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--- |
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base_model: ai-forever/ruRoberta-large |
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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: ruRoberta-large_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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# ruRoberta-large_pos |
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This model is a fine-tuned version of [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5140 |
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- Precision: 0.5566 |
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- Recall: 0.5871 |
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- F1: 0.5714 |
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- Accuracy: 0.8981 |
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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 | 0.6582 | 0.0 | 0.0 | 0.0 | 0.7628 | |
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| No log | 2.0 | 100 | 0.5705 | 0.0118 | 0.0173 | 0.0140 | 0.7783 | |
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| No log | 3.0 | 150 | 0.4784 | 0.0277 | 0.0501 | 0.0356 | 0.8028 | |
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| No log | 4.0 | 200 | 0.4043 | 0.0784 | 0.1329 | 0.0986 | 0.8323 | |
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| No log | 5.0 | 250 | 0.3553 | 0.1545 | 0.2697 | 0.1965 | 0.8523 | |
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| No log | 6.0 | 300 | 0.4051 | 0.2312 | 0.2601 | 0.2448 | 0.8692 | |
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| No log | 7.0 | 350 | 0.3351 | 0.3456 | 0.3796 | 0.3618 | 0.8901 | |
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| No log | 8.0 | 400 | 0.2774 | 0.3344 | 0.3911 | 0.3606 | 0.8974 | |
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| No log | 9.0 | 450 | 0.3010 | 0.3819 | 0.5048 | 0.4349 | 0.9022 | |
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| 0.3753 | 10.0 | 500 | 0.2892 | 0.4114 | 0.4875 | 0.4462 | 0.9051 | |
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| 0.3753 | 11.0 | 550 | 0.2773 | 0.3707 | 0.5222 | 0.4336 | 0.9076 | |
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| 0.3753 | 12.0 | 600 | 0.3447 | 0.4706 | 0.5549 | 0.5093 | 0.9076 | |
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| 0.3753 | 13.0 | 650 | 0.3312 | 0.4317 | 0.5356 | 0.4781 | 0.9073 | |
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| 0.3753 | 14.0 | 700 | 0.2870 | 0.4818 | 0.6378 | 0.5489 | 0.9132 | |
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| 0.3753 | 15.0 | 750 | 0.3944 | 0.4443 | 0.5992 | 0.5103 | 0.9024 | |
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| 0.3753 | 16.0 | 800 | 0.3599 | 0.4319 | 0.6416 | 0.5163 | 0.9018 | |
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| 0.3753 | 17.0 | 850 | 0.3568 | 0.4560 | 0.6397 | 0.5325 | 0.9042 | |
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| 0.3753 | 18.0 | 900 | 0.4296 | 0.4674 | 0.5241 | 0.4941 | 0.9106 | |
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| 0.3753 | 19.0 | 950 | 0.3939 | 0.4617 | 0.5453 | 0.5 | 0.9137 | |
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| 0.0842 | 20.0 | 1000 | 0.3882 | 0.5109 | 0.5434 | 0.5266 | 0.9066 | |
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| 0.0842 | 21.0 | 1050 | 0.3870 | 0.5311 | 0.6243 | 0.5740 | 0.9075 | |
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| 0.0842 | 22.0 | 1100 | 0.4163 | 0.4252 | 0.6628 | 0.5181 | 0.8925 | |
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| 0.0842 | 23.0 | 1150 | 0.4097 | 0.4577 | 0.5010 | 0.4784 | 0.9004 | |
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| 0.0842 | 24.0 | 1200 | 0.3709 | 0.5482 | 0.6031 | 0.5743 | 0.9161 | |
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| 0.0842 | 25.0 | 1250 | 0.3366 | 0.5088 | 0.6647 | 0.5764 | 0.9141 | |
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| 0.0842 | 26.0 | 1300 | 0.4558 | 0.6132 | 0.6108 | 0.6120 | 0.9171 | |
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| 0.0842 | 27.0 | 1350 | 0.4982 | 0.5720 | 0.5896 | 0.5806 | 0.9102 | |
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| 0.0842 | 28.0 | 1400 | 0.3998 | 0.5615 | 0.6513 | 0.6030 | 0.9178 | |
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| 0.0842 | 29.0 | 1450 | 0.5028 | 0.5620 | 0.6551 | 0.6050 | 0.9108 | |
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| 0.0476 | 30.0 | 1500 | 0.3672 | 0.5739 | 0.6435 | 0.6067 | 0.9117 | |
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| 0.0476 | 31.0 | 1550 | 0.4520 | 0.5330 | 0.6532 | 0.5870 | 0.9084 | |
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| 0.0476 | 32.0 | 1600 | 0.5027 | 0.5628 | 0.6127 | 0.5867 | 0.9101 | |
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| 0.0476 | 33.0 | 1650 | 0.4461 | 0.4581 | 0.6108 | 0.5235 | 0.9087 | |
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| 0.0476 | 34.0 | 1700 | 0.4407 | 0.4726 | 0.5992 | 0.5285 | 0.9070 | |
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| 0.0476 | 35.0 | 1750 | 0.4512 | 0.5211 | 0.5241 | 0.5226 | 0.9082 | |
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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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