--- license: mit base_model: hongpingjun98/BioMedNLP_DeBERTa tags: - generated_from_trainer datasets: - sem_eval_2024_task_2 metrics: - accuracy - precision - recall - f1 model-index: - name: BioMedNLP_DeBERTa_all_updates results: - task: name: Text Classification type: text-classification dataset: name: sem_eval_2024_task_2 type: sem_eval_2024_task_2 config: sem_eval_2024_task_2_source split: validation args: sem_eval_2024_task_2_source metrics: - name: Accuracy type: accuracy value: 0.655 - name: Precision type: precision value: 0.6551396256630968 - name: Recall type: recall value: 0.655 - name: F1 type: f1 value: 0.6549223575304444 --- # BioMedNLP_DeBERTa_all_updates This model is a fine-tuned version of [hongpingjun98/BioMedNLP_DeBERTa](https://huggingface.co/hongpingjun98/BioMedNLP_DeBERTa) on the sem_eval_2024_task_2 dataset. It achieves the following results on the evaluation set: - Loss: 2.5118 - Accuracy: 0.655 - Precision: 0.6551 - Recall: 0.655 - F1: 0.6549 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 9 | 0.6482 | 0.62 | 0.6403 | 0.62 | 0.6058 | | 0.7604 | 2.0 | 18 | 0.6376 | 0.635 | 0.6515 | 0.635 | 0.6248 | | 0.7485 | 3.0 | 27 | 0.6256 | 0.655 | 0.6672 | 0.655 | 0.6486 | | 0.7114 | 4.0 | 36 | 0.6188 | 0.675 | 0.6790 | 0.675 | 0.6732 | | 0.6906 | 5.0 | 45 | 0.6181 | 0.705 | 0.7050 | 0.705 | 0.7050 | | 0.5355 | 6.0 | 54 | 0.6257 | 0.68 | 0.6803 | 0.6800 | 0.6799 | | 0.5411 | 7.0 | 63 | 0.6258 | 0.675 | 0.6754 | 0.675 | 0.6748 | | 0.4849 | 8.0 | 72 | 0.6376 | 0.665 | 0.6670 | 0.665 | 0.6640 | | 0.4386 | 9.0 | 81 | 0.6507 | 0.68 | 0.6826 | 0.6800 | 0.6788 | | 0.3565 | 10.0 | 90 | 0.6631 | 0.685 | 0.6850 | 0.685 | 0.6850 | | 0.3565 | 11.0 | 99 | 0.7089 | 0.66 | 0.6616 | 0.6600 | 0.6591 | | 0.2992 | 12.0 | 108 | 0.7791 | 0.67 | 0.6717 | 0.6700 | 0.6692 | | 0.2092 | 13.0 | 117 | 0.8224 | 0.68 | 0.6803 | 0.6800 | 0.6799 | | 0.1643 | 14.0 | 126 | 0.9128 | 0.675 | 0.6750 | 0.675 | 0.6750 | | 0.0811 | 15.0 | 135 | 1.0458 | 0.67 | 0.6701 | 0.67 | 0.6700 | | 0.0502 | 16.0 | 144 | 1.2061 | 0.67 | 0.6701 | 0.67 | 0.6700 | | 0.011 | 17.0 | 153 | 1.3763 | 0.655 | 0.6558 | 0.655 | 0.6546 | | 0.0261 | 18.0 | 162 | 1.4862 | 0.655 | 0.6558 | 0.655 | 0.6546 | | 0.0057 | 19.0 | 171 | 1.5609 | 0.665 | 0.6651 | 0.665 | 0.6649 | | 0.0026 | 20.0 | 180 | 1.6435 | 0.655 | 0.6550 | 0.655 | 0.6550 | | 0.0026 | 21.0 | 189 | 1.7122 | 0.655 | 0.6550 | 0.655 | 0.6550 | | 0.0019 | 22.0 | 198 | 1.7682 | 0.655 | 0.6550 | 0.655 | 0.6550 | | 0.0016 | 23.0 | 207 | 1.8163 | 0.655 | 0.6550 | 0.655 | 0.6550 | | 0.0013 | 24.0 | 216 | 1.8590 | 0.655 | 0.6550 | 0.655 | 0.6550 | | 0.0012 | 25.0 | 225 | 1.8883 | 0.66 | 0.6601 | 0.66 | 0.6600 | | 0.001 | 26.0 | 234 | 1.9199 | 0.665 | 0.6651 | 0.665 | 0.6649 | | 0.0008 | 27.0 | 243 | 1.9548 | 0.665 | 0.6651 | 0.665 | 0.6649 | | 0.0007 | 28.0 | 252 | 1.9958 | 0.665 | 0.6658 | 0.665 | 0.6646 | | 0.0007 | 29.0 | 261 | 2.0427 | 0.665 | 0.6658 | 0.665 | 0.6646 | | 0.0006 | 30.0 | 270 | 2.0890 | 0.66 | 0.6601 | 0.66 | 0.6600 | | 0.0006 | 31.0 | 279 | 2.1265 | 0.66 | 0.6601 | 0.66 | 0.6600 | | 0.0005 | 32.0 | 288 | 2.1537 | 0.66 | 0.6601 | 0.66 | 0.6600 | | 0.0077 | 33.0 | 297 | 2.1871 | 0.655 | 0.6550 | 0.655 | 0.6550 | | 0.0004 | 34.0 | 306 | 2.2152 | 0.66 | 0.66 | 0.66 | 0.66 | | 0.0004 | 35.0 | 315 | 2.2393 | 0.66 | 0.6601 | 0.66 | 0.6600 | | 0.0003 | 36.0 | 324 | 2.2641 | 0.66 | 0.6601 | 0.66 | 0.6600 | | 0.0003 | 37.0 | 333 | 2.2881 | 0.66 | 0.6601 | 0.66 | 0.6600 | | 0.0008 | 38.0 | 342 | 2.3215 | 0.645 | 0.6462 | 0.645 | 0.6443 | | 0.0005 | 39.0 | 351 | 2.3445 | 0.665 | 0.6650 | 0.665 | 0.6650 | | 0.0426 | 40.0 | 360 | 2.3033 | 0.68 | 0.6818 | 0.6800 | 0.6792 | | 0.0426 | 41.0 | 369 | 2.3582 | 0.66 | 0.6601 | 0.66 | 0.6600 | | 0.0005 | 42.0 | 378 | 2.3550 | 0.66 | 0.6603 | 0.66 | 0.6599 | | 0.0402 | 43.0 | 387 | 2.3575 | 0.665 | 0.6654 | 0.665 | 0.6648 | | 0.0003 | 44.0 | 396 | 2.3372 | 0.675 | 0.6752 | 0.675 | 0.6749 | | 0.0135 | 45.0 | 405 | 2.3467 | 0.66 | 0.6603 | 0.66 | 0.6599 | | 0.0007 | 46.0 | 414 | 2.3033 | 0.685 | 0.6859 | 0.685 | 0.6846 | | 0.0003 | 47.0 | 423 | 2.2770 | 0.675 | 0.6764 | 0.675 | 0.6743 | | 0.0003 | 48.0 | 432 | 2.3131 | 0.68 | 0.6807 | 0.6800 | 0.6797 | | 0.0002 | 49.0 | 441 | 2.4371 | 0.66 | 0.6601 | 0.66 | 0.6600 | | 0.0004 | 50.0 | 450 | 2.5118 | 0.655 | 0.6551 | 0.655 | 0.6549 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0