 376, 23: 147})]) | | 0.1174 | 16.0 | 1248 | 0.8178 | 0.6895 | OrderedDict([(, {0: 768, 1: 768, 2: 768, 3: 768, 4: 768, 5: 768, 6: 768, 7: 768, 8: 768, 9: 768, 10: 768, 11: 768, 12: 3072, 13: 3072, 14: 3072, 15: 3072, 16: 3072, 17: 3072, 18: 3072, 19: 3072, 20: 3072, 21: 3072, 22: 3072, 23: 3072})]) | ### Framework versions - Transformers 4.29.1 - Pytorch 1.12.1 - Datasets 2.13.1 - Tokenizers 0.13.3