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spa-eng-pos-tagging-v2

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4432
  • Accuracy: 0.8418
  • Precision: 0.8395
  • Recall: 0.7600
  • F1: 0.7676
  • Hamming Loss: 0.1582

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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 12

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Hamming Loss
1.4285 1.0 1744 1.2584 0.5671 0.6506 0.4372 0.4716 0.4329
1.1788 2.0 3488 1.0023 0.6388 0.6753 0.5323 0.5578 0.3612
0.9144 3.0 5232 0.7885 0.7093 0.7259 0.6091 0.6281 0.2907
0.78 4.0 6976 0.6970 0.7439 0.7517 0.6527 0.6673 0.2561
0.6565 5.0 8720 0.6072 0.7765 0.7792 0.6838 0.6952 0.2235
0.5845 6.0 10464 0.5438 0.7995 0.7974 0.7125 0.7221 0.2005
0.5158 7.0 12208 0.5127 0.8132 0.8180 0.7250 0.7362 0.1868
0.4697 8.0 13952 0.4939 0.8186 0.8188 0.7345 0.7438 0.1814
0.4251 9.0 15696 0.4712 0.8334 0.8349 0.7502 0.7591 0.1666
0.4039 10.0 17440 0.4564 0.8381 0.8382 0.7538 0.7629 0.1619
0.3826 11.0 19184 0.4479 0.8397 0.8399 0.7565 0.7656 0.1603
0.3691 12.0 20928 0.4432 0.8418 0.8395 0.7600 0.7676 0.1582

Framework versions

  • Transformers 4.32.0
  • Pytorch 2.0.1+cu118
  • Tokenizers 0.13.3
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