README.md

---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: turkic-cyrillic-classifier
  results: []
language:
- ba
- cv
- sah
- tt
- ky
- kk
- tyv
- krc
- ru
datasets:
- tatiana-merz/cyrillic_turkic_langs
pipeline_tag: text-classification
---

<!-- 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. -->

# turkic-cyrillic-classifier

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an tatiana-merz/cyrillic_turkic_langs dataset.
It achieves the following results on the evaluation set:


{'test_loss': 0.013604652136564255,
 'test_accuracy': 0.997,
 'test_f1': 0.9969996069718668,
 'test_runtime': 60.5479,
 'test_samples_per_second': 148.643,
 'test_steps_per_second': 2.329}

## Model description

The model classifies text based on a provided Turkic language written in Cyrillic script.

## Intended uses & limitations



## Training and evaluation data

[cyrillic_turkic_langs](https://huggingface.co/datasets/tatiana-merz/cyrillic_turkic_langs/)


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1063        | 1.0   | 1000 | 0.0204          | 0.9950   | 0.9950 |
| 0.0126        | 2.0   | 2000 | 0.0136          | 0.9970   | 0.9970 |


### Framework versions

- Transformers 4.27.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1