---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-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-finetuned

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2266
- Accuracy: 0.9541
- F1: 0.9542

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.2327 | 200  | 0.2630          | 0.8904   | 0.8872 |
| No log        | 0.4654 | 400  | 0.2128          | 0.9117   | 0.9129 |
| No log        | 0.6981 | 600  | 0.1844          | 0.9308   | 0.9311 |
| No log        | 0.9308 | 800  | 0.1673          | 0.9346   | 0.9346 |
| No log        | 1.1635 | 1000 | 0.1735          | 0.9346   | 0.9338 |
| No log        | 1.3962 | 1200 | 0.1440          | 0.9433   | 0.9429 |
| No log        | 1.6289 | 1400 | 0.1443          | 0.9467   | 0.9469 |
| No log        | 1.8615 | 1600 | 0.1357          | 0.9504   | 0.9507 |
| 0.2197        | 2.0942 | 1800 | 0.1532          | 0.9469   | 0.9473 |
| 0.2197        | 2.3269 | 2000 | 0.1478          | 0.9496   | 0.9500 |
| 0.2197        | 2.5596 | 2200 | 0.1379          | 0.9501   | 0.9504 |
| 0.2197        | 2.7923 | 2400 | 0.1381          | 0.9511   | 0.9514 |
| 0.2197        | 3.0250 | 2600 | 0.1627          | 0.9493   | 0.9496 |
| 0.2197        | 3.2577 | 2800 | 0.1596          | 0.9546   | 0.9546 |
| 0.2197        | 3.4904 | 3000 | 0.1421          | 0.9526   | 0.9527 |
| 0.2197        | 3.7231 | 3200 | 0.1459          | 0.9539   | 0.9539 |
| 0.2197        | 3.9558 | 3400 | 0.1348          | 0.9495   | 0.9499 |
| 0.1176        | 4.1885 | 3600 | 0.1519          | 0.9501   | 0.9507 |
| 0.1176        | 4.4212 | 3800 | 0.1570          | 0.9525   | 0.9529 |
| 0.1176        | 4.6539 | 4000 | 0.1367          | 0.9511   | 0.9514 |
| 0.1176        | 4.8866 | 4200 | 0.1409          | 0.954    | 0.9541 |
| 0.1176        | 5.1193 | 4400 | 0.1690          | 0.9539   | 0.9541 |
| 0.1176        | 5.3519 | 4600 | 0.1757          | 0.9544   | 0.9545 |
| 0.1176        | 5.5846 | 4800 | 0.1508          | 0.9513   | 0.9518 |
| 0.1176        | 5.8173 | 5000 | 0.1537          | 0.9545   | 0.9546 |
| 0.0849        | 6.0500 | 5200 | 0.1814          | 0.954    | 0.9540 |
| 0.0849        | 6.2827 | 5400 | 0.1674          | 0.9543   | 0.9546 |
| 0.0849        | 6.5154 | 5600 | 0.1923          | 0.9538   | 0.9539 |
| 0.0849        | 6.7481 | 5800 | 0.1750          | 0.9543   | 0.9545 |
| 0.0849        | 6.9808 | 6000 | 0.1890          | 0.9527   | 0.9529 |
| 0.0849        | 7.2135 | 6200 | 0.1999          | 0.9547   | 0.9548 |
| 0.0849        | 7.4462 | 6400 | 0.1722          | 0.9547   | 0.9549 |
| 0.0849        | 7.6789 | 6600 | 0.1693          | 0.9524   | 0.9528 |
| 0.0849        | 7.9116 | 6800 | 0.1848          | 0.9548   | 0.9549 |
| 0.0614        | 8.1443 | 7000 | 0.2067          | 0.9554   | 0.9554 |
| 0.0614        | 8.3770 | 7200 | 0.2073          | 0.9544   | 0.9546 |
| 0.0614        | 8.6097 | 7400 | 0.1929          | 0.9547   | 0.9548 |
| 0.0614        | 8.8424 | 7600 | 0.2081          | 0.9544   | 0.9545 |
| 0.0614        | 9.0750 | 7800 | 0.2031          | 0.9541   | 0.9542 |
| 0.0614        | 9.3077 | 8000 | 0.2232          | 0.9549   | 0.9552 |
| 0.0614        | 9.5404 | 8200 | 0.2238          | 0.9544   | 0.9545 |
| 0.0614        | 9.7731 | 8400 | 0.2266          | 0.9541   | 0.9542 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1