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MedRuRobertaLarge_pos

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metadata
license: apache-2.0
base_model: DmitryPogrebnoy/MedRuRobertaLarge
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: MedRuRobertaLarge_pos
    results: []

MedRuRobertaLarge_pos

This model is a fine-tuned version of DmitryPogrebnoy/MedRuRobertaLarge on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4823
  • Precision: 0.4746
  • Recall: 0.5274
  • F1: 0.4996
  • Accuracy: 0.9031

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: 4
  • eval_batch_size: 8
  • 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 Precision Recall F1 Accuracy
No log 1.0 50 0.6491 0.0 0.0 0.0 0.7598
No log 2.0 100 0.6401 0.0 0.0 0.0 0.7664
No log 3.0 150 0.4835 0.0195 0.0193 0.0194 0.8187
No log 4.0 200 0.4325 0.0790 0.1368 0.1001 0.8181
No log 5.0 250 0.3456 0.1653 0.2370 0.1948 0.8675
No log 6.0 300 0.3438 0.2128 0.2697 0.2379 0.8744
No log 7.0 350 0.3814 0.3415 0.2948 0.3164 0.8832
No log 8.0 400 0.3005 0.3026 0.3854 0.3390 0.8877
No log 9.0 450 0.2641 0.3718 0.5279 0.4363 0.8997
0.4044 10.0 500 0.2754 0.4036 0.5164 0.4531 0.9057
0.4044 11.0 550 0.3153 0.4041 0.6416 0.4959 0.8949
0.4044 12.0 600 0.3362 0.4428 0.5222 0.4792 0.9094
0.4044 13.0 650 0.3325 0.4433 0.5645 0.4966 0.9109
0.4044 14.0 700 0.2921 0.4320 0.5568 0.4865 0.9064
0.4044 15.0 750 0.3871 0.4630 0.5780 0.5141 0.9080
0.4044 16.0 800 0.3479 0.4218 0.6339 0.5065 0.8946
0.4044 17.0 850 0.3886 0.4914 0.6031 0.5415 0.9096
0.4044 18.0 900 0.5079 0.5108 0.5491 0.5292 0.9076
0.4044 19.0 950 0.3963 0.4344 0.6763 0.5290 0.8999
0.0912 20.0 1000 0.3845 0.5033 0.5915 0.5438 0.9145
0.0912 21.0 1050 0.5141 0.3986 0.4239 0.4108 0.8925
0.0912 22.0 1100 0.4587 0.4706 0.5395 0.5027 0.9028
0.0912 23.0 1150 0.4360 0.5017 0.5800 0.5380 0.9075

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.2