| --- |
| license: mit |
| base_model: papluca/xlm-roberta-base-language-detection |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: Roberta-CLS-URL |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # Roberta-CLS-URL |
|
|
| This model is a fine-tuned version of [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1817 |
| - Accuracy: 0.9571 |
| - F1: 0.9572 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | No log | 0.14 | 50 | 0.2787 | 0.8943 | 0.8942 | |
| | No log | 0.28 | 100 | 0.2332 | 0.9179 | 0.9180 | |
| | No log | 0.42 | 150 | 0.2369 | 0.9268 | 0.9269 | |
| | No log | 0.56 | 200 | 0.2071 | 0.9313 | 0.9314 | |
| | No log | 0.69 | 250 | 0.2017 | 0.9344 | 0.9343 | |
| | No log | 0.83 | 300 | 0.1953 | 0.9414 | 0.9415 | |
| | No log | 0.97 | 350 | 0.2031 | 0.9394 | 0.9394 | |
| | 0.2675 | 1.11 | 400 | 0.1915 | 0.9439 | 0.9439 | |
| | 0.2675 | 1.25 | 450 | 0.1696 | 0.9439 | 0.9440 | |
| | 0.2675 | 1.39 | 500 | 0.1747 | 0.9487 | 0.9488 | |
| | 0.2675 | 1.53 | 550 | 0.1958 | 0.9425 | 0.9427 | |
| | 0.2675 | 1.67 | 600 | 0.1608 | 0.9462 | 0.9461 | |
| | 0.2675 | 1.81 | 650 | 0.1547 | 0.9523 | 0.9524 | |
| | 0.2675 | 1.94 | 700 | 0.1668 | 0.9557 | 0.9557 | |
| | 0.1686 | 2.08 | 750 | 0.1709 | 0.9498 | 0.9499 | |
| | 0.1686 | 2.22 | 800 | 0.1605 | 0.9554 | 0.9555 | |
| | 0.1686 | 2.36 | 850 | 0.1703 | 0.9501 | 0.9501 | |
| | 0.1686 | 2.5 | 900 | 0.1603 | 0.9465 | 0.9466 | |
| | 0.1686 | 2.64 | 950 | 0.1742 | 0.9451 | 0.9451 | |
| | 0.1686 | 2.78 | 1000 | 0.1507 | 0.9546 | 0.9546 | |
| | 0.1686 | 2.92 | 1050 | 0.1423 | 0.9557 | 0.9557 | |
| | 0.1385 | 3.06 | 1100 | 0.1496 | 0.9574 | 0.9575 | |
| | 0.1385 | 3.19 | 1150 | 0.1590 | 0.9549 | 0.9549 | |
| | 0.1385 | 3.33 | 1200 | 0.1492 | 0.9523 | 0.9524 | |
| | 0.1385 | 3.47 | 1250 | 0.1390 | 0.9565 | 0.9566 | |
| | 0.1385 | 3.61 | 1300 | 0.1496 | 0.9529 | 0.9530 | |
| | 0.1385 | 3.75 | 1350 | 0.1425 | 0.9551 | 0.9552 | |
| | 0.1385 | 3.89 | 1400 | 0.1494 | 0.9521 | 0.9522 | |
| | 0.1221 | 4.03 | 1450 | 0.1541 | 0.9557 | 0.9557 | |
| | 0.1221 | 4.17 | 1500 | 0.1897 | 0.9532 | 0.9532 | |
| | 0.1221 | 4.31 | 1550 | 0.1595 | 0.9518 | 0.9519 | |
| | 0.1221 | 4.44 | 1600 | 0.1514 | 0.9554 | 0.9555 | |
| | 0.1221 | 4.58 | 1650 | 0.1553 | 0.9554 | 0.9555 | |
| | 0.1221 | 4.72 | 1700 | 0.1626 | 0.9543 | 0.9543 | |
| | 0.1221 | 4.86 | 1750 | 0.1509 | 0.9523 | 0.9523 | |
| | 0.1034 | 5.0 | 1800 | 0.1448 | 0.9554 | 0.9555 | |
| | 0.1034 | 5.14 | 1850 | 0.1685 | 0.9574 | 0.9574 | |
| | 0.1034 | 5.28 | 1900 | 0.1555 | 0.9551 | 0.9552 | |
| | 0.1034 | 5.42 | 1950 | 0.1595 | 0.9557 | 0.9557 | |
| | 0.1034 | 5.56 | 2000 | 0.1660 | 0.9565 | 0.9566 | |
| | 0.1034 | 5.69 | 2050 | 0.1511 | 0.9554 | 0.9555 | |
| | 0.1034 | 5.83 | 2100 | 0.1443 | 0.9565 | 0.9566 | |
| | 0.1034 | 5.97 | 2150 | 0.1526 | 0.9554 | 0.9554 | |
| | 0.0925 | 6.11 | 2200 | 0.1753 | 0.9540 | 0.9541 | |
| | 0.0925 | 6.25 | 2250 | 0.1503 | 0.9557 | 0.9557 | |
| | 0.0925 | 6.39 | 2300 | 0.1827 | 0.9518 | 0.9518 | |
| | 0.0925 | 6.53 | 2350 | 0.1486 | 0.9568 | 0.9568 | |
| | 0.0925 | 6.67 | 2400 | 0.1652 | 0.9568 | 0.9569 | |
| | 0.0925 | 6.81 | 2450 | 0.1544 | 0.9537 | 0.9538 | |
| | 0.0925 | 6.94 | 2500 | 0.1599 | 0.9551 | 0.9552 | |
| | 0.082 | 7.08 | 2550 | 0.1748 | 0.9568 | 0.9569 | |
| | 0.082 | 7.22 | 2600 | 0.1765 | 0.9582 | 0.9583 | |
| | 0.082 | 7.36 | 2650 | 0.1699 | 0.9568 | 0.9569 | |
| | 0.082 | 7.5 | 2700 | 0.1631 | 0.9563 | 0.9563 | |
| | 0.082 | 7.64 | 2750 | 0.1759 | 0.9602 | 0.9602 | |
| | 0.082 | 7.78 | 2800 | 0.1746 | 0.9565 | 0.9566 | |
| | 0.082 | 7.92 | 2850 | 0.1561 | 0.9568 | 0.9569 | |
| | 0.0742 | 8.06 | 2900 | 0.1721 | 0.9577 | 0.9577 | |
| | 0.0742 | 8.19 | 2950 | 0.1877 | 0.9563 | 0.9563 | |
| | 0.0742 | 8.33 | 3000 | 0.1896 | 0.9549 | 0.9549 | |
| | 0.0742 | 8.47 | 3050 | 0.1751 | 0.9577 | 0.9577 | |
| | 0.0742 | 8.61 | 3100 | 0.1812 | 0.9577 | 0.9577 | |
| | 0.0742 | 8.75 | 3150 | 0.1845 | 0.9577 | 0.9577 | |
| | 0.0742 | 8.89 | 3200 | 0.1844 | 0.9579 | 0.9580 | |
| | 0.0659 | 9.03 | 3250 | 0.1963 | 0.9571 | 0.9571 | |
| | 0.0659 | 9.17 | 3300 | 0.1861 | 0.9577 | 0.9577 | |
| | 0.0659 | 9.31 | 3350 | 0.1941 | 0.9585 | 0.9586 | |
| | 0.0659 | 9.44 | 3400 | 0.1900 | 0.9565 | 0.9566 | |
| | 0.0659 | 9.58 | 3450 | 0.1903 | 0.9565 | 0.9566 | |
| | 0.0659 | 9.72 | 3500 | 0.1836 | 0.9579 | 0.9580 | |
| | 0.0659 | 9.86 | 3550 | 0.1818 | 0.9565 | 0.9566 | |
| | 0.0631 | 10.0 | 3600 | 0.1817 | 0.9571 | 0.9572 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.39.3 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
|
|
