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---
base_model: ai-forever/ruRoberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ruRoberta-large_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. -->
# ruRoberta-large_neg
This model is a fine-tuned version of [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6173
- Precision: 0.5980
- Recall: 0.5920
- F1: 0.5950
- Accuracy: 0.9001
## 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 | 0.6748 | 0.0 | 0.0 | 0.0 | 0.7758 |
| No log | 2.0 | 100 | 0.6015 | 0.0054 | 0.0019 | 0.0028 | 0.7853 |
| No log | 3.0 | 150 | 0.4397 | 0.0699 | 0.0867 | 0.0774 | 0.8296 |
| No log | 4.0 | 200 | 0.3701 | 0.1805 | 0.2351 | 0.2042 | 0.8555 |
| No log | 5.0 | 250 | 0.3134 | 0.3189 | 0.3680 | 0.3417 | 0.8823 |
| No log | 6.0 | 300 | 0.2931 | 0.3305 | 0.4528 | 0.3821 | 0.8921 |
| No log | 7.0 | 350 | 0.2891 | 0.4114 | 0.4297 | 0.4204 | 0.9017 |
| No log | 8.0 | 400 | 0.2799 | 0.4714 | 0.5087 | 0.4893 | 0.9033 |
| No log | 9.0 | 450 | 0.2671 | 0.5045 | 0.5453 | 0.5241 | 0.9118 |
| 0.3651 | 10.0 | 500 | 0.2917 | 0.5287 | 0.5145 | 0.5215 | 0.9149 |
| 0.3651 | 11.0 | 550 | 0.2900 | 0.4768 | 0.6127 | 0.5363 | 0.9105 |
| 0.3651 | 12.0 | 600 | 0.3307 | 0.4873 | 0.5896 | 0.5336 | 0.9135 |
| 0.3651 | 13.0 | 650 | 0.2883 | 0.5490 | 0.6050 | 0.5756 | 0.9163 |
| 0.3651 | 14.0 | 700 | 0.3514 | 0.5308 | 0.5819 | 0.5551 | 0.9170 |
| 0.3651 | 15.0 | 750 | 0.3858 | 0.5120 | 0.6590 | 0.5762 | 0.9055 |
| 0.3651 | 16.0 | 800 | 0.3655 | 0.5008 | 0.6262 | 0.5565 | 0.9204 |
| 0.3651 | 17.0 | 850 | 0.3605 | 0.5952 | 0.6628 | 0.6272 | 0.9206 |
| 0.3651 | 18.0 | 900 | 0.5156 | 0.5822 | 0.6416 | 0.6104 | 0.9148 |
| 0.3651 | 19.0 | 950 | 0.4462 | 0.4873 | 0.6628 | 0.5616 | 0.8964 |
| 0.0734 | 20.0 | 1000 | 0.3837 | 0.5817 | 0.5626 | 0.5720 | 0.9147 |
| 0.0734 | 21.0 | 1050 | 0.5484 | 0.6283 | 0.5472 | 0.5850 | 0.9122 |
| 0.0734 | 22.0 | 1100 | 0.4612 | 0.4459 | 0.6358 | 0.5242 | 0.8869 |
| 0.0734 | 23.0 | 1150 | 0.5106 | 0.588 | 0.5665 | 0.5770 | 0.9146 |
| 0.0734 | 24.0 | 1200 | 0.4511 | 0.6526 | 0.5973 | 0.6237 | 0.9187 |
| 0.0734 | 25.0 | 1250 | 0.4511 | 0.6152 | 0.6069 | 0.6111 | 0.9183 |
| 0.0734 | 26.0 | 1300 | 0.4642 | 0.6141 | 0.5703 | 0.5914 | 0.9141 |
| 0.0734 | 27.0 | 1350 | 0.4177 | 0.5191 | 0.6802 | 0.5888 | 0.9057 |
| 0.0734 | 28.0 | 1400 | 0.4025 | 0.6011 | 0.6532 | 0.6260 | 0.9210 |
| 0.0734 | 29.0 | 1450 | 0.4620 | 0.5519 | 0.6455 | 0.5950 | 0.9068 |
| 0.0435 | 30.0 | 1500 | 0.4229 | 0.6029 | 0.6320 | 0.6171 | 0.9205 |
| 0.0435 | 31.0 | 1550 | 0.3752 | 0.5565 | 0.6647 | 0.6058 | 0.9139 |
| 0.0435 | 32.0 | 1600 | 0.5814 | 0.6146 | 0.5684 | 0.5906 | 0.9131 |
| 0.0435 | 33.0 | 1650 | 0.4216 | 0.6155 | 0.5800 | 0.5972 | 0.9128 |
| 0.0435 | 34.0 | 1700 | 0.5093 | 0.5853 | 0.5819 | 0.5836 | 0.9147 |
| 0.0435 | 35.0 | 1750 | 0.4221 | 0.5968 | 0.6532 | 0.6237 | 0.9153 |
| 0.0435 | 36.0 | 1800 | 0.4700 | 0.6404 | 0.6416 | 0.6410 | 0.9179 |
| 0.0435 | 37.0 | 1850 | 0.3946 | 0.5651 | 0.5684 | 0.5668 | 0.9167 |
| 0.0435 | 38.0 | 1900 | 0.4196 | 0.6013 | 0.5549 | 0.5772 | 0.9062 |
| 0.0435 | 39.0 | 1950 | 0.4054 | 0.6282 | 0.5761 | 0.6010 | 0.9194 |
| 0.0447 | 40.0 | 2000 | 0.3649 | 0.6075 | 0.5934 | 0.6004 | 0.9133 |
| 0.0447 | 41.0 | 2050 | 0.4154 | 0.5907 | 0.6089 | 0.5996 | 0.9145 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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