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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
model-index:
  - name: bert-base-uncased-finetuned-math_punctuation-ignore_word_parts
    results: []

bert-base-uncased-finetuned-math_punctuation-ignore_word_parts

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

  • Loss: 0.1981
  • Precision: 0.7843
  • Recall: 0.7485
  • F Score: 0.7648
  • Auc: 0.9248

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: 16
  • eval_batch_size: 8
  • 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 Precision Recall F Score Auc
0.1064 0.64 500 0.1082 0.7558 0.6580 0.6964 0.9086
0.0781 1.27 1000 0.1025 0.7594 0.7226 0.7365 0.9261
0.0757 1.91 1500 0.1001 0.7945 0.6899 0.7302 0.9272
0.0538 2.54 2000 0.1061 0.7689 0.7348 0.7480 0.9298
0.0425 3.18 2500 0.1123 0.7806 0.7361 0.7560 0.9300
0.0377 3.81 3000 0.1159 0.7841 0.7437 0.7610 0.9292
0.0235 4.45 3500 0.1259 0.7786 0.7368 0.7561 0.9276
0.0227 5.08 4000 0.1436 0.7699 0.7448 0.7555 0.9277
0.0159 5.72 4500 0.1466 0.7715 0.7333 0.7514 0.9252
0.0106 6.35 5000 0.1574 0.7710 0.7456 0.7566 0.9276
0.0111 6.99 5500 0.1560 0.7694 0.7500 0.7595 0.9286
0.0074 7.62 6000 0.1645 0.7789 0.7511 0.7639 0.9305
0.0056 8.26 6500 0.1745 0.7887 0.7453 0.7648 0.9265
0.005 8.89 7000 0.1760 0.7779 0.7497 0.7629 0.9281
0.0038 9.53 7500 0.1873 0.7826 0.7505 0.7634 0.9273
0.0031 10.17 8000 0.1896 0.7855 0.7477 0.7644 0.9258
0.0026 10.8 8500 0.1929 0.7849 0.7485 0.7650 0.9263
0.0017 11.44 9000 0.1981 0.7843 0.7485 0.7648 0.9248

Framework versions

  • Transformers 4.25.1
  • Pytorch 2.0.0.dev20230111
  • Datasets 2.8.0
  • Tokenizers 0.13.2