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--- |
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license: mit |
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base_model: microsoft/deberta-v3-large |
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tags: |
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- generated_from_trainer |
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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: BBC_CLS_deberta_v3_large_v2 |
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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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# BBC_CLS_deberta_v3_large_v2 |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co./microsoft/deberta-v3-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0857 |
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- Accuracy: 0.9866 |
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- Precision: 0.9723 |
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- Recall: 0.9780 |
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- F1: 0.9751 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 10 |
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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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| 1.235 | 1.0 | 66 | 0.6331 | 0.7964 | 0.4047 | 0.4873 | 0.4418 | |
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| 0.4336 | 2.0 | 132 | 0.2201 | 0.8971 | 0.6754 | 0.7091 | 0.6910 | |
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| 0.2133 | 3.0 | 198 | 0.0990 | 0.9776 | 0.9476 | 0.9786 | 0.9602 | |
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| 0.1083 | 4.0 | 264 | 0.1038 | 0.9821 | 0.9656 | 0.9651 | 0.9653 | |
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| 0.0848 | 5.0 | 330 | 0.0907 | 0.9866 | 0.9782 | 0.9714 | 0.9747 | |
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| 0.1087 | 6.0 | 396 | 0.1270 | 0.9799 | 0.9672 | 0.9689 | 0.9671 | |
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| 0.1011 | 7.0 | 462 | 0.1289 | 0.9754 | 0.9677 | 0.9660 | 0.9667 | |
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| 0.0827 | 8.0 | 528 | 0.0990 | 0.9799 | 0.9818 | 0.9479 | 0.9632 | |
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| 0.0621 | 9.0 | 594 | 0.0857 | 0.9866 | 0.9723 | 0.9780 | 0.9751 | |
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| 0.0444 | 10.0 | 660 | 0.1071 | 0.9843 | 0.9769 | 0.9663 | 0.9715 | |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 1.13.1 |
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- Datasets 2.13.0 |
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- Tokenizers 0.14.1 |
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