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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-cl-cardiff_cl_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-cl-cardiff_cl_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: 4.9878 |
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- Accuracy: 0.5278 |
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- F1: 0.5292 |
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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 | 1.09 | 250 | 1.1991 | 0.5154 | 0.5166 | |
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| 0.731 | 2.17 | 500 | 1.5346 | 0.5316 | 0.5279 | |
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| 0.731 | 3.26 | 750 | 1.6658 | 0.5255 | 0.5251 | |
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| 0.3491 | 4.35 | 1000 | 1.9635 | 0.5185 | 0.5189 | |
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| 0.3491 | 5.43 | 1250 | 2.1732 | 0.5231 | 0.5221 | |
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| 0.1838 | 6.52 | 1500 | 3.0035 | 0.5239 | 0.5256 | |
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| 0.1838 | 7.61 | 1750 | 2.9315 | 0.5239 | 0.5258 | |
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| 0.122 | 8.7 | 2000 | 2.8799 | 0.5039 | 0.5009 | |
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| 0.122 | 9.78 | 2250 | 3.0551 | 0.5023 | 0.5037 | |
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| 0.0746 | 10.87 | 2500 | 3.2668 | 0.5262 | 0.5279 | |
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| 0.0746 | 11.96 | 2750 | 3.3828 | 0.5046 | 0.5062 | |
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| 0.0434 | 13.04 | 3000 | 3.8937 | 0.4954 | 0.4929 | |
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| 0.0434 | 14.13 | 3250 | 3.7629 | 0.5224 | 0.5235 | |
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| 0.0369 | 15.22 | 3500 | 4.1508 | 0.4931 | 0.4880 | |
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| 0.0369 | 16.3 | 3750 | 4.2268 | 0.5239 | 0.5240 | |
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| 0.0186 | 17.39 | 4000 | 4.3692 | 0.5054 | 0.5057 | |
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| 0.0186 | 18.48 | 4250 | 4.3635 | 0.5108 | 0.5108 | |
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| 0.0156 | 19.57 | 4500 | 4.4833 | 0.5062 | 0.5039 | |
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| 0.0156 | 20.65 | 4750 | 4.5300 | 0.5039 | 0.5043 | |
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| 0.0093 | 21.74 | 5000 | 4.5612 | 0.5239 | 0.5236 | |
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| 0.0093 | 22.83 | 5250 | 4.7381 | 0.5208 | 0.5216 | |
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| 0.0088 | 23.91 | 5500 | 4.6106 | 0.5324 | 0.5334 | |
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| 0.0088 | 25.0 | 5750 | 4.8040 | 0.5255 | 0.5269 | |
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| 0.0039 | 26.09 | 6000 | 4.8616 | 0.5262 | 0.5283 | |
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| 0.0039 | 27.17 | 6250 | 4.9228 | 0.5231 | 0.5247 | |
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| 0.0052 | 28.26 | 6500 | 5.1665 | 0.5008 | 0.5012 | |
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| 0.0052 | 29.35 | 6750 | 4.9878 | 0.5278 | 0.5292 | |
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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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