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training_results

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electra
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metadata
license: apache-2.0
base_model: ai-forever/ruBert-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - precision
  - f1
model-index:
  - name: training_results
    results: []

training_results

This model is a fine-tuned version of ai-forever/ruBert-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.8438
  • Accuracy: 0.7661
  • Recall: 0.7479
  • Precision: 0.7613
  • F1: 0.7523

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: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy Recall Precision F1
No log 1.0 100 0.9047 0.7222 0.6078 0.6087 0.5979
No log 2.0 200 0.7773 0.7427 0.6082 0.5795 0.5919
No log 3.0 300 0.9403 0.7398 0.7074 0.7732 0.7198
No log 4.0 400 1.1453 0.7135 0.6713 0.7322 0.6785
0.505 5.0 500 1.3685 0.7310 0.7011 0.7616 0.7131
0.505 6.0 600 1.3323 0.7310 0.7511 0.7179 0.7290
0.505 7.0 700 1.3571 0.7544 0.7280 0.7483 0.7283
0.505 8.0 800 1.4632 0.7368 0.7334 0.7298 0.7275
0.505 9.0 900 1.5987 0.7515 0.7474 0.7494 0.7429
0.0175 10.0 1000 1.5397 0.7778 0.7534 0.7902 0.7671
0.0175 11.0 1100 1.6137 0.7749 0.7731 0.7927 0.7784
0.0175 12.0 1200 1.6046 0.7778 0.7611 0.7916 0.7714
0.0175 13.0 1300 1.5817 0.7778 0.7591 0.7894 0.7706
0.0175 14.0 1400 1.6229 0.7865 0.7642 0.7965 0.7766
0.0035 15.0 1500 1.5925 0.7836 0.7620 0.7910 0.7733
0.0035 16.0 1600 1.6239 0.7836 0.7640 0.7922 0.7747
0.0035 17.0 1700 1.6805 0.7778 0.7564 0.7769 0.7643
0.0035 18.0 1800 1.7244 0.7719 0.7528 0.7622 0.7560
0.0035 19.0 1900 1.7410 0.7719 0.7561 0.7619 0.7576
0.0028 20.0 2000 1.7693 0.7690 0.7617 0.7579 0.7569
0.0028 21.0 2100 1.7823 0.7690 0.7520 0.7623 0.7542
0.0028 22.0 2200 1.7821 0.7719 0.7524 0.7652 0.7560
0.0028 23.0 2300 1.7932 0.7690 0.7510 0.7634 0.7546
0.0028 24.0 2400 1.8111 0.7690 0.7510 0.7703 0.7584
0.0017 25.0 2500 1.8289 0.7690 0.7494 0.7707 0.7580
0.0017 26.0 2600 1.8438 0.7661 0.7479 0.7613 0.7523

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.5
  • Tokenizers 0.14.1