Hugging Face's logo

Jsevisal
/
balanced-augmented-distilbert-base-gest-pred-seqeval-partialmatch

Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
1
balanced-augmented-distilbert-base-gest-pred-seqeval-partialmatch / README.md
Jsevisal's picture
Jsevisal
Update README.md
ab34d5e
preview code
|
raw
history blame contribute delete
4.13 kB
metadata
license: other
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: balanced-augmented-distilbert-base-gest-pred-seqeval-partialmatch
    results: []
datasets:
  - Jsevisal/balanced_augmented_dataset

balanced-augmented-distilbert-base-gest-pred-seqeval-partialmatch

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

  • Loss: 0.945763
  • Precision: 0.8278
  • Recall: 0.756
  • F1: 0.7773
  • Accuracy: 0.7808

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
2.675 1.0 32 2.2896 0.2866 0.1994 0.2017 0.3764
2.0426 2.0 64 1.7819 0.5176 0.4342 0.4249 0.5357
1.5284 3.0 96 1.4798 0.7435 0.5285 0.5441 0.6210
1.1542 4.0 128 1.3020 0.7677 0.6013 0.6319 0.6737
0.8963 5.0 160 1.1608 0.7886 0.6494 0.6814 0.7053
0.6926 6.0 192 1.1128 0.7891 0.6968 0.7238 0.7316
0.5249 7.0 224 1.0319 0.8019 0.7179 0.7406 0.7487
0.4063 8.0 256 1.0163 0.8117 0.7091 0.7399 0.7570
0.3238 9.0 288 1.0071 0.8052 0.7201 0.7447 0.7642
0.2578 10.0 320 0.9579 0.8070 0.7469 0.7655 0.7740
0.2097 11.0 352 0.9458 0.8278 0.7560 0.7773 0.7808
0.1729 12.0 384 0.9760 0.8184 0.7471 0.7670 0.7756
0.1473 13.0 416 0.9598 0.8175 0.7503 0.7702 0.7823
0.1235 14.0 448 0.9671 0.8244 0.7548 0.7734 0.7802
0.1082 15.0 480 0.9788 0.8169 0.7508 0.7709 0.7746
0.0966 16.0 512 1.0090 0.8098 0.7489 0.7669 0.7715
0.0871 17.0 544 0.9926 0.8111 0.7545 0.7690 0.7694
0.0825 18.0 576 1.0070 0.8144 0.7524 0.7688 0.7704
0.0768 19.0 608 1.0137 0.8191 0.7557 0.7743 0.7746
0.0744 20.0 640 1.0130 0.8209 0.7559 0.7750 0.7746

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.