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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-1
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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: 0.8998718652754897
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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-1
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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: 0.2782
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- Accuracy: 0.8999
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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: 1
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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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| 0.3768 | 0.15 | 500 | 0.3291 | 0.8596 |
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| 0.3506 | 0.31 | 1000 | 0.2961 | 0.8752 |
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| 0.3417 | 0.46 | 1500 | 0.2917 | 0.8808 |
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| 0.3319 | 0.61 | 2000 | 0.2742 | 0.8871 |
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| 0.3126 | 0.76 | 2500 | 0.2686 | 0.8913 |
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| 0.3073 | 0.92 | 3000 | 0.2639 | 0.8916 |
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| 0.2867 | 1.07 | 3500 | 0.2557 | 0.8958 |
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| 0.2313 | 1.22 | 4000 | 0.2937 | 0.8880 |
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| 0.2364 | 1.37 | 4500 | 0.2585 | 0.8971 |
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| 0.2533 | 1.53 | 5000 | 0.2545 | 0.8938 |
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| 0.2333 | 1.68 | 5500 | 0.2629 | 0.8955 |
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| 0.225 | 1.83 | 6000 | 0.2532 | 0.9002 |
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| 0.2313 | 1.99 | 6500 | 0.2520 | 0.8988 |
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| 0.1793 | 2.14 | 7000 | 0.2819 | 0.8953 |
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| 0.1639 | 2.29 | 7500 | 0.2809 | 0.8964 |
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| 0.1645 | 2.44 | 8000 | 0.2778 | 0.8990 |
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| 0.1753 | 2.6 | 8500 | 0.2802 | 0.8988 |
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| 0.1859 | 2.75 | 9000 | 0.2775 | 0.9001 |
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| 0.1809 | 2.9 | 9500 | 0.2767 | 0.8988 |
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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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