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balanced-augmented-bert-gest-pred-seqeval-partialmatch-2

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metadata
license: other
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: balanced-augmented-bert-gest-pred-seqeval-partialmatch-2
    results: []
datasets:
  - Jsevisal/balanced_augmented_dataset_2
pipeline_tag: token-classification

balanced-augmented-bert-gest-pred-seqeval-partialmatch-2

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

  • Loss: 0.3649
  • Precision: 0.9322
  • Recall: 0.9236
  • F1: 0.9235
  • Accuracy: 0.9138

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
3.1239 1.0 52 2.5758 0.1906 0.1293 0.1175 0.3121
2.1363 2.0 104 1.7477 0.4478 0.4505 0.4151 0.5576
1.5182 3.0 156 1.3113 0.7051 0.6014 0.5633 0.6507
1.0948 4.0 208 0.9763 0.7361 0.6854 0.6695 0.7183
0.7591 5.0 260 0.7834 0.7900 0.7905 0.7726 0.7869
0.5168 6.0 312 0.5764 0.8775 0.8569 0.8479 0.8550
0.3609 7.0 364 0.5130 0.9055 0.8857 0.8815 0.8760
0.2538 8.0 416 0.4872 0.9106 0.8828 0.8865 0.8805
0.1898 9.0 468 0.3937 0.9219 0.9070 0.9076 0.8996
0.1343 10.0 520 0.3897 0.9271 0.9016 0.9095 0.9010
0.1053 11.0 572 0.3900 0.9309 0.9085 0.9143 0.9030
0.0788 12.0 624 0.3649 0.9322 0.9236 0.9235 0.9138
0.0643 13.0 676 0.4147 0.9293 0.9073 0.9122 0.9045
0.0501 14.0 728 0.4788 0.9369 0.9205 0.9200 0.9054
0.0424 15.0 780 0.4003 0.9346 0.9180 0.9197 0.9094
0.0376 16.0 832 0.3686 0.9373 0.9261 0.9274 0.9182
0.0317 17.0 884 0.4025 0.9360 0.9199 0.9223 0.9098
0.0288 18.0 936 0.4484 0.9406 0.9212 0.9239 0.9098
0.0282 19.0 988 0.4004 0.9377 0.9207 0.9238 0.9094
0.0256 20.0 1040 0.4046 0.9383 0.9205 0.9237 0.9098

Framework versions

  • Transformers 4.27.3
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.1
  • Tokenizers 0.13.2

LICENSE

Copyright (c) 2014, Universidad Carlos III de Madrid. Todos los derechos reservados. Este software es propiedad de la Universidad Carlos III de Madrid, grupo de investigaci贸n Robots Sociales. La Universidad Carlos III de Madrid es titular en exclusiva de los derechos de propiedad intelectual de este software. Queda prohibido cualquier uso indebido o no autorizado, entre estos, a t铆tulo enunciativo pero no limitativo, la reproducci贸n, fijaci贸n, distribuci贸n, comunicaci贸n p煤blica, ingenier铆a inversa y/o transformaci贸n sobre dicho software, ya sea total o parcialmente, siendo el responsable del uso indebido o no autorizado tambi茅n responsable de las consecuencias legales que pudieran derivarse de sus actos.