| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| base_model: microsoft/deberta-v3-large |
| model-index: |
| - name: deberta-v3-large__sst2__train-16-9 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # deberta-v3-large__sst2__train-16-9 |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.2598 |
| - Accuracy: 0.7809 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 50 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.6887 | 1.0 | 7 | 0.7452 | 0.2857 | |
| | 0.6889 | 2.0 | 14 | 0.7988 | 0.2857 | |
| | 0.6501 | 3.0 | 21 | 0.8987 | 0.2857 | |
| | 0.4286 | 4.0 | 28 | 0.9186 | 0.4286 | |
| | 0.3591 | 5.0 | 35 | 0.5566 | 0.7143 | |
| | 0.0339 | 6.0 | 42 | 1.1130 | 0.5714 | |
| | 0.013 | 7.0 | 49 | 1.8296 | 0.7143 | |
| | 0.0041 | 8.0 | 56 | 1.7069 | 0.7143 | |
| | 0.0023 | 9.0 | 63 | 1.1942 | 0.7143 | |
| | 0.0022 | 10.0 | 70 | 0.6054 | 0.7143 | |
| | 0.0011 | 11.0 | 77 | 0.3872 | 0.7143 | |
| | 0.0006 | 12.0 | 84 | 0.3217 | 0.7143 | |
| | 0.0005 | 13.0 | 91 | 0.2879 | 0.8571 | |
| | 0.0005 | 14.0 | 98 | 0.2640 | 0.8571 | |
| | 0.0004 | 15.0 | 105 | 0.2531 | 0.8571 | |
| | 0.0003 | 16.0 | 112 | 0.2384 | 0.8571 | |
| | 0.0004 | 17.0 | 119 | 0.2338 | 0.8571 | |
| | 0.0003 | 18.0 | 126 | 0.2314 | 0.8571 | |
| | 0.0003 | 19.0 | 133 | 0.2276 | 0.8571 | |
| | 0.0003 | 20.0 | 140 | 0.2172 | 0.8571 | |
| | 0.0003 | 21.0 | 147 | 0.2069 | 0.8571 | |
| | 0.0002 | 22.0 | 154 | 0.2018 | 0.8571 | |
| | 0.0002 | 23.0 | 161 | 0.2005 | 0.8571 | |
| | 0.0002 | 24.0 | 168 | 0.1985 | 0.8571 | |
| | 0.0002 | 25.0 | 175 | 0.1985 | 1.0 | |
| | 0.0002 | 26.0 | 182 | 0.1955 | 1.0 | |
| | 0.0002 | 27.0 | 189 | 0.1967 | 1.0 | |
| | 0.0002 | 28.0 | 196 | 0.1918 | 1.0 | |
| | 0.0002 | 29.0 | 203 | 0.1888 | 1.0 | |
| | 0.0002 | 30.0 | 210 | 0.1864 | 1.0 | |
| | 0.0002 | 31.0 | 217 | 0.1870 | 1.0 | |
| | 0.0002 | 32.0 | 224 | 0.1892 | 1.0 | |
| | 0.0002 | 33.0 | 231 | 0.1917 | 1.0 | |
| | 0.0002 | 34.0 | 238 | 0.1869 | 1.0 | |
| | 0.0002 | 35.0 | 245 | 0.1812 | 1.0 | |
| | 0.0001 | 36.0 | 252 | 0.1777 | 1.0 | |
| | 0.0002 | 37.0 | 259 | 0.1798 | 1.0 | |
| | 0.0002 | 38.0 | 266 | 0.1824 | 0.8571 | |
| | 0.0002 | 39.0 | 273 | 0.1846 | 0.8571 | |
| | 0.0002 | 40.0 | 280 | 0.1839 | 0.8571 | |
| | 0.0001 | 41.0 | 287 | 0.1826 | 0.8571 | |
| | 0.0001 | 42.0 | 294 | 0.1779 | 0.8571 | |
| | 0.0002 | 43.0 | 301 | 0.1762 | 0.8571 | |
| | 0.0001 | 44.0 | 308 | 0.1742 | 1.0 | |
| | 0.0002 | 45.0 | 315 | 0.1708 | 1.0 | |
| | 0.0001 | 46.0 | 322 | 0.1702 | 1.0 | |
| | 0.0001 | 47.0 | 329 | 0.1699 | 1.0 | |
| | 0.0001 | 48.0 | 336 | 0.1695 | 1.0 | |
| | 0.0001 | 49.0 | 343 | 0.1683 | 1.0 | |
| | 0.0001 | 50.0 | 350 | 0.1681 | 1.0 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.15.0 |
| - Pytorch 1.10.2+cu102 |
| - Datasets 1.18.2 |
| - Tokenizers 0.10.3 |
|
|
