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--- |
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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-xsmall-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-xsmall-finetuned-DAGPap22 |
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This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0798 |
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- Accuracy: 0.9907 |
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- F1: 0.9934 |
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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: 4.5e-05 |
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- train_batch_size: 12 |
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- eval_batch_size: 12 |
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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: 1000 |
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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 | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 1.0 | 402 | 0.1626 | 0.9477 | 0.9616 | |
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| 0.4003 | 2.0 | 804 | 0.0586 | 0.9794 | 0.9853 | |
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| 0.1075 | 3.0 | 1206 | 0.0342 | 0.9907 | 0.9933 | |
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| 0.0581 | 4.0 | 1608 | 0.1140 | 0.9776 | 0.9838 | |
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| 0.0245 | 5.0 | 2010 | 0.1409 | 0.9776 | 0.9842 | |
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| 0.0245 | 6.0 | 2412 | 0.0732 | 0.9832 | 0.9881 | |
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| 0.0167 | 7.0 | 2814 | 0.1996 | 0.9682 | 0.9778 | |
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| 0.0139 | 8.0 | 3216 | 0.1219 | 0.9850 | 0.9894 | |
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| 0.006 | 9.0 | 3618 | 0.0670 | 0.9907 | 0.9934 | |
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| 0.0067 | 10.0 | 4020 | 0.1036 | 0.9869 | 0.9907 | |
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| 0.0067 | 11.0 | 4422 | 0.1220 | 0.9776 | 0.9838 | |
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| 0.0041 | 12.0 | 4824 | 0.1768 | 0.9776 | 0.9839 | |
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| 0.0007 | 13.0 | 5226 | 0.0943 | 0.9888 | 0.9920 | |
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| 0.0 | 14.0 | 5628 | 0.0959 | 0.9907 | 0.9934 | |
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| 0.0054 | 15.0 | 6030 | 0.0915 | 0.9888 | 0.9921 | |
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| 0.0054 | 16.0 | 6432 | 0.1618 | 0.9794 | 0.9855 | |
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| 0.0019 | 17.0 | 6834 | 0.0794 | 0.9907 | 0.9934 | |
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| 0.0 | 18.0 | 7236 | 0.0799 | 0.9907 | 0.9934 | |
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| 0.0 | 19.0 | 7638 | 0.0797 | 0.9907 | 0.9934 | |
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| 0.0 | 20.0 | 8040 | 0.0798 | 0.9907 | 0.9934 | |
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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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