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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-r-base-amazon-massive-slot
    results: []

xlm-r-base-amazon-massive-slot

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5006
  • Precision: 0.8144
  • Recall: 0.8683
  • F1: 0.8405
  • Accuracy: 0.9333

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: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
1.1445 1.0 720 0.5446 0.6681 0.6770 0.6725 0.8842
0.5908 2.0 1440 0.3869 0.7331 0.7706 0.7514 0.9083
0.3228 3.0 2160 0.3285 0.7658 0.8288 0.7961 0.9219
0.2561 4.0 2880 0.3063 0.7819 0.8402 0.8100 0.9257
0.1808 5.0 3600 0.3000 0.8011 0.8429 0.8214 0.9305
0.1487 6.0 4320 0.2982 0.8201 0.8492 0.8344 0.9361
0.1156 7.0 5040 0.3252 0.8009 0.8569 0.8280 0.9313
0.094 8.0 5760 0.3481 0.8127 0.8502 0.8310 0.9333
0.0843 9.0 6480 0.3764 0.7990 0.8613 0.8290 0.9304
0.0641 10.0 7200 0.3822 0.7930 0.8609 0.8256 0.9280
0.0547 11.0 7920 0.3889 0.8223 0.8649 0.8431 0.9354
0.04 12.0 8640 0.4416 0.8019 0.8633 0.8314 0.9288
0.0368 13.0 9360 0.4339 0.8117 0.8606 0.8354 0.9328
0.0297 14.0 10080 0.4698 0.8062 0.8623 0.8333 0.9314
0.0227 15.0 10800 0.4763 0.8058 0.8656 0.8346 0.9327
0.0185 16.0 11520 0.4793 0.8124 0.8613 0.8361 0.9326
0.0182 17.0 12240 0.4835 0.8191 0.8629 0.8404 0.9341
0.0147 18.0 12960 0.4981 0.8140 0.8693 0.8407 0.9336
0.0111 19.0 13680 0.5002 0.8099 0.8719 0.8398 0.9340
0.0128 20.0 14400 0.5006 0.8144 0.8683 0.8405 0.9333

Framework versions

  • Transformers 4.22.2
  • Pytorch 1.12.1+cu113
  • Datasets 2.5.1
  • Tokenizers 0.12.1