---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: language-detection-fine-tuned-on-xlm-roberta-base
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: common_language
      type: common_language
      config: full
      split: test
      args: full
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9778634915311085
---

<!-- 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-detection-fine-tuned-on-xlm-roberta-base

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1527
- Accuracy: 0.9779

## 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: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2047        | 1.0   | 22194 | 0.1527          | 0.9779   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3