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--- |
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license: mit |
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base_model: papluca/xlm-roberta-base-language-detection |
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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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model-index: |
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- name: xlm-roberta-base-language-detection-finetuned |
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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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# xlm-roberta-base-language-detection-finetuned |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1662 |
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- Accuracy: 0.9619 |
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- F1: 0.9619 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 0.14 | 50 | 0.2578 | 0.9137 | 0.9135 | |
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| No log | 0.28 | 100 | 0.2252 | 0.9294 | 0.9294 | |
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| No log | 0.42 | 150 | 0.2141 | 0.9350 | 0.9351 | |
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| No log | 0.56 | 200 | 0.1996 | 0.9394 | 0.9395 | |
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| No log | 0.69 | 250 | 0.1767 | 0.9451 | 0.9451 | |
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| No log | 0.83 | 300 | 0.1669 | 0.9476 | 0.9477 | |
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| No log | 0.97 | 350 | 0.1935 | 0.9479 | 0.9479 | |
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| 0.2195 | 1.11 | 400 | 0.1823 | 0.9504 | 0.9505 | |
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| 0.2195 | 1.25 | 450 | 0.1641 | 0.9498 | 0.9499 | |
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| 0.2195 | 1.39 | 500 | 0.1906 | 0.9529 | 0.9530 | |
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| 0.2195 | 1.53 | 550 | 0.1868 | 0.9481 | 0.9483 | |
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| 0.2195 | 1.67 | 600 | 0.1581 | 0.9557 | 0.9557 | |
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| 0.2195 | 1.81 | 650 | 0.1539 | 0.9518 | 0.9518 | |
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| 0.2195 | 1.94 | 700 | 0.1476 | 0.9579 | 0.9580 | |
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| 0.1469 | 2.08 | 750 | 0.1500 | 0.9557 | 0.9558 | |
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| 0.1469 | 2.22 | 800 | 0.1645 | 0.9571 | 0.9571 | |
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| 0.1469 | 2.36 | 850 | 0.1470 | 0.9579 | 0.9580 | |
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| 0.1469 | 2.5 | 900 | 0.1506 | 0.9521 | 0.9522 | |
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| 0.1469 | 2.64 | 950 | 0.1511 | 0.9574 | 0.9574 | |
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| 0.1469 | 2.78 | 1000 | 0.1553 | 0.9596 | 0.9596 | |
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| 0.1469 | 2.92 | 1050 | 0.1467 | 0.9557 | 0.9558 | |
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| 0.1247 | 3.06 | 1100 | 0.1676 | 0.9579 | 0.9580 | |
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| 0.1247 | 3.19 | 1150 | 0.1508 | 0.9535 | 0.9536 | |
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| 0.1247 | 3.33 | 1200 | 0.1404 | 0.9563 | 0.9564 | |
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| 0.1247 | 3.47 | 1250 | 0.1394 | 0.9619 | 0.9619 | |
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| 0.1247 | 3.61 | 1300 | 0.1439 | 0.9644 | 0.9644 | |
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| 0.1247 | 3.75 | 1350 | 0.1444 | 0.9591 | 0.9591 | |
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| 0.1247 | 3.89 | 1400 | 0.1495 | 0.9577 | 0.9578 | |
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| 0.1082 | 4.03 | 1450 | 0.1361 | 0.9608 | 0.9608 | |
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| 0.1082 | 4.17 | 1500 | 0.1531 | 0.9588 | 0.9589 | |
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| 0.1082 | 4.31 | 1550 | 0.1711 | 0.9507 | 0.9508 | |
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| 0.1082 | 4.44 | 1600 | 0.1371 | 0.9585 | 0.9586 | |
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| 0.1082 | 4.58 | 1650 | 0.1408 | 0.9579 | 0.9580 | |
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| 0.1082 | 4.72 | 1700 | 0.1444 | 0.9636 | 0.9636 | |
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| 0.1082 | 4.86 | 1750 | 0.1504 | 0.9613 | 0.9614 | |
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| 0.0972 | 5.0 | 1800 | 0.1315 | 0.9599 | 0.9600 | |
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| 0.0972 | 5.14 | 1850 | 0.1521 | 0.9610 | 0.9611 | |
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| 0.0972 | 5.28 | 1900 | 0.1531 | 0.9577 | 0.9577 | |
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| 0.0972 | 5.42 | 1950 | 0.1534 | 0.9610 | 0.9611 | |
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| 0.0972 | 5.56 | 2000 | 0.1506 | 0.9622 | 0.9622 | |
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| 0.0972 | 5.69 | 2050 | 0.1487 | 0.9610 | 0.9611 | |
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| 0.0972 | 5.83 | 2100 | 0.1541 | 0.9610 | 0.9610 | |
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| 0.0972 | 5.97 | 2150 | 0.1376 | 0.9571 | 0.9572 | |
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| 0.0853 | 6.11 | 2200 | 0.1667 | 0.9588 | 0.9589 | |
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| 0.0853 | 6.25 | 2250 | 0.1548 | 0.9557 | 0.9558 | |
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| 0.0853 | 6.39 | 2300 | 0.1527 | 0.9622 | 0.9622 | |
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| 0.0853 | 6.53 | 2350 | 0.1469 | 0.9619 | 0.9619 | |
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| 0.0853 | 6.67 | 2400 | 0.1510 | 0.9596 | 0.9597 | |
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| 0.0853 | 6.81 | 2450 | 0.1531 | 0.9613 | 0.9613 | |
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| 0.0853 | 6.94 | 2500 | 0.1605 | 0.9619 | 0.9619 | |
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| 0.0784 | 7.08 | 2550 | 0.1740 | 0.9571 | 0.9572 | |
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| 0.0784 | 7.22 | 2600 | 0.1441 | 0.9633 | 0.9633 | |
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| 0.0784 | 7.36 | 2650 | 0.1596 | 0.9633 | 0.9633 | |
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| 0.0784 | 7.5 | 2700 | 0.1469 | 0.9613 | 0.9614 | |
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| 0.0784 | 7.64 | 2750 | 0.1643 | 0.9596 | 0.9597 | |
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| 0.0784 | 7.78 | 2800 | 0.1752 | 0.9619 | 0.9619 | |
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| 0.0784 | 7.92 | 2850 | 0.1591 | 0.9613 | 0.9614 | |
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| 0.0712 | 8.06 | 2900 | 0.1604 | 0.9608 | 0.9608 | |
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| 0.0712 | 8.19 | 2950 | 0.1565 | 0.9596 | 0.9597 | |
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| 0.0712 | 8.33 | 3000 | 0.1601 | 0.9605 | 0.9605 | |
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| 0.0712 | 8.47 | 3050 | 0.1668 | 0.9605 | 0.9605 | |
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| 0.0712 | 8.61 | 3100 | 0.1765 | 0.9624 | 0.9625 | |
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| 0.0712 | 8.75 | 3150 | 0.1616 | 0.9613 | 0.9614 | |
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| 0.0712 | 8.89 | 3200 | 0.1624 | 0.9616 | 0.9616 | |
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| 0.062 | 9.03 | 3250 | 0.1598 | 0.9613 | 0.9613 | |
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| 0.062 | 9.17 | 3300 | 0.1628 | 0.9624 | 0.9625 | |
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| 0.062 | 9.31 | 3350 | 0.1627 | 0.9624 | 0.9625 | |
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| 0.062 | 9.44 | 3400 | 0.1616 | 0.9613 | 0.9613 | |
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| 0.062 | 9.58 | 3450 | 0.1669 | 0.9610 | 0.9611 | |
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| 0.062 | 9.72 | 3500 | 0.1643 | 0.9608 | 0.9608 | |
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| 0.062 | 9.86 | 3550 | 0.1650 | 0.9610 | 0.9611 | |
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| 0.057 | 10.0 | 3600 | 0.1662 | 0.9619 | 0.9619 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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