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--- |
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base_model: alexyalunin/RuBioRoBERTa |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: RuBioRoBERTa_pos |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# RuBioRoBERTa_pos |
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This model is a fine-tuned version of [alexyalunin/RuBioRoBERTa](https://huggingface.co/alexyalunin/RuBioRoBERTa) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5510 |
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- Precision: 0.6388 |
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- Recall: 0.5954 |
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- F1: 0.6163 |
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- Accuracy: 0.9111 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 50 | 0.6556 | 0.0 | 0.0 | 0.0 | 0.7611 | |
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| No log | 2.0 | 100 | 1.2213 | 0.0011 | 0.0019 | 0.0014 | 0.2513 | |
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| No log | 3.0 | 150 | 0.6117 | 0.0 | 0.0 | 0.0 | 0.7642 | |
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| No log | 4.0 | 200 | 0.5155 | 0.0135 | 0.0405 | 0.0203 | 0.7884 | |
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| No log | 5.0 | 250 | 0.4171 | 0.0697 | 0.1715 | 0.0991 | 0.8268 | |
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| No log | 6.0 | 300 | 0.3536 | 0.1054 | 0.1753 | 0.1317 | 0.8594 | |
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| No log | 7.0 | 350 | 0.3714 | 0.1638 | 0.2216 | 0.1884 | 0.8685 | |
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| No log | 8.0 | 400 | 0.2889 | 0.2477 | 0.3622 | 0.2942 | 0.8864 | |
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| No log | 9.0 | 450 | 0.2943 | 0.2799 | 0.3969 | 0.3283 | 0.8921 | |
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| 0.452 | 10.0 | 500 | 0.2916 | 0.3823 | 0.4817 | 0.4263 | 0.9011 | |
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| 0.452 | 11.0 | 550 | 0.3162 | 0.3329 | 0.4817 | 0.3937 | 0.8935 | |
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| 0.452 | 12.0 | 600 | 0.3245 | 0.3629 | 0.4971 | 0.4195 | 0.9040 | |
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| 0.452 | 13.0 | 650 | 0.3535 | 0.4022 | 0.4913 | 0.4423 | 0.9021 | |
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| 0.452 | 14.0 | 700 | 0.3313 | 0.4161 | 0.5588 | 0.4770 | 0.9023 | |
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| 0.452 | 15.0 | 750 | 0.3560 | 0.4210 | 0.5800 | 0.4878 | 0.9006 | |
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| 0.452 | 16.0 | 800 | 0.3980 | 0.4125 | 0.6224 | 0.4962 | 0.8905 | |
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| 0.452 | 17.0 | 850 | 0.3767 | 0.4820 | 0.6455 | 0.5519 | 0.9071 | |
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| 0.452 | 18.0 | 900 | 0.3947 | 0.4605 | 0.6513 | 0.5395 | 0.9034 | |
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| 0.452 | 19.0 | 950 | 0.4351 | 0.4395 | 0.5877 | 0.5029 | 0.9066 | |
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| 0.0844 | 20.0 | 1000 | 0.3581 | 0.4931 | 0.5530 | 0.5213 | 0.9097 | |
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| 0.0844 | 21.0 | 1050 | 0.4050 | 0.4892 | 0.6108 | 0.5433 | 0.9063 | |
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| 0.0844 | 22.0 | 1100 | 0.4893 | 0.5504 | 0.5472 | 0.5488 | 0.9076 | |
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| 0.0844 | 23.0 | 1150 | 0.4173 | 0.4722 | 0.6050 | 0.5304 | 0.9062 | |
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| 0.0844 | 24.0 | 1200 | 0.4307 | 0.4819 | 0.6146 | 0.5402 | 0.9075 | |
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| 0.0844 | 25.0 | 1250 | 0.3874 | 0.4977 | 0.6185 | 0.5515 | 0.9151 | |
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| 0.0844 | 26.0 | 1300 | 0.4591 | 0.5478 | 0.6513 | 0.5951 | 0.9130 | |
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| 0.0844 | 27.0 | 1350 | 0.3543 | 0.5308 | 0.5973 | 0.5621 | 0.9144 | |
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| 0.0844 | 28.0 | 1400 | 0.4676 | 0.5380 | 0.5453 | 0.5416 | 0.9187 | |
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| 0.0844 | 29.0 | 1450 | 0.4169 | 0.5365 | 0.6224 | 0.5763 | 0.9131 | |
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| 0.0401 | 30.0 | 1500 | 0.4394 | 0.5867 | 0.5607 | 0.5734 | 0.9114 | |
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| 0.0401 | 31.0 | 1550 | 0.4550 | 0.5446 | 0.6474 | 0.5915 | 0.9166 | |
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| 0.0401 | 32.0 | 1600 | 0.4592 | 0.5415 | 0.6166 | 0.5766 | 0.9125 | |
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| 0.0401 | 33.0 | 1650 | 0.5040 | 0.5218 | 0.6455 | 0.5771 | 0.9093 | |
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| 0.0401 | 34.0 | 1700 | 0.4609 | 0.4295 | 0.6686 | 0.5230 | 0.8989 | |
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| 0.0401 | 35.0 | 1750 | 0.6256 | 0.4833 | 0.6397 | 0.5506 | 0.8975 | |
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| 0.0401 | 36.0 | 1800 | 0.4697 | 0.5742 | 0.6185 | 0.5955 | 0.9088 | |
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| 0.0401 | 37.0 | 1850 | 0.5114 | 0.5645 | 0.6069 | 0.5850 | 0.9139 | |
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| 0.0401 | 38.0 | 1900 | 0.5884 | 0.6237 | 0.5780 | 0.6 | 0.9088 | |
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| 0.0401 | 39.0 | 1950 | 0.5022 | 0.5429 | 0.6455 | 0.5898 | 0.9135 | |
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| 0.0328 | 40.0 | 2000 | 0.4154 | 0.6315 | 0.6339 | 0.6327 | 0.9202 | |
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| 0.0328 | 41.0 | 2050 | 0.3940 | 0.5519 | 0.6146 | 0.5816 | 0.9145 | |
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| 0.0328 | 42.0 | 2100 | 0.3374 | 0.5477 | 0.6301 | 0.5860 | 0.9120 | |
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| 0.0328 | 43.0 | 2150 | 0.5907 | 0.5483 | 0.5029 | 0.5246 | 0.9041 | |
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| 0.0328 | 44.0 | 2200 | 0.4235 | 0.5606 | 0.6416 | 0.5984 | 0.9145 | |
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| 0.0328 | 45.0 | 2250 | 0.6646 | 0.0 | 0.0 | 0.0 | 0.7640 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.2 |
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