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---
license: mit
base_model: ai-forever/ruElectra-medium
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: training_results
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. -->
# training_results
This model is a fine-tuned version of [ai-forever/ruElectra-medium](https://huggingface.co/ai-forever/ruElectra-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6856
- Accuracy: 0.7135
- Recall: 0.6688
- Precision: 0.7321
- F1: 0.6855
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Accuracy | Recall | Precision | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 200 | 1.0503 | 0.6462 | 0.5404 | 0.5573 | 0.5309 |
| No log | 2.0 | 400 | 0.9312 | 0.6842 | 0.6358 | 0.6068 | 0.5981 |
| 0.9761 | 3.0 | 600 | 0.9141 | 0.7193 | 0.6410 | 0.6629 | 0.6447 |
| 0.9761 | 4.0 | 800 | 1.1036 | 0.7193 | 0.6453 | 0.6843 | 0.6516 |
| 0.3389 | 5.0 | 1000 | 1.3396 | 0.7135 | 0.6512 | 0.7203 | 0.6576 |
| 0.3389 | 6.0 | 1200 | 1.4660 | 0.7251 | 0.6688 | 0.7587 | 0.6759 |
| 0.3389 | 7.0 | 1400 | 1.4835 | 0.7135 | 0.6656 | 0.6910 | 0.6640 |
| 0.1627 | 8.0 | 1600 | 1.8635 | 0.7135 | 0.6535 | 0.7441 | 0.6673 |
| 0.1627 | 9.0 | 1800 | 1.5689 | 0.7368 | 0.7140 | 0.7412 | 0.7192 |
| 0.0893 | 10.0 | 2000 | 1.9628 | 0.7047 | 0.6885 | 0.7050 | 0.6842 |
| 0.0893 | 11.0 | 2200 | 1.9155 | 0.7339 | 0.6814 | 0.7328 | 0.6995 |
| 0.0893 | 12.0 | 2400 | 2.0020 | 0.7398 | 0.7086 | 0.7351 | 0.7064 |
| 0.0781 | 13.0 | 2600 | 2.0432 | 0.7193 | 0.7005 | 0.7265 | 0.6876 |
| 0.0781 | 14.0 | 2800 | 1.8877 | 0.7544 | 0.7385 | 0.7634 | 0.7415 |
| 0.0435 | 15.0 | 3000 | 2.2208 | 0.7281 | 0.6876 | 0.7271 | 0.6871 |
| 0.0435 | 16.0 | 3200 | 1.9514 | 0.7485 | 0.7071 | 0.7438 | 0.7169 |
| 0.0435 | 17.0 | 3400 | 2.0358 | 0.7368 | 0.7551 | 0.7406 | 0.7402 |
| 0.0405 | 18.0 | 3600 | 2.2364 | 0.7310 | 0.6250 | 0.6655 | 0.6307 |
| 0.0405 | 19.0 | 3800 | 2.3225 | 0.7164 | 0.6779 | 0.7234 | 0.6868 |
| 0.0511 | 20.0 | 4000 | 2.1369 | 0.7310 | 0.6826 | 0.7670 | 0.7089 |
| 0.0511 | 21.0 | 4200 | 2.2229 | 0.7427 | 0.6981 | 0.7783 | 0.7145 |
| 0.0511 | 22.0 | 4400 | 2.2711 | 0.7222 | 0.6650 | 0.7214 | 0.6671 |
| 0.0382 | 23.0 | 4600 | 2.4241 | 0.7222 | 0.6556 | 0.7826 | 0.6834 |
| 0.0382 | 24.0 | 4800 | 2.0575 | 0.7368 | 0.6767 | 0.7238 | 0.6804 |
| 0.0413 | 25.0 | 5000 | 2.5485 | 0.7076 | 0.6681 | 0.6842 | 0.6682 |
| 0.0413 | 26.0 | 5200 | 2.2235 | 0.7222 | 0.6474 | 0.6889 | 0.6536 |
| 0.0413 | 27.0 | 5400 | 2.5252 | 0.7105 | 0.6835 | 0.7028 | 0.6793 |
| 0.035 | 28.0 | 5600 | 2.5843 | 0.7164 | 0.6438 | 0.7341 | 0.6654 |
| 0.035 | 29.0 | 5800 | 2.6856 | 0.7135 | 0.6688 | 0.7321 | 0.6855 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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