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BioMedNLP_DeBERTa_all_updates

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  1. README.md +30 -60
  2. model.safetensors +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.655
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  - name: Precision
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  type: precision
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- value: 0.6551396256630968
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  - name: Recall
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  type: recall
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- value: 0.655
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  - name: F1
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  type: f1
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- value: 0.6549223575304444
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.5118
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- - Accuracy: 0.655
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- - Precision: 0.6551
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- - Recall: 0.655
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- - F1: 0.6549
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  ## Model description
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@@ -74,63 +74,33 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 500
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- - num_epochs: 50
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | No log | 1.0 | 9 | 0.6482 | 0.62 | 0.6403 | 0.62 | 0.6058 |
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- | 0.7604 | 2.0 | 18 | 0.6376 | 0.635 | 0.6515 | 0.635 | 0.6248 |
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- | 0.7485 | 3.0 | 27 | 0.6256 | 0.655 | 0.6672 | 0.655 | 0.6486 |
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- | 0.7114 | 4.0 | 36 | 0.6188 | 0.675 | 0.6790 | 0.675 | 0.6732 |
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- | 0.6906 | 5.0 | 45 | 0.6181 | 0.705 | 0.7050 | 0.705 | 0.7050 |
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- | 0.5355 | 6.0 | 54 | 0.6257 | 0.68 | 0.6803 | 0.6800 | 0.6799 |
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- | 0.5411 | 7.0 | 63 | 0.6258 | 0.675 | 0.6754 | 0.675 | 0.6748 |
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- | 0.4849 | 8.0 | 72 | 0.6376 | 0.665 | 0.6670 | 0.665 | 0.6640 |
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- | 0.4386 | 9.0 | 81 | 0.6507 | 0.68 | 0.6826 | 0.6800 | 0.6788 |
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- | 0.3565 | 10.0 | 90 | 0.6631 | 0.685 | 0.6850 | 0.685 | 0.6850 |
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- | 0.3565 | 11.0 | 99 | 0.7089 | 0.66 | 0.6616 | 0.6600 | 0.6591 |
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- | 0.2992 | 12.0 | 108 | 0.7791 | 0.67 | 0.6717 | 0.6700 | 0.6692 |
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- | 0.2092 | 13.0 | 117 | 0.8224 | 0.68 | 0.6803 | 0.6800 | 0.6799 |
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- | 0.1643 | 14.0 | 126 | 0.9128 | 0.675 | 0.6750 | 0.675 | 0.6750 |
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- | 0.0811 | 15.0 | 135 | 1.0458 | 0.67 | 0.6701 | 0.67 | 0.6700 |
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- | 0.0502 | 16.0 | 144 | 1.2061 | 0.67 | 0.6701 | 0.67 | 0.6700 |
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- | 0.011 | 17.0 | 153 | 1.3763 | 0.655 | 0.6558 | 0.655 | 0.6546 |
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- | 0.0261 | 18.0 | 162 | 1.4862 | 0.655 | 0.6558 | 0.655 | 0.6546 |
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- | 0.0057 | 19.0 | 171 | 1.5609 | 0.665 | 0.6651 | 0.665 | 0.6649 |
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- | 0.0026 | 20.0 | 180 | 1.6435 | 0.655 | 0.6550 | 0.655 | 0.6550 |
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- | 0.0026 | 21.0 | 189 | 1.7122 | 0.655 | 0.6550 | 0.655 | 0.6550 |
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- | 0.0019 | 22.0 | 198 | 1.7682 | 0.655 | 0.6550 | 0.655 | 0.6550 |
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- | 0.0016 | 23.0 | 207 | 1.8163 | 0.655 | 0.6550 | 0.655 | 0.6550 |
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- | 0.0013 | 24.0 | 216 | 1.8590 | 0.655 | 0.6550 | 0.655 | 0.6550 |
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- | 0.0012 | 25.0 | 225 | 1.8883 | 0.66 | 0.6601 | 0.66 | 0.6600 |
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- | 0.001 | 26.0 | 234 | 1.9199 | 0.665 | 0.6651 | 0.665 | 0.6649 |
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- | 0.0008 | 27.0 | 243 | 1.9548 | 0.665 | 0.6651 | 0.665 | 0.6649 |
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- | 0.0007 | 28.0 | 252 | 1.9958 | 0.665 | 0.6658 | 0.665 | 0.6646 |
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- | 0.0007 | 29.0 | 261 | 2.0427 | 0.665 | 0.6658 | 0.665 | 0.6646 |
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- | 0.0006 | 30.0 | 270 | 2.0890 | 0.66 | 0.6601 | 0.66 | 0.6600 |
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- | 0.0006 | 31.0 | 279 | 2.1265 | 0.66 | 0.6601 | 0.66 | 0.6600 |
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- | 0.0005 | 32.0 | 288 | 2.1537 | 0.66 | 0.6601 | 0.66 | 0.6600 |
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- | 0.0077 | 33.0 | 297 | 2.1871 | 0.655 | 0.6550 | 0.655 | 0.6550 |
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- | 0.0004 | 34.0 | 306 | 2.2152 | 0.66 | 0.66 | 0.66 | 0.66 |
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- | 0.0004 | 35.0 | 315 | 2.2393 | 0.66 | 0.6601 | 0.66 | 0.6600 |
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- | 0.0003 | 36.0 | 324 | 2.2641 | 0.66 | 0.6601 | 0.66 | 0.6600 |
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- | 0.0003 | 37.0 | 333 | 2.2881 | 0.66 | 0.6601 | 0.66 | 0.6600 |
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- | 0.0008 | 38.0 | 342 | 2.3215 | 0.645 | 0.6462 | 0.645 | 0.6443 |
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- | 0.0005 | 39.0 | 351 | 2.3445 | 0.665 | 0.6650 | 0.665 | 0.6650 |
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- | 0.0426 | 40.0 | 360 | 2.3033 | 0.68 | 0.6818 | 0.6800 | 0.6792 |
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- | 0.0426 | 41.0 | 369 | 2.3582 | 0.66 | 0.6601 | 0.66 | 0.6600 |
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- | 0.0005 | 42.0 | 378 | 2.3550 | 0.66 | 0.6603 | 0.66 | 0.6599 |
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- | 0.0402 | 43.0 | 387 | 2.3575 | 0.665 | 0.6654 | 0.665 | 0.6648 |
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- | 0.0003 | 44.0 | 396 | 2.3372 | 0.675 | 0.6752 | 0.675 | 0.6749 |
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- | 0.0135 | 45.0 | 405 | 2.3467 | 0.66 | 0.6603 | 0.66 | 0.6599 |
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- | 0.0007 | 46.0 | 414 | 2.3033 | 0.685 | 0.6859 | 0.685 | 0.6846 |
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- | 0.0003 | 47.0 | 423 | 2.2770 | 0.675 | 0.6764 | 0.675 | 0.6743 |
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- | 0.0003 | 48.0 | 432 | 2.3131 | 0.68 | 0.6807 | 0.6800 | 0.6797 |
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- | 0.0002 | 49.0 | 441 | 2.4371 | 0.66 | 0.6601 | 0.66 | 0.6600 |
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- | 0.0004 | 50.0 | 450 | 2.5118 | 0.655 | 0.6551 | 0.655 | 0.6549 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.705
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  - name: Precision
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  type: precision
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+ value: 0.7238235615241838
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  - name: Recall
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  type: recall
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+ value: 0.7050000000000001
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  - name: F1
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  type: f1
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+ value: 0.6986644194182692
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.1863
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+ - Accuracy: 0.705
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+ - Precision: 0.7238
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+ - Recall: 0.7050
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+ - F1: 0.6987
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 20
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.4238 | 1.0 | 116 | 0.6639 | 0.665 | 0.6678 | 0.665 | 0.6636 |
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+ | 0.4316 | 2.0 | 232 | 0.6644 | 0.68 | 0.6875 | 0.6800 | 0.6768 |
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+ | 0.3819 | 3.0 | 348 | 0.7328 | 0.71 | 0.7188 | 0.71 | 0.7071 |
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+ | 0.3243 | 4.0 | 464 | 0.9162 | 0.7 | 0.7083 | 0.7 | 0.6970 |
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+ | 0.4053 | 5.0 | 580 | 0.7145 | 0.715 | 0.7214 | 0.7150 | 0.7129 |
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+ | 0.2548 | 6.0 | 696 | 1.0598 | 0.69 | 0.7016 | 0.69 | 0.6855 |
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+ | 0.3455 | 7.0 | 812 | 0.7782 | 0.72 | 0.7232 | 0.72 | 0.7190 |
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+ | 0.2177 | 8.0 | 928 | 1.1182 | 0.69 | 0.6950 | 0.69 | 0.6880 |
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+ | 0.2304 | 9.0 | 1044 | 1.4332 | 0.695 | 0.708 | 0.695 | 0.6902 |
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+ | 0.2103 | 10.0 | 1160 | 1.2736 | 0.7 | 0.7198 | 0.7 | 0.6931 |
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+ | 0.1748 | 11.0 | 1276 | 1.2654 | 0.675 | 0.6816 | 0.675 | 0.6720 |
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+ | 0.1608 | 12.0 | 1392 | 1.8885 | 0.63 | 0.6689 | 0.63 | 0.6074 |
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+ | 0.1082 | 13.0 | 1508 | 1.7004 | 0.68 | 0.7005 | 0.6800 | 0.6716 |
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+ | 0.1074 | 14.0 | 1624 | 1.8145 | 0.67 | 0.6804 | 0.67 | 0.6652 |
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+ | 0.0238 | 15.0 | 1740 | 1.7608 | 0.68 | 0.6931 | 0.68 | 0.6745 |
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+ | 0.038 | 16.0 | 1856 | 1.9937 | 0.67 | 0.6953 | 0.6700 | 0.6589 |
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+ | 0.0365 | 17.0 | 1972 | 2.1871 | 0.675 | 0.6964 | 0.675 | 0.6659 |
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+ | 0.0144 | 18.0 | 2088 | 2.1093 | 0.695 | 0.7059 | 0.6950 | 0.6909 |
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+ | 0.0014 | 19.0 | 2204 | 2.1559 | 0.695 | 0.7103 | 0.6950 | 0.6893 |
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+ | 0.0324 | 20.0 | 2320 | 2.1863 | 0.705 | 0.7238 | 0.7050 | 0.6987 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
model.safetensors CHANGED
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