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xlm-r-base-amazon-massive-intent-label_smoothing

This model is a fine-tuned version of xlm-roberta-base on the MASSIVE1.1 dataset. It achieves the following results on the evaluation set:

  • Loss: 2.5148
  • Accuracy: 0.8879
  • F1: 0.8879

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5
  • label_smoothing_factor: 0.4

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
3.3945 1.0 720 2.7175 0.7900 0.7900
2.7629 2.0 1440 2.5660 0.8549 0.8549
2.5143 3.0 2160 2.5389 0.8711 0.8711
2.4678 4.0 2880 2.5172 0.8883 0.8883
2.4187 5.0 3600 2.5148 0.8879 0.8879

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1+cu113
  • Datasets 2.7.0
  • Tokenizers 0.13.2
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Finetuned from

Dataset used to train cartesinus/xlm-r-base-amazon-massive-intent-label_smoothing

Evaluation results