README.md

metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - told-br
metrics:
  - accuracy
  - f1
model-index:
  - name: xlm-roberta-base-finetuned-told-br
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: told-br
          type: told-br
          config: binary
          split: validation
          args: binary
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7457142857142857
          - name: F1
            type: f1
            value: 0.7452494655157376

xlm-roberta-base-finetuned-told-br

This model is a fine-tuned version of xlm-roberta-base on the told-br dataset. It achieves the following results on the evaluation set:

• Loss: 0.4925
• Accuracy: 0.7457
• F1: 0.7452

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: 64
• eval_batch_size: 128
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.5935	1.0	263	0.4970	0.7338	0.7350
0.4797	2.0	526	0.4925	0.7457	0.7452

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1