---
library_name: transformers
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: rubert-tiny2-rus-MICRO
  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. -->

# rubert-tiny2-rus-MICRO

This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1485
- F1: 0.8458
- Roc Auc: 0.9005
- Accuracy: 0.7887

## 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: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.2588        | 1.0   | 607  | 0.2564          | 0.6892 | 0.7777  | 0.6469   |
| 0.1663        | 2.0   | 1214 | 0.1743          | 0.8322 | 0.8850  | 0.7668   |
| 0.1014        | 3.0   | 1821 | 0.1481          | 0.8399 | 0.8829  | 0.7912   |
| 0.0716        | 4.0   | 2428 | 0.1458          | 0.8433 | 0.8968  | 0.7861   |
| 0.0496        | 5.0   | 3035 | 0.1440          | 0.8423 | 0.8945  | 0.7835   |
| 0.0389        | 6.0   | 3642 | 0.1485          | 0.8458 | 0.9005  | 0.7887   |
| 0.037         | 7.0   | 4249 | 0.1538          | 0.8428 | 0.8998  | 0.7822   |
| 0.0218        | 8.0   | 4856 | 0.1623          | 0.8422 | 0.8997  | 0.7809   |
| 0.0196        | 9.0   | 5463 | 0.1678          | 0.8420 | 0.9007  | 0.7796   |
| 0.0204        | 10.0  | 6070 | 0.1743          | 0.8355 | 0.8967  | 0.7732   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0