---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: language_detector
  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. -->

# language_detector

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: 0.0157
- Accuracy: 0.9986
- F1 Macro: 0.9985
- Precision Macro: 0.9988
- Recall Macro: 0.9981

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|
| 0.0207        | 1.0   | 3850  | 0.0094          | 0.9989   | 0.9987   | 0.9990          | 0.9985       |
| 0.007         | 2.0   | 7700  | 0.0141          | 0.9984   | 0.9982   | 0.9983          | 0.9981       |
| 0.0031        | 3.0   | 11550 | 0.0095          | 0.9993   | 0.9992   | 0.9995          | 0.999        |
| 0.0083        | 4.0   | 15400 | 0.0061          | 0.9995   | 0.9995   | 0.9997          | 0.9993       |
| 0.0013        | 5.0   | 19250 | 0.0157          | 0.9986   | 0.9985   | 0.9988          | 0.9981       |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1