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BioMedNLP_DeBERTa_all_updates

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Eval Results
Inference Endpoints
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README.md ADDED
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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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+
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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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+
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+ # BioMedNLP_DeBERTa_all_updates
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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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