| --- |
| base_model: alexyalunin/RuBioRoBERTa |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: RuBioRoBERTa_neg |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # RuBioRoBERTa_neg |
| |
| This model is a fine-tuned version of [alexyalunin/RuBioRoBERTa](https://huggingface.co/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 |
| |
