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license: mit |
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tags: |
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- text-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: deberta-v3-large-dapt-tapt-scientific-papers-pubmed-finetuned-DAGPap22 |
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results: [] |
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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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# deberta-v3-large-dapt-tapt-scientific-papers-pubmed-finetuned-DAGPap22 |
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This model is a fine-tuned version of [domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed-tapt](https://huggingface.co/domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed-tapt) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0002 |
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- Accuracy: 0.9998 |
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- F1: 0.9999 |
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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: 6e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 50 |
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- num_epochs: 12 |
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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 | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.1884 | 1.0 | 669 | 0.0248 | 0.9951 | 0.9964 | |
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| 0.0494 | 2.0 | 1338 | 0.0084 | 0.9987 | 0.9990 | |
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| 0.0199 | 3.0 | 2007 | 0.0051 | 0.9991 | 0.9993 | |
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| 0.0079 | 4.0 | 2676 | 0.0030 | 0.9993 | 0.9995 | |
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| 0.0 | 5.0 | 3345 | 0.0026 | 0.9994 | 0.9996 | |
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| 0.0 | 6.0 | 4014 | 0.0014 | 0.9996 | 0.9997 | |
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| 0.0 | 7.0 | 4683 | 0.0015 | 0.9996 | 0.9997 | |
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| 0.0 | 8.0 | 5352 | 0.0011 | 0.9996 | 0.9997 | |
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| 0.0143 | 9.0 | 6021 | 0.0000 | 1.0 | 1.0 | |
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| 0.0 | 10.0 | 6690 | 0.0035 | 0.9991 | 0.9993 | |
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| 0.0 | 11.0 | 7359 | 0.0004 | 0.9998 | 0.9999 | |
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| 0.0 | 12.0 | 8028 | 0.0002 | 0.9998 | 0.9999 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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