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--- |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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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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- f1 |
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model-index: |
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- name: scenario-TCR_data-en-cardiff_eng_only |
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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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# scenario-TCR_data-en-cardiff_eng_only |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.5981 |
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- Accuracy: 0.5798 |
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- F1: 0.5830 |
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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: 66 |
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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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- num_epochs: 30 |
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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 | 1.03 | 60 | 1.0880 | 0.5295 | 0.5251 | |
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| No log | 2.07 | 120 | 1.0869 | 0.5617 | 0.5392 | |
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| No log | 3.1 | 180 | 1.1333 | 0.5789 | 0.5818 | |
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| No log | 4.14 | 240 | 1.2897 | 0.5728 | 0.5743 | |
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| No log | 5.17 | 300 | 1.4495 | 0.5899 | 0.5944 | |
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| No log | 6.21 | 360 | 1.9107 | 0.5573 | 0.5582 | |
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| No log | 7.24 | 420 | 1.8983 | 0.5851 | 0.5883 | |
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| No log | 8.28 | 480 | 2.1481 | 0.5816 | 0.5838 | |
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| 0.4492 | 9.31 | 540 | 2.1906 | 0.5697 | 0.5681 | |
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| 0.4492 | 10.34 | 600 | 2.4558 | 0.5692 | 0.5658 | |
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| 0.4492 | 11.38 | 660 | 2.2698 | 0.5891 | 0.5917 | |
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| 0.4492 | 12.41 | 720 | 2.6192 | 0.5816 | 0.5832 | |
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| 0.4492 | 13.45 | 780 | 2.8040 | 0.5825 | 0.5866 | |
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| 0.4492 | 14.48 | 840 | 3.0573 | 0.5754 | 0.5790 | |
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| 0.4492 | 15.52 | 900 | 2.8448 | 0.5847 | 0.5872 | |
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| 0.4492 | 16.55 | 960 | 3.2238 | 0.5829 | 0.5874 | |
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| 0.0555 | 17.59 | 1020 | 3.2796 | 0.5811 | 0.5852 | |
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| 0.0555 | 18.62 | 1080 | 3.2371 | 0.5869 | 0.5878 | |
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| 0.0555 | 19.66 | 1140 | 3.4683 | 0.5802 | 0.5831 | |
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| 0.0555 | 20.69 | 1200 | 3.4679 | 0.5772 | 0.5793 | |
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| 0.0555 | 21.72 | 1260 | 3.4337 | 0.5877 | 0.5912 | |
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| 0.0555 | 22.76 | 1320 | 3.5059 | 0.5763 | 0.5792 | |
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| 0.0555 | 23.79 | 1380 | 3.6144 | 0.5807 | 0.5851 | |
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| 0.0555 | 24.83 | 1440 | 3.5076 | 0.5847 | 0.5874 | |
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| 0.0086 | 25.86 | 1500 | 3.5835 | 0.5842 | 0.5878 | |
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| 0.0086 | 26.9 | 1560 | 3.5517 | 0.5847 | 0.5872 | |
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| 0.0086 | 27.93 | 1620 | 3.6182 | 0.5825 | 0.5855 | |
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| 0.0086 | 28.97 | 1680 | 3.5885 | 0.5816 | 0.5847 | |
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| 0.0086 | 30.0 | 1740 | 3.5981 | 0.5798 | 0.5830 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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