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RuBioRoBERTa_neg

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metadata
base_model: alexyalunin/RuBioRoBERTa
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: RuBioRoBERTa_neg
    results: []

RuBioRoBERTa_neg

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

  • Loss: 0.5876
  • Precision: 0.584
  • Recall: 0.6053
  • F1: 0.5945
  • Accuracy: 0.9040

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 0.6516 0.0 0.0 0.0 0.7729
No log 2.0 100 0.6625 0.0 0.0 0.0 0.7706
No log 3.0 150 0.5142 0.0081 0.0058 0.0068 0.7944
No log 4.0 200 0.4359 0.0788 0.1464 0.1024 0.8281
No log 5.0 250 0.3580 0.2362 0.3141 0.2696 0.8642
No log 6.0 300 0.3419 0.2819 0.3237 0.3013 0.8762
No log 7.0 350 0.3492 0.35 0.3642 0.3569 0.8841
No log 8.0 400 0.2633 0.3549 0.4432 0.3942 0.8982
No log 9.0 450 0.2819 0.3871 0.4624 0.4214 0.9001
0.4095 10.0 500 0.2522 0.5035 0.5491 0.5253 0.9119
0.4095 11.0 550 0.2831 0.4704 0.5511 0.5075 0.9077
0.4095 12.0 600 0.3013 0.5245 0.6185 0.5676 0.9105
0.4095 13.0 650 0.3070 0.4711 0.6127 0.5327 0.9048
0.4095 14.0 700 0.3398 0.4771 0.6416 0.5472 0.9039
0.4095 15.0 750 0.3275 0.4661 0.6224 0.5330 0.9114
0.4095 16.0 800 0.3730 0.5118 0.6281 0.5640 0.9141
0.4095 17.0 850 0.3847 0.5593 0.6358 0.5951 0.9160
0.4095 18.0 900 0.4070 0.5824 0.6262 0.6035 0.9182
0.4095 19.0 950 0.3583 0.5433 0.6281 0.5827 0.9161
0.0776 20.0 1000 0.3096 0.5152 0.5877 0.5491 0.9154
0.0776 21.0 1050 0.4015 0.5669 0.6204 0.5925 0.9224
0.0776 22.0 1100 0.5603 0.4251 0.6667 0.5191 0.8753
0.0776 23.0 1150 0.3353 0.6220 0.6089 0.6154 0.9230
0.0776 24.0 1200 0.3800 0.6133 0.6204 0.6169 0.9254
0.0776 25.0 1250 0.4451 0.5792 0.6127 0.5955 0.9153
0.0776 26.0 1300 0.4639 0.6060 0.6224 0.6141 0.9220
0.0776 27.0 1350 0.4141 0.5574 0.6647 0.6063 0.9194
0.0776 28.0 1400 0.4258 0.5675 0.6397 0.6014 0.9143
0.0776 29.0 1450 0.4131 0.5880 0.6435 0.6145 0.9193
0.0374 30.0 1500 0.4104 0.5823 0.6609 0.6191 0.9200
0.0374 31.0 1550 0.4047 0.6190 0.6667 0.6419 0.9213
0.0374 32.0 1600 0.4615 0.6233 0.6185 0.6209 0.9205
0.0374 33.0 1650 0.4597 0.6430 0.5934 0.6172 0.9169
0.0374 34.0 1700 0.3851 0.5043 0.6821 0.5799 0.9040
0.0374 35.0 1750 0.3989 0.6241 0.6590 0.6410 0.9206
0.0374 36.0 1800 0.4866 0.5710 0.6667 0.6151 0.9156
0.0374 37.0 1850 0.4198 0.6208 0.6339 0.6273 0.9241
0.0374 38.0 1900 0.4526 0.5615 0.6243 0.5912 0.9164
0.0374 39.0 1950 0.5038 0.6149 0.6031 0.6089 0.9187
0.0337 40.0 2000 0.3879 0.5684 0.6243 0.5950 0.9196
0.0337 41.0 2050 0.5178 0.5913 0.6301 0.6101 0.9170
0.0337 42.0 2100 0.4898 0.6558 0.5838 0.6177 0.9155

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1