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--- |
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license: apache-2.0 |
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base_model: DmitryPogrebnoy/MedRuRobertaLarge |
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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: MedRuRobertaLarge_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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# MedRuRobertaLarge_pos |
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This model is a fine-tuned version of [DmitryPogrebnoy/MedRuRobertaLarge](https://huggingface.co./DmitryPogrebnoy/MedRuRobertaLarge) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4867 |
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- Precision: 0.5088 |
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- Recall: 0.5257 |
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- F1: 0.5171 |
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- Accuracy: 0.8997 |
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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 | 0.6663 | 0.0 | 0.0 | 0.0 | 0.7639 | |
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| No log | 2.0 | 100 | 0.5206 | 0.0178 | 0.0154 | 0.0165 | 0.8015 | |
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| No log | 3.0 | 150 | 0.4083 | 0.0409 | 0.0617 | 0.0492 | 0.8346 | |
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| No log | 4.0 | 200 | 0.3900 | 0.1300 | 0.2139 | 0.1617 | 0.8368 | |
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| No log | 5.0 | 250 | 0.3372 | 0.1893 | 0.2987 | 0.2317 | 0.8598 | |
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| No log | 6.0 | 300 | 0.2828 | 0.2713 | 0.3622 | 0.3102 | 0.8907 | |
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| No log | 7.0 | 350 | 0.3583 | 0.3625 | 0.4066 | 0.3833 | 0.8890 | |
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| No log | 8.0 | 400 | 0.2786 | 0.3638 | 0.4605 | 0.4065 | 0.8995 | |
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| No log | 9.0 | 450 | 0.3000 | 0.3224 | 0.4181 | 0.3641 | 0.8981 | |
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| 0.3576 | 10.0 | 500 | 0.3055 | 0.4872 | 0.5145 | 0.5005 | 0.9085 | |
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| 0.3576 | 11.0 | 550 | 0.2949 | 0.4633 | 0.5106 | 0.4858 | 0.9123 | |
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| 0.3576 | 12.0 | 600 | 0.3481 | 0.4407 | 0.5723 | 0.4979 | 0.9054 | |
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| 0.3576 | 13.0 | 650 | 0.3636 | 0.4814 | 0.5241 | 0.5018 | 0.9054 | |
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| 0.3576 | 14.0 | 700 | 0.3186 | 0.4981 | 0.5010 | 0.4995 | 0.9132 | |
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| 0.3576 | 15.0 | 750 | 0.3472 | 0.4329 | 0.5780 | 0.4950 | 0.9084 | |
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| 0.3576 | 16.0 | 800 | 0.3664 | 0.4843 | 0.5665 | 0.5222 | 0.9177 | |
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| 0.3576 | 17.0 | 850 | 0.3666 | 0.4371 | 0.6089 | 0.5089 | 0.9085 | |
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| 0.3576 | 18.0 | 900 | 0.4685 | 0.4894 | 0.5356 | 0.5115 | 0.9167 | |
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| 0.3576 | 19.0 | 950 | 0.3722 | 0.4309 | 0.5703 | 0.4909 | 0.9154 | |
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| 0.0824 | 20.0 | 1000 | 0.3861 | 0.5327 | 0.5645 | 0.5482 | 0.9097 | |
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| 0.0824 | 21.0 | 1050 | 0.6866 | 0.5201 | 0.4239 | 0.4671 | 0.8853 | |
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| 0.0824 | 22.0 | 1100 | 0.5474 | 0.4616 | 0.6493 | 0.5396 | 0.8934 | |
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| 0.0824 | 23.0 | 1150 | 0.4203 | 0.5714 | 0.5857 | 0.5785 | 0.9168 | |
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| 0.0824 | 24.0 | 1200 | 0.4038 | 0.3748 | 0.5568 | 0.4481 | 0.8989 | |
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| 0.0824 | 25.0 | 1250 | 0.4873 | 0.5564 | 0.5414 | 0.5488 | 0.9123 | |
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| 0.0824 | 26.0 | 1300 | 0.4516 | 0.5306 | 0.5838 | 0.5560 | 0.9170 | |
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| 0.0824 | 27.0 | 1350 | 0.4349 | 0.5738 | 0.5915 | 0.5825 | 0.9110 | |
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| 0.0824 | 28.0 | 1400 | 0.4042 | 0.5250 | 0.5857 | 0.5537 | 0.9083 | |
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| 0.0824 | 29.0 | 1450 | 0.4187 | 0.6107 | 0.6166 | 0.6136 | 0.9103 | |
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| 0.0475 | 30.0 | 1500 | 0.3910 | 0.4615 | 0.6127 | 0.5265 | 0.9060 | |
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| 0.0475 | 31.0 | 1550 | 0.4171 | 0.5541 | 0.6416 | 0.5946 | 0.9133 | |
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| 0.0475 | 32.0 | 1600 | 0.4948 | 0.5730 | 0.6127 | 0.5922 | 0.9109 | |
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| 0.0475 | 33.0 | 1650 | 0.4637 | 0.5048 | 0.6089 | 0.5520 | 0.9118 | |
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| 0.0475 | 34.0 | 1700 | 0.3740 | 0.5431 | 0.6185 | 0.5784 | 0.9213 | |
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| 0.0475 | 35.0 | 1750 | 0.4047 | 0.5280 | 0.5992 | 0.5614 | 0.9129 | |
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| 0.0475 | 36.0 | 1800 | 0.4010 | 0.5352 | 0.6301 | 0.5788 | 0.9150 | |
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| 0.0475 | 37.0 | 1850 | 0.5743 | 0.5905 | 0.5530 | 0.5711 | 0.9108 | |
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| 0.0475 | 38.0 | 1900 | 0.4936 | 0.5110 | 0.4913 | 0.5010 | 0.9102 | |
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| 0.0475 | 39.0 | 1950 | 0.4450 | 0.4537 | 0.5857 | 0.5114 | 0.9119 | |
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| 0.0424 | 40.0 | 2000 | 0.4611 | 0.4983 | 0.5588 | 0.5268 | 0.9130 | |
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| 0.0424 | 41.0 | 2050 | 0.4748 | 0.5199 | 0.5279 | 0.5239 | 0.9075 | |
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| 0.0424 | 42.0 | 2100 | 0.5121 | 0.5264 | 0.5568 | 0.5412 | 0.9126 | |
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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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