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--- |
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license: mit |
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base_model: microsoft/deberta-v2-xxlarge |
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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: output |
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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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# output |
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This model is a fine-tuned version of [microsoft/deberta-v2-xxlarge](https://huggingface.co/microsoft/deberta-v2-xxlarge) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7550 |
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- Accuracy: 0.6786 |
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- Macro F1: 0.6773 |
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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: 3e-06 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 64 |
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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 |
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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 | Macro F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:| |
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| 1.6193 | 0.2286 | 100 | 1.6018 | 0.2184 | 0.1357 | |
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| 1.305 | 0.4571 | 200 | 0.9285 | 0.591 | 0.5953 | |
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| 0.8772 | 0.6857 | 300 | 0.8561 | 0.6256 | 0.6250 | |
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| 0.8552 | 0.9143 | 400 | 0.8332 | 0.6511 | 0.6473 | |
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| 0.798 | 1.1429 | 500 | 0.8210 | 0.6641 | 0.6579 | |
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| 0.7713 | 1.3714 | 600 | 0.7759 | 0.666 | 0.6669 | |
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| 0.7758 | 1.6 | 700 | 0.7634 | 0.6667 | 0.6615 | |
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| 0.7442 | 1.8286 | 800 | 0.7960 | 0.6613 | 0.6590 | |
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| 0.752 | 2.0571 | 900 | 0.7715 | 0.667 | 0.6690 | |
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| 0.7123 | 2.2857 | 1000 | 0.7600 | 0.6696 | 0.6698 | |
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| 0.7066 | 2.5143 | 1100 | 0.7599 | 0.6701 | 0.6684 | |
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| 0.7024 | 2.7429 | 1200 | 0.7551 | 0.6757 | 0.6763 | |
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| 0.7117 | 2.9714 | 1300 | 0.7550 | 0.6786 | 0.6773 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.2 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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