---
license: mit
base_model: papluca/xlm-roberta-base-language-detection
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-language-detection-finetuned
  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. -->

# xlm-roberta-base-language-detection-finetuned

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: 0.1662
- Accuracy: 0.9619
- F1: 0.9619

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


### Framework versions

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