---
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 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2