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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: 0.6714791459232217 |
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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: 0.6465073388150311 |
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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: 2.4673 |
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- Accuracy: 0.655 |
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- Precision: 0.6715 |
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- Recall: 0.655 |
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- F1: 0.6465 |
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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: 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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| 0.3757 | 1.0 | 115 | 0.6988 | 0.7 | 0.7020 | 0.7 | 0.6992 | |
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| 0.3965 | 2.0 | 230 | 0.7320 | 0.695 | 0.7259 | 0.6950 | 0.6842 | |
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| 0.3603 | 3.0 | 345 | 0.7736 | 0.7 | 0.7338 | 0.7 | 0.6888 | |
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| 0.2721 | 4.0 | 460 | 0.8780 | 0.665 | 0.6802 | 0.665 | 0.6578 | |
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| 0.4003 | 5.0 | 575 | 0.9046 | 0.655 | 0.6796 | 0.655 | 0.6428 | |
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| 0.2773 | 6.0 | 690 | 0.9664 | 0.7 | 0.7053 | 0.7 | 0.6981 | |
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| 0.2465 | 7.0 | 805 | 1.0035 | 0.67 | 0.6845 | 0.67 | 0.6634 | |
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| 0.3437 | 8.0 | 920 | 1.0087 | 0.665 | 0.6780 | 0.665 | 0.6588 | |
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| 0.1175 | 9.0 | 1035 | 1.2598 | 0.675 | 0.6780 | 0.675 | 0.6736 | |
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| 0.155 | 10.0 | 1150 | 1.3976 | 0.69 | 0.7038 | 0.69 | 0.6847 | |
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| 0.1013 | 11.0 | 1265 | 1.3761 | 0.67 | 0.6757 | 0.6700 | 0.6673 | |
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| 0.1664 | 12.0 | 1380 | 1.5027 | 0.695 | 0.6950 | 0.695 | 0.6950 | |
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| 0.0847 | 13.0 | 1495 | 1.8199 | 0.685 | 0.6973 | 0.685 | 0.68 | |
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| 0.0856 | 14.0 | 1610 | 1.8299 | 0.66 | 0.6783 | 0.6600 | 0.6511 | |
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| 0.1053 | 15.0 | 1725 | 2.0431 | 0.665 | 0.6852 | 0.665 | 0.6556 | |
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| 0.0958 | 16.0 | 1840 | 1.9203 | 0.7 | 0.7040 | 0.7 | 0.6985 | |
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| 0.0344 | 17.0 | 1955 | 2.1390 | 0.665 | 0.6780 | 0.665 | 0.6588 | |
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| 0.014 | 18.0 | 2070 | 2.3609 | 0.655 | 0.6692 | 0.655 | 0.6476 | |
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| 0.0085 | 19.0 | 2185 | 2.4310 | 0.65 | 0.6671 | 0.65 | 0.6408 | |
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| 0.0285 | 20.0 | 2300 | 2.4673 | 0.655 | 0.6715 | 0.655 | 0.6465 | |
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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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