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--- |
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license: mit |
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base_model: hongpingjun98/BioMedNLP_DeBERTa |
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tags: |
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- generated_from_trainer |
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datasets: |
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- sem_eval_2024_task_2 |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: BioMedNLP_DeBERTa_all_updates |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: sem_eval_2024_task_2 |
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type: sem_eval_2024_task_2 |
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config: sem_eval_2024_task_2_source |
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split: validation |
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args: sem_eval_2024_task_2_source |
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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 |
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should probably proofread and complete it, then remove this comment. --> |
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# BioMedNLP_DeBERTa_all_updates |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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