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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: output1 |
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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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# output1 |
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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: 0.7690 |
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- Accuracy: 0.676 |
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- Macro F1: 0.6761 |
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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: 6e-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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| 1.5278 | 0.2286 | 100 | 1.1249 | 0.5146 | 0.4600 | |
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| 0.9452 | 0.4571 | 200 | 0.8437 | 0.645 | 0.6425 | |
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| 0.8367 | 0.6857 | 300 | 0.8038 | 0.6477 | 0.6531 | |
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| 0.8092 | 0.9143 | 400 | 0.7801 | 0.6593 | 0.6611 | |
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| 0.7679 | 1.1429 | 500 | 0.7868 | 0.6717 | 0.6697 | |
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| 0.7451 | 1.3714 | 600 | 0.7711 | 0.6647 | 0.6645 | |
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| 0.7467 | 1.6 | 700 | 0.7646 | 0.6659 | 0.6649 | |
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| 0.7261 | 1.8286 | 800 | 0.7840 | 0.6649 | 0.6632 | |
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| 0.7305 | 2.0571 | 900 | 0.7755 | 0.6681 | 0.6707 | |
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| 0.6742 | 2.2857 | 1000 | 0.7719 | 0.6691 | 0.6707 | |
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| 0.6728 | 2.5143 | 1100 | 0.7640 | 0.6726 | 0.6726 | |
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| 0.6691 | 2.7429 | 1200 | 0.7759 | 0.6761 | 0.6783 | |
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| 0.677 | 2.9714 | 1300 | 0.7690 | 0.676 | 0.6761 | |
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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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