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

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metadata
license: other
widget:
  - text: I'm fine. Who is this?
  - text: You can't take anything seriously.
  - text: In the end he's going to croak, isn't he?
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: balanced-augmented-bert-gest-pred-seqeval-partialmatch
    results: []
pipeline_tag: token-classification
datasets:
  - Jsevisal/balanced_augmented_dataset

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

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: 0.8382
  • Precision: 0.8478
  • Recall: 0.8224
  • F1: 0.8293
  • Accuracy: 0.8118

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
3.3729 1.0 32 2.8438 0.0806 0.0549 0.0294 0.1986
2.7169 2.0 64 2.2356 0.4355 0.2940 0.2982 0.4307
2.0107 3.0 96 1.7202 0.6950 0.5187 0.5245 0.5698
1.4085 4.0 128 1.3703 0.7994 0.6487 0.6499 0.6582
0.9974 5.0 160 1.1172 0.8205 0.7349 0.7514 0.7156
0.6996 6.0 192 1.0020 0.8220 0.7550 0.7684 0.7451
0.492 7.0 224 0.9132 0.8203 0.7626 0.7722 0.7549
0.3593 8.0 256 0.8785 0.8475 0.8042 0.8135 0.7921
0.2618 9.0 288 0.8383 0.8395 0.8135 0.8199 0.7999
0.1928 10.0 320 0.8410 0.8433 0.8165 0.8240 0.8014
0.1541 11.0 352 0.8382 0.8478 0.8224 0.8293 0.8118
0.1216 12.0 384 0.8667 0.8259 0.8253 0.8210 0.8046
0.096 13.0 416 0.8726 0.8471 0.8253 0.8301 0.8133
0.0767 14.0 448 0.8826 0.8475 0.8307 0.8330 0.8102
0.0696 15.0 480 0.8964 0.8411 0.8285 0.8303 0.8149
0.057 16.0 512 0.9194 0.8365 0.8292 0.8289 0.8097
0.0514 17.0 544 0.9085 0.8502 0.8277 0.8326 0.8118
0.0468 18.0 576 0.9261 0.8345 0.8250 0.8243 0.8092
0.0437 19.0 608 0.9279 0.8394 0.8258 0.8270 0.8118
0.0414 20.0 640 0.9263 0.8443 0.8275 0.8298 0.8139

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.