---
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: 1.8438
- Accuracy: 0.7661
- Recall: 0.7479
- Precision: 0.7613
- F1: 0.7523

## 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   | 100  | 0.9047          | 0.7222   | 0.6078 | 0.6087    | 0.5979 |
| No log        | 2.0   | 200  | 0.7773          | 0.7427   | 0.6082 | 0.5795    | 0.5919 |
| No log        | 3.0   | 300  | 0.9403          | 0.7398   | 0.7074 | 0.7732    | 0.7198 |
| No log        | 4.0   | 400  | 1.1453          | 0.7135   | 0.6713 | 0.7322    | 0.6785 |
| 0.505         | 5.0   | 500  | 1.3685          | 0.7310   | 0.7011 | 0.7616    | 0.7131 |
| 0.505         | 6.0   | 600  | 1.3323          | 0.7310   | 0.7511 | 0.7179    | 0.7290 |
| 0.505         | 7.0   | 700  | 1.3571          | 0.7544   | 0.7280 | 0.7483    | 0.7283 |
| 0.505         | 8.0   | 800  | 1.4632          | 0.7368   | 0.7334 | 0.7298    | 0.7275 |
| 0.505         | 9.0   | 900  | 1.5987          | 0.7515   | 0.7474 | 0.7494    | 0.7429 |
| 0.0175        | 10.0  | 1000 | 1.5397          | 0.7778   | 0.7534 | 0.7902    | 0.7671 |
| 0.0175        | 11.0  | 1100 | 1.6137          | 0.7749   | 0.7731 | 0.7927    | 0.7784 |
| 0.0175        | 12.0  | 1200 | 1.6046          | 0.7778   | 0.7611 | 0.7916    | 0.7714 |
| 0.0175        | 13.0  | 1300 | 1.5817          | 0.7778   | 0.7591 | 0.7894    | 0.7706 |
| 0.0175        | 14.0  | 1400 | 1.6229          | 0.7865   | 0.7642 | 0.7965    | 0.7766 |
| 0.0035        | 15.0  | 1500 | 1.5925          | 0.7836   | 0.7620 | 0.7910    | 0.7733 |
| 0.0035        | 16.0  | 1600 | 1.6239          | 0.7836   | 0.7640 | 0.7922    | 0.7747 |
| 0.0035        | 17.0  | 1700 | 1.6805          | 0.7778   | 0.7564 | 0.7769    | 0.7643 |
| 0.0035        | 18.0  | 1800 | 1.7244          | 0.7719   | 0.7528 | 0.7622    | 0.7560 |
| 0.0035        | 19.0  | 1900 | 1.7410          | 0.7719   | 0.7561 | 0.7619    | 0.7576 |
| 0.0028        | 20.0  | 2000 | 1.7693          | 0.7690   | 0.7617 | 0.7579    | 0.7569 |
| 0.0028        | 21.0  | 2100 | 1.7823          | 0.7690   | 0.7520 | 0.7623    | 0.7542 |
| 0.0028        | 22.0  | 2200 | 1.7821          | 0.7719   | 0.7524 | 0.7652    | 0.7560 |
| 0.0028        | 23.0  | 2300 | 1.7932          | 0.7690   | 0.7510 | 0.7634    | 0.7546 |
| 0.0028        | 24.0  | 2400 | 1.8111          | 0.7690   | 0.7510 | 0.7703    | 0.7584 |
| 0.0017        | 25.0  | 2500 | 1.8289          | 0.7690   | 0.7494 | 0.7707    | 0.7580 |
| 0.0017        | 26.0  | 2600 | 1.8438          | 0.7661   | 0.7479 | 0.7613    | 0.7523 |


### Framework versions

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