README.md

metadata

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

language-detection-fine-tuned-on-xlm-roberta-base

This model is a fine-tuned version of 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