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--- |
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license: apache-2.0 |
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base_model: projecte-aina/roberta-base-ca-v2-cased-te |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: SYN_300524_epoch_5 |
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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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# SYN_300524_epoch_5 |
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This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3372 |
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- Accuracy: 0.98 |
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- Precision: 0.9803 |
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- Recall: 0.98 |
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- F1: 0.9800 |
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- Ratio: 0.488 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 47 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_ratio: 0.06 |
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- lr_scheduler_warmup_steps: 4 |
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- num_epochs: 1 |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----:| |
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| 0.3174 | 0.0533 | 10 | 0.3307 | 0.984 | 0.9840 | 0.984 | 0.9840 | 0.496 | |
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| 0.3202 | 0.1067 | 20 | 0.3258 | 0.986 | 0.9861 | 0.986 | 0.9860 | 0.494 | |
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| 0.3016 | 0.16 | 30 | 0.3282 | 0.986 | 0.9860 | 0.986 | 0.9860 | 0.504 | |
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| 0.3291 | 0.2133 | 40 | 0.3495 | 0.977 | 0.9774 | 0.977 | 0.9770 | 0.485 | |
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| 0.2942 | 0.2667 | 50 | 0.3602 | 0.973 | 0.9738 | 0.973 | 0.9730 | 0.479 | |
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| 0.3121 | 0.32 | 60 | 0.3586 | 0.973 | 0.9731 | 0.973 | 0.9730 | 0.493 | |
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| 0.3226 | 0.3733 | 70 | 0.3736 | 0.968 | 0.9681 | 0.968 | 0.9680 | 0.508 | |
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| 0.3226 | 0.4267 | 80 | 0.3515 | 0.979 | 0.9791 | 0.979 | 0.9790 | 0.493 | |
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| 0.3265 | 0.48 | 90 | 0.3697 | 0.97 | 0.9706 | 0.97 | 0.9700 | 0.482 | |
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| 0.3424 | 0.5333 | 100 | 0.3650 | 0.971 | 0.9717 | 0.971 | 0.9710 | 0.481 | |
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| 0.3348 | 0.5867 | 110 | 0.3502 | 0.976 | 0.9760 | 0.976 | 0.9760 | 0.496 | |
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| 0.3393 | 0.64 | 120 | 0.3441 | 0.978 | 0.9780 | 0.978 | 0.9780 | 0.496 | |
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| 0.3421 | 0.6933 | 130 | 0.3397 | 0.979 | 0.9791 | 0.979 | 0.9790 | 0.493 | |
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| 0.3319 | 0.7467 | 140 | 0.3412 | 0.979 | 0.9791 | 0.979 | 0.9790 | 0.493 | |
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| 0.3554 | 0.8 | 150 | 0.3416 | 0.977 | 0.9772 | 0.977 | 0.9770 | 0.489 | |
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| 0.3829 | 0.8533 | 160 | 0.3428 | 0.978 | 0.9785 | 0.978 | 0.9780 | 0.484 | |
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| 0.3631 | 0.9067 | 170 | 0.3396 | 0.979 | 0.9793 | 0.979 | 0.9790 | 0.487 | |
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| 0.3362 | 0.96 | 180 | 0.3376 | 0.98 | 0.9803 | 0.98 | 0.9800 | 0.488 | |
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### Framework versions |
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- Transformers 4.41.1 |
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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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