---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: russian-BERT
  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. -->

# russian-BERT

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0126
- Accuracy: 0.8813
- Precision: 0.8813
- Recall: 0.8813
- Micro-avg-recall: 0.8813
- Micro-avg-precision: 0.8813

## 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: 2e-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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Micro-avg-recall | Micro-avg-precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:----------------:|:-------------------:|
| 0.0795        | 1.0   | 750  | 0.8896          | 0.8637   | 0.8665    | 0.8637 | 0.8637           | 0.8637              |
| 0.073         | 2.0   | 1500 | 0.8294          | 0.8633   | 0.8640    | 0.8633 | 0.8633           | 0.8633              |
| 0.0001        | 3.0   | 2250 | 0.9873          | 0.8757   | 0.8779    | 0.8757 | 0.8757           | 0.8757              |
| 0.0432        | 4.0   | 3000 | 0.9801          | 0.8813   | 0.8817    | 0.8813 | 0.8813           | 0.8813              |
| 0.0002        | 5.0   | 3750 | 1.0126          | 0.8813   | 0.8813    | 0.8813 | 0.8813           | 0.8813              |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1