---
license: apache-2.0
base_model: ai-forever/ruBert-base
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/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1162
- Accuracy: 0.7193
- Recall: 0.7342
- Precision: 0.7262
- F1: 0.7271

## 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: 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  | 0.7908          | 0.7251   | 0.6546 | 0.6651    | 0.6511 |
| No log        | 2.0   | 400  | 0.9332          | 0.6988   | 0.6728 | 0.7010    | 0.6670 |
| 0.6701        | 3.0   | 600  | 1.0779          | 0.7427   | 0.7348 | 0.7912    | 0.7473 |
| 0.6701        | 4.0   | 800  | 1.2092          | 0.7427   | 0.6989 | 0.7237    | 0.7037 |
| 0.1446        | 5.0   | 1000 | 1.5001          | 0.7281   | 0.7049 | 0.7784    | 0.7231 |
| 0.1446        | 6.0   | 1200 | 1.5468          | 0.7368   | 0.7078 | 0.7808    | 0.7256 |
| 0.1446        | 7.0   | 1400 | 1.7923          | 0.7193   | 0.7284 | 0.7360    | 0.7230 |
| 0.0354        | 8.0   | 1600 | 1.7583          | 0.7339   | 0.7357 | 0.7462    | 0.7357 |
| 0.0354        | 9.0   | 1800 | 1.7942          | 0.7485   | 0.7203 | 0.7576    | 0.7308 |
| 0.0259        | 10.0  | 2000 | 1.9056          | 0.7310   | 0.7059 | 0.7337    | 0.7143 |
| 0.0259        | 11.0  | 2200 | 2.0351          | 0.7018   | 0.7047 | 0.6977    | 0.6952 |
| 0.0259        | 12.0  | 2400 | 1.6337          | 0.7602   | 0.7246 | 0.7861    | 0.7338 |
| 0.0262        | 13.0  | 2600 | 1.9012          | 0.7251   | 0.7056 | 0.7611    | 0.7148 |
| 0.0262        | 14.0  | 2800 | 2.0006          | 0.7339   | 0.7022 | 0.7625    | 0.7166 |
| 0.0302        | 15.0  | 3000 | 2.2857          | 0.6842   | 0.7072 | 0.6874    | 0.6766 |
| 0.0302        | 16.0  | 3200 | 2.0855          | 0.7310   | 0.7168 | 0.7557    | 0.7232 |
| 0.0302        | 17.0  | 3400 | 2.1281          | 0.7398   | 0.6868 | 0.7629    | 0.7132 |
| 0.0321        | 18.0  | 3600 | 2.1162          | 0.7193   | 0.7342 | 0.7262    | 0.7271 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1