End of training
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- runs/Jul05_10-26-50_cfaea0d34aeb/events.out.tfevents.1720175213.cfaea0d34aeb.278.2 +3 -0
- runs/Jul05_10-27-33_cfaea0d34aeb/events.out.tfevents.1720175260.cfaea0d34aeb.278.3 +3 -0
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- runs/Jul05_10-34-54_cfaea0d34aeb/events.out.tfevents.1720175831.cfaea0d34aeb.278.7 +3 -0
- tokenizer.json +2 -2
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Precision: 0.0
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- Recall: 0.0
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- F1: 0.0
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- Accuracy: 0.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 8
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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:
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### Training results
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| Training Loss | Epoch
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### Framework versions
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6324
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- Precision: 0.0
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- Recall: 0.0
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- F1: 0.0
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- Accuracy: 0.7314
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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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: 15
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.5604 | 1.0 | 12 | 0.2449 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.141 | 2.0 | 24 | 0.2360 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.1111 | 3.0 | 36 | 0.2263 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.0939 | 4.0 | 48 | 0.1960 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.1069 | 5.0 | 60 | 0.1867 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.0818 | 6.0 | 72 | 0.1879 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.0614 | 7.0 | 84 | 0.1510 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.0622 | 8.0 | 96 | 0.1348 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.0633 | 9.0 | 108 | 0.1674 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.0427 | 10.0 | 120 | 0.1632 | 0.0 | 0.0 | 0.0 | 0.9593 |
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| 0.0389 | 11.0 | 132 | 0.1546 | 0.0 | 0.0 | 0.0 | 0.9590 |
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| 0.0344 | 12.0 | 144 | 0.1754 | 0.125 | 0.0312 | 0.05 | 0.9618 |
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| 0.0342 | 13.0 | 156 | 0.1702 | 0.0 | 0.0 | 0.0 | 0.9611 |
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| 0.0272 | 14.0 | 168 | 0.1739 | 0.0526 | 0.0312 | 0.0392 | 0.9623 |
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| 0.0224 | 15.0 | 180 | 0.1759 | 0.0526 | 0.0312 | 0.0392 | 0.9630 |
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### Framework versions
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