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README.md
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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-10
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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: 0.8984074684239429
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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-10
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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: 0.2859
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- Accuracy: 0.8984
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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: 10
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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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| 0.3968 | 0.15 | 500 | 0.3264 | 0.8623 |
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| 0.3826 | 0.31 | 1000 | 0.2996 | 0.8774 |
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| 0.3478 | 0.46 | 1500 | 0.2894 | 0.8845 |
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| 0.2959 | 0.61 | 2000 | 0.2745 | 0.8883 |
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| 0.3228 | 0.76 | 2500 | 0.2640 | 0.8905 |
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| 0.2899 | 0.92 | 3000 | 0.2723 | 0.8925 |
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| 0.2269 | 1.07 | 3500 | 0.2850 | 0.8935 |
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| 0.2614 | 1.22 | 4000 | 0.2607 | 0.8984 |
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| 0.2508 | 1.37 | 4500 | 0.2831 | 0.8878 |
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| 0.2563 | 1.53 | 5000 | 0.2556 | 0.8960 |
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| 0.2485 | 1.68 | 5500 | 0.2618 | 0.9019 |
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| 0.2373 | 1.83 | 6000 | 0.2600 | 0.8953 |
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| 0.2361 | 1.99 | 6500 | 0.2545 | 0.9023 |
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| 0.162 | 2.14 | 7000 | 0.3093 | 0.8997 |
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| 0.2115 | 2.29 | 7500 | 0.2685 | 0.9010 |
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| 0.176 | 2.44 | 8000 | 0.2966 | 0.8982 |
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| 0.2047 | 2.6 | 8500 | 0.2767 | 0.8982 |
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| 0.1831 | 2.75 | 9000 | 0.2918 | 0.8968 |
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| 0.1818 | 2.9 | 9500 | 0.2818 | 0.8979 |
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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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