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not-ner-v1

This model is a fine-tuned version of dccuchile/tulio-chilean-spanish-bert on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1680
  • Accuracy: 0.9337
  • Precision: 0.9334
  • Recall: 0.9337
  • F1: 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: 5e-05
  • train_batch_size: 20
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.3465 0.0799 200 0.3024 0.8719 0.8787 0.8719 0.8737
0.2925 0.1599 400 0.2530 0.9045 0.9039 0.9045 0.9041
0.2362 0.2398 600 0.2383 0.9089 0.9084 0.9089 0.9085
0.239 0.3197 800 0.2083 0.9169 0.9163 0.9169 0.9163
0.2149 0.3997 1000 0.2640 0.9130 0.9150 0.9130 0.9109
0.2171 0.4796 1200 0.1932 0.9211 0.9214 0.9211 0.9212
0.2056 0.5596 1400 0.1962 0.9237 0.9243 0.9237 0.9224
0.1973 0.6395 1600 0.1906 0.9258 0.9255 0.9258 0.9256
0.1912 0.7194 1800 0.1870 0.9277 0.9275 0.9277 0.9270
0.183 0.7994 2000 0.1727 0.9318 0.9317 0.9318 0.9318
0.1672 0.8793 2200 0.1809 0.9320 0.9318 0.9320 0.9313
0.1643 0.9592 2400 0.1680 0.9337 0.9334 0.9337 0.9333

Framework versions

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