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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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- f1 |
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model-index: |
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- name: mdeberta-v3-base-qqp-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/QQP |
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type: tmnam20/VieGLUE |
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config: qqp |
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split: validation |
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args: qqp |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8996784565916399 |
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- name: F1 |
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type: f1 |
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value: 0.865810891285648 |
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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-qqp-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/QQP dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2774 |
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- Accuracy: 0.8997 |
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- F1: 0.8658 |
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- Combined Score: 0.8827 |
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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 | F1 | Combined Score | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| |
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| 0.2888 | 0.44 | 5000 | 0.2928 | 0.8740 | 0.8314 | 0.8527 | |
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| 0.2968 | 0.88 | 10000 | 0.2770 | 0.8793 | 0.8325 | 0.8559 | |
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| 0.2365 | 1.32 | 15000 | 0.2894 | 0.8871 | 0.8507 | 0.8689 | |
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| 0.2257 | 1.76 | 20000 | 0.2664 | 0.8941 | 0.8572 | 0.8757 | |
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| 0.1939 | 2.2 | 25000 | 0.2777 | 0.8970 | 0.8617 | 0.8793 | |
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| 0.2001 | 2.64 | 30000 | 0.2762 | 0.8987 | 0.8643 | 0.8815 | |
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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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