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--- |
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license: cc-by-4.0 |
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base_model: strongpear/deberta-large_vMCQ |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: deberta-large_vMCQ |
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results: [] |
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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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# deberta-large_vMCQ |
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This model is a fine-tuned version of [strongpear/deberta-large_vMCQ](https://huggingface.co/strongpear/deberta-large_vMCQ) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4703 |
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- Accuracy: 0.9102 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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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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| 0.2733 | 0.08 | 500 | 0.4367 | 0.9010 | |
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| 0.2226 | 0.17 | 1000 | 0.5558 | 0.9022 | |
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| 0.2971 | 0.25 | 1500 | 0.5317 | 0.8999 | |
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| 0.3168 | 0.33 | 2000 | 0.8198 | 0.9024 | |
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| 0.3367 | 0.42 | 2500 | 0.5744 | 0.9031 | |
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| 0.3089 | 0.5 | 3000 | 0.8358 | 0.9018 | |
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| 0.3796 | 0.58 | 3500 | 0.7308 | 0.9078 | |
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| 0.3663 | 0.67 | 4000 | 0.6393 | 0.9044 | |
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| 0.4152 | 0.75 | 4500 | 0.6098 | 0.9123 | |
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| 0.3905 | 0.83 | 5000 | 0.5029 | 0.9106 | |
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| 0.4446 | 0.91 | 5500 | 0.4821 | 0.9106 | |
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| 0.5263 | 1.0 | 6000 | 0.4703 | 0.9102 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.14.1 |
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