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--- |
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language: |
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- en |
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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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datasets: |
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- tmnam20/VieGLUE |
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metrics: |
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- accuracy |
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model-index: |
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- name: mdeberta-v3-base-qnli-100 |
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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: tmnam20/VieGLUE/QNLI |
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type: tmnam20/VieGLUE |
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config: qnli |
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split: validation |
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args: qnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8974922203917262 |
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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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# mdeberta-v3-base-qnli-100 |
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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 tmnam20/VieGLUE/QNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2906 |
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- Accuracy: 0.8975 |
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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: 32 |
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- eval_batch_size: 16 |
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- seed: 100 |
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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: 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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| 0.3773 | 0.15 | 500 | 0.3870 | 0.8431 | |
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| 0.3547 | 0.31 | 1000 | 0.3175 | 0.8658 | |
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| 0.3385 | 0.46 | 1500 | 0.2986 | 0.8739 | |
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| 0.342 | 0.61 | 2000 | 0.2787 | 0.8845 | |
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| 0.3003 | 0.76 | 2500 | 0.3075 | 0.8726 | |
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| 0.3298 | 0.92 | 3000 | 0.2781 | 0.8807 | |
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| 0.2475 | 1.07 | 3500 | 0.2695 | 0.8942 | |
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| 0.2441 | 1.22 | 4000 | 0.2615 | 0.8940 | |
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| 0.249 | 1.37 | 4500 | 0.2548 | 0.8958 | |
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| 0.2261 | 1.53 | 5000 | 0.2588 | 0.8946 | |
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| 0.2348 | 1.68 | 5500 | 0.2587 | 0.8982 | |
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| 0.2626 | 1.83 | 6000 | 0.2581 | 0.8982 | |
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| 0.2463 | 1.99 | 6500 | 0.2520 | 0.8964 | |
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| 0.1768 | 2.14 | 7000 | 0.2795 | 0.8951 | |
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| 0.1768 | 2.29 | 7500 | 0.3069 | 0.8942 | |
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| 0.1752 | 2.44 | 8000 | 0.2783 | 0.8971 | |
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| 0.1687 | 2.6 | 8500 | 0.2900 | 0.8995 | |
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| 0.163 | 2.75 | 9000 | 0.2828 | 0.8969 | |
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| 0.1547 | 2.9 | 9500 | 0.2873 | 0.8980 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.2.0.dev20231203+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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