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--- |
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license: mit |
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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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model-index: |
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- name: deberta-v3-large__sst2__train-8-6 |
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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-large__sst2__train-8-6 |
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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: 1.4331 |
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- Accuracy: 0.7106 |
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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: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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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- num_epochs: 50 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6486 | 1.0 | 3 | 0.7901 | 0.25 | |
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| 0.6418 | 2.0 | 6 | 0.9259 | 0.25 | |
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| 0.6169 | 3.0 | 9 | 1.0574 | 0.25 | |
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| 0.5639 | 4.0 | 12 | 1.1372 | 0.25 | |
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| 0.4562 | 5.0 | 15 | 0.6090 | 0.5 | |
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| 0.3105 | 6.0 | 18 | 0.4435 | 1.0 | |
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| 0.2303 | 7.0 | 21 | 0.2804 | 1.0 | |
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| 0.1388 | 8.0 | 24 | 0.2205 | 1.0 | |
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| 0.0918 | 9.0 | 27 | 0.1282 | 1.0 | |
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| 0.0447 | 10.0 | 30 | 0.0643 | 1.0 | |
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| 0.0297 | 11.0 | 33 | 0.0361 | 1.0 | |
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| 0.0159 | 12.0 | 36 | 0.0211 | 1.0 | |
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| 0.0102 | 13.0 | 39 | 0.0155 | 1.0 | |
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| 0.0061 | 14.0 | 42 | 0.0158 | 1.0 | |
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| 0.0049 | 15.0 | 45 | 0.0189 | 1.0 | |
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| 0.0035 | 16.0 | 48 | 0.0254 | 1.0 | |
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| 0.0027 | 17.0 | 51 | 0.0305 | 1.0 | |
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| 0.0021 | 18.0 | 54 | 0.0287 | 1.0 | |
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| 0.0016 | 19.0 | 57 | 0.0215 | 1.0 | |
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| 0.0016 | 20.0 | 60 | 0.0163 | 1.0 | |
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| 0.0014 | 21.0 | 63 | 0.0138 | 1.0 | |
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| 0.0015 | 22.0 | 66 | 0.0131 | 1.0 | |
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| 0.001 | 23.0 | 69 | 0.0132 | 1.0 | |
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| 0.0014 | 24.0 | 72 | 0.0126 | 1.0 | |
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| 0.0011 | 25.0 | 75 | 0.0125 | 1.0 | |
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| 0.001 | 26.0 | 78 | 0.0119 | 1.0 | |
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| 0.0008 | 27.0 | 81 | 0.0110 | 1.0 | |
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| 0.0007 | 28.0 | 84 | 0.0106 | 1.0 | |
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| 0.0008 | 29.0 | 87 | 0.0095 | 1.0 | |
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| 0.0009 | 30.0 | 90 | 0.0089 | 1.0 | |
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| 0.0008 | 31.0 | 93 | 0.0083 | 1.0 | |
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| 0.0007 | 32.0 | 96 | 0.0075 | 1.0 | |
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| 0.0008 | 33.0 | 99 | 0.0066 | 1.0 | |
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| 0.0006 | 34.0 | 102 | 0.0059 | 1.0 | |
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| 0.0007 | 35.0 | 105 | 0.0054 | 1.0 | |
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| 0.0008 | 36.0 | 108 | 0.0051 | 1.0 | |
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| 0.0007 | 37.0 | 111 | 0.0049 | 1.0 | |
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| 0.0007 | 38.0 | 114 | 0.0047 | 1.0 | |
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| 0.0006 | 39.0 | 117 | 0.0045 | 1.0 | |
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| 0.0006 | 40.0 | 120 | 0.0046 | 1.0 | |
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| 0.0005 | 41.0 | 123 | 0.0045 | 1.0 | |
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| 0.0006 | 42.0 | 126 | 0.0044 | 1.0 | |
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| 0.0006 | 43.0 | 129 | 0.0043 | 1.0 | |
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| 0.0006 | 44.0 | 132 | 0.0044 | 1.0 | |
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| 0.0005 | 45.0 | 135 | 0.0045 | 1.0 | |
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| 0.0006 | 46.0 | 138 | 0.0043 | 1.0 | |
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| 0.0006 | 47.0 | 141 | 0.0043 | 1.0 | |
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| 0.0006 | 48.0 | 144 | 0.0041 | 1.0 | |
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| 0.0007 | 49.0 | 147 | 0.0042 | 1.0 | |
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| 0.0005 | 50.0 | 150 | 0.0042 | 1.0 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2 |
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- Tokenizers 0.10.3 |
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