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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-sst2-100
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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/SST2
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type: tmnam20/VieGLUE
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config: sst2
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split: validation
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args: sst2
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8944954128440367
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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-sst2-100
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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/SST2 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3880
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- Accuracy: 0.8945
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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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| 0.3414 | 0.24 | 500 | 0.3477 | 0.8681 |
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| 0.2858 | 0.48 | 1000 | 0.3121 | 0.8911 |
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| 0.2358 | 0.71 | 1500 | 0.3466 | 0.8807 |
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| 0.2413 | 0.95 | 2000 | 0.3225 | 0.8819 |
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| 0.1722 | 1.19 | 2500 | 0.3268 | 0.8933 |
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| 0.1926 | 1.43 | 3000 | 0.3712 | 0.8899 |
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| 0.1766 | 1.66 | 3500 | 0.3130 | 0.9014 |
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| 0.1706 | 1.9 | 4000 | 0.3517 | 0.8899 |
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| 0.1308 | 2.14 | 4500 | 0.3970 | 0.9014 |
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| 0.1315 | 2.38 | 5000 | 0.3525 | 0.8991 |
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| 0.1504 | 2.61 | 5500 | 0.3728 | 0.8968 |
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| 0.1178 | 2.85 | 6000 | 0.3987 | 0.8922 |
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### Framework versions
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- Transformers 4.36.0
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- Pytorch 2.1.0+cu121
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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