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--- |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: roberta-mqa |
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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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# roberta-mqa |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4631 |
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- Accuracy: 0.3793 |
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- F1: 0.3774 |
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- Precision: 0.3819 |
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- Recall: 0.3760 |
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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: 28 |
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- eval_batch_size: 28 |
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- seed: 42 |
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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 | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.5076 | 1.0 | 1061 | 1.4901 | 0.3372 | 0.3328 | 0.3366 | 0.3321 | |
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| 1.4244 | 2.0 | 2122 | 1.4584 | 0.3594 | 0.3560 | 0.3615 | 0.3545 | |
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| 1.3553 | 3.0 | 3183 | 1.4631 | 0.3793 | 0.3774 | 0.3819 | 0.3760 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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