---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: trainer_output
  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. -->

# trainer_output

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1527
- Accuracy: 0.9717
- F1: 0.9716
- Precision: 0.9716
- Recall: 0.9717

## 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
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 573
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1714        | 1.0   | 1147 | 0.1891          | 0.9302   | 0.9308 | 0.9350    | 0.9302 |
| 0.105         | 2.0   | 2294 | 0.1224          | 0.9627   | 0.9625 | 0.9633    | 0.9627 |
| 0.0689        | 3.0   | 3441 | 0.1218          | 0.9695   | 0.9693 | 0.9698    | 0.9695 |
| 0.0341        | 4.0   | 4588 | 0.1358          | 0.9714   | 0.9714 | 0.9715    | 0.9714 |
| 0.0228        | 5.0   | 5735 | 0.1429          | 0.9730   | 0.9729 | 0.9730    | 0.9730 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1