transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer dataset:common_language license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- common_language
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: language-detection-fine-tuned-on-xlm-roberta-base
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: common_language
      type: common_language
      config: full
      split: test
      args: full
    metrics:
    - type: accuracy
      value: 0.9745094750964279
      name: Accuracy
---

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# 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: 0.1789
- Accuracy: 0.9745

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1658        | 1.0   | 22194 | 0.1789          | 0.9745   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
