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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- generated_from_trainer |
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- deberta-v3 |
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datasets: |
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- glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: ds_results |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: GLUE MNLI |
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type: glue |
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args: mnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.874593165174939 |
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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 (small) fine-tuned on MNLI |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE MNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4985 |
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- Accuracy: 0.8746 |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: 1000 |
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- num_epochs: 3.0 |
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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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| 0.7773 | 0.04 | 1000 | 0.5241 | 0.7984 | |
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| 0.546 | 0.08 | 2000 | 0.4629 | 0.8194 | |
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| 0.5032 | 0.12 | 3000 | 0.4704 | 0.8274 | |
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| 0.4711 | 0.16 | 4000 | 0.4383 | 0.8355 | |
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| 0.473 | 0.2 | 5000 | 0.4652 | 0.8305 | |
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| 0.4619 | 0.24 | 6000 | 0.4234 | 0.8386 | |
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| 0.4542 | 0.29 | 7000 | 0.4825 | 0.8349 | |
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| 0.4468 | 0.33 | 8000 | 0.3985 | 0.8513 | |
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| 0.4288 | 0.37 | 9000 | 0.4084 | 0.8493 | |
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| 0.4354 | 0.41 | 10000 | 0.3850 | 0.8533 | |
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| 0.423 | 0.45 | 11000 | 0.3855 | 0.8509 | |
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| 0.4167 | 0.49 | 12000 | 0.4122 | 0.8513 | |
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| 0.4129 | 0.53 | 13000 | 0.4009 | 0.8550 | |
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| 0.4135 | 0.57 | 14000 | 0.4136 | 0.8544 | |
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| 0.4074 | 0.61 | 15000 | 0.3869 | 0.8595 | |
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| 0.415 | 0.65 | 16000 | 0.3911 | 0.8517 | |
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| 0.4095 | 0.69 | 17000 | 0.3880 | 0.8593 | |
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| 0.4001 | 0.73 | 18000 | 0.3907 | 0.8587 | |
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| 0.4069 | 0.77 | 19000 | 0.3686 | 0.8630 | |
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| 0.3927 | 0.81 | 20000 | 0.4008 | 0.8593 | |
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| 0.3958 | 0.86 | 21000 | 0.3716 | 0.8639 | |
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| 0.4016 | 0.9 | 22000 | 0.3594 | 0.8679 | |
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| 0.3945 | 0.94 | 23000 | 0.3595 | 0.8679 | |
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| 0.3932 | 0.98 | 24000 | 0.3577 | 0.8645 | |
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| 0.345 | 1.02 | 25000 | 0.4080 | 0.8699 | |
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| 0.2885 | 1.06 | 26000 | 0.3919 | 0.8674 | |
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| 0.2858 | 1.1 | 27000 | 0.4346 | 0.8651 | |
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| 0.2872 | 1.14 | 28000 | 0.4105 | 0.8674 | |
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| 0.3002 | 1.18 | 29000 | 0.4133 | 0.8708 | |
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| 0.2954 | 1.22 | 30000 | 0.4062 | 0.8667 | |
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| 0.2912 | 1.26 | 31000 | 0.3972 | 0.8708 | |
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| 0.2958 | 1.3 | 32000 | 0.3713 | 0.8732 | |
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| 0.293 | 1.34 | 33000 | 0.3717 | 0.8715 | |
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| 0.3001 | 1.39 | 34000 | 0.3826 | 0.8716 | |
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| 0.2864 | 1.43 | 35000 | 0.4155 | 0.8694 | |
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| 0.2827 | 1.47 | 36000 | 0.4224 | 0.8666 | |
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| 0.2836 | 1.51 | 37000 | 0.3832 | 0.8744 | |
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| 0.2844 | 1.55 | 38000 | 0.4179 | 0.8699 | |
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| 0.2866 | 1.59 | 39000 | 0.3969 | 0.8681 | |
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| 0.2883 | 1.63 | 40000 | 0.4000 | 0.8683 | |
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| 0.2832 | 1.67 | 41000 | 0.3853 | 0.8688 | |
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| 0.2876 | 1.71 | 42000 | 0.3924 | 0.8677 | |
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| 0.2855 | 1.75 | 43000 | 0.4177 | 0.8719 | |
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| 0.2845 | 1.79 | 44000 | 0.3877 | 0.8724 | |
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| 0.2882 | 1.83 | 45000 | 0.3961 | 0.8713 | |
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| 0.2773 | 1.87 | 46000 | 0.3791 | 0.8740 | |
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| 0.2767 | 1.91 | 47000 | 0.3877 | 0.8779 | |
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| 0.2772 | 1.96 | 48000 | 0.4022 | 0.8690 | |
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| 0.2816 | 2.0 | 49000 | 0.3837 | 0.8732 | |
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| 0.2068 | 2.04 | 50000 | 0.4644 | 0.8720 | |
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| 0.1914 | 2.08 | 51000 | 0.4919 | 0.8744 | |
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| 0.2 | 2.12 | 52000 | 0.4870 | 0.8702 | |
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| 0.1904 | 2.16 | 53000 | 0.5038 | 0.8737 | |
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| 0.1915 | 2.2 | 54000 | 0.5232 | 0.8711 | |
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| 0.1956 | 2.24 | 55000 | 0.5192 | 0.8747 | |
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| 0.1911 | 2.28 | 56000 | 0.5215 | 0.8761 | |
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| 0.2053 | 2.32 | 57000 | 0.4604 | 0.8738 | |
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| 0.2008 | 2.36 | 58000 | 0.5162 | 0.8715 | |
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| 0.1971 | 2.4 | 59000 | 0.4886 | 0.8754 | |
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| 0.192 | 2.44 | 60000 | 0.4921 | 0.8725 | |
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| 0.1937 | 2.49 | 61000 | 0.4917 | 0.8763 | |
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| 0.1931 | 2.53 | 62000 | 0.4789 | 0.8778 | |
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| 0.1964 | 2.57 | 63000 | 0.4997 | 0.8721 | |
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| 0.2008 | 2.61 | 64000 | 0.4748 | 0.8756 | |
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| 0.1962 | 2.65 | 65000 | 0.4840 | 0.8764 | |
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| 0.2029 | 2.69 | 66000 | 0.4889 | 0.8767 | |
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| 0.1927 | 2.73 | 67000 | 0.4820 | 0.8758 | |
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| 0.1926 | 2.77 | 68000 | 0.4857 | 0.8762 | |
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| 0.1919 | 2.81 | 69000 | 0.4836 | 0.8749 | |
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| 0.1911 | 2.85 | 70000 | 0.4859 | 0.8742 | |
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| 0.1897 | 2.89 | 71000 | 0.4853 | 0.8766 | |
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| 0.186 | 2.93 | 72000 | 0.4946 | 0.8768 | |
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| 0.2011 | 2.97 | 73000 | 0.4851 | 0.8767 | |
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### Framework versions |
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- Transformers 4.13.0.dev0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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