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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_1 |
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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_1 |
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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: 0.3748 |
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- Accuracy: 0.961 |
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- Precision: 0.9615 |
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- Recall: 0.961 |
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- F1: 0.9610 |
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- Ratio: 0.483 |
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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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| 2.9367 | 0.0533 | 10 | 1.4668 | 0.65 | 0.7117 | 0.65 | 0.6225 | 0.77 | |
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| 1.1009 | 0.1067 | 20 | 0.6674 | 0.856 | 0.8560 | 0.856 | 0.8560 | 0.502 | |
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| 0.6993 | 0.16 | 30 | 0.5583 | 0.908 | 0.9095 | 0.9080 | 0.9079 | 0.53 | |
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| 0.6377 | 0.2133 | 40 | 0.4923 | 0.934 | 0.9343 | 0.9340 | 0.9340 | 0.486 | |
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| 0.5192 | 0.2667 | 50 | 0.4930 | 0.926 | 0.9282 | 0.9260 | 0.9259 | 0.464 | |
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| 0.5189 | 0.32 | 60 | 0.4687 | 0.937 | 0.9383 | 0.937 | 0.9370 | 0.527 | |
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| 0.5083 | 0.3733 | 70 | 0.4321 | 0.944 | 0.9445 | 0.944 | 0.9440 | 0.484 | |
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| 0.4645 | 0.4267 | 80 | 0.4026 | 0.949 | 0.9490 | 0.949 | 0.9490 | 0.495 | |
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| 0.4268 | 0.48 | 90 | 0.3990 | 0.949 | 0.9498 | 0.9490 | 0.9490 | 0.479 | |
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| 0.4327 | 0.5333 | 100 | 0.3949 | 0.952 | 0.9524 | 0.952 | 0.9520 | 0.486 | |
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| 0.4283 | 0.5867 | 110 | 0.3894 | 0.954 | 0.9540 | 0.954 | 0.9540 | 0.496 | |
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| 0.4263 | 0.64 | 120 | 0.3829 | 0.957 | 0.9572 | 0.957 | 0.9570 | 0.489 | |
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| 0.4205 | 0.6933 | 130 | 0.3800 | 0.962 | 0.9620 | 0.962 | 0.9620 | 0.496 | |
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| 0.4291 | 0.7467 | 140 | 0.3760 | 0.962 | 0.9620 | 0.962 | 0.9620 | 0.502 | |
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| 0.4124 | 0.8 | 150 | 0.3723 | 0.964 | 0.9641 | 0.964 | 0.9640 | 0.494 | |
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| 0.4142 | 0.8533 | 160 | 0.3720 | 0.964 | 0.9641 | 0.964 | 0.9640 | 0.492 | |
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| 0.4209 | 0.9067 | 170 | 0.3767 | 0.96 | 0.9605 | 0.96 | 0.9600 | 0.484 | |
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| 0.3908 | 0.96 | 180 | 0.3748 | 0.96 | 0.9605 | 0.96 | 0.9600 | 0.484 | |
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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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