---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mongolian-roberta-base
  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. -->

# mongolian-roberta-base

This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1308
- Precision: 0.9243
- Recall: 0.9322
- F1: 0.9283
- Accuracy: 0.9799

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1632        | 1.0   | 477  | 0.0908          | 0.8293    | 0.8817 | 0.8547 | 0.9682   |
| 0.0607        | 2.0   | 954  | 0.0920          | 0.8506    | 0.8898 | 0.8698 | 0.9712   |
| 0.0331        | 3.0   | 1431 | 0.0975          | 0.9192    | 0.9267 | 0.9229 | 0.9779   |
| 0.0148        | 4.0   | 1908 | 0.1024          | 0.9179    | 0.9294 | 0.9236 | 0.9786   |
| 0.0087        | 5.0   | 2385 | 0.1091          | 0.9196    | 0.9296 | 0.9246 | 0.9796   |
| 0.0052        | 6.0   | 2862 | 0.1222          | 0.9240    | 0.9323 | 0.9281 | 0.9794   |
| 0.0033        | 7.0   | 3339 | 0.1233          | 0.9214    | 0.9317 | 0.9265 | 0.9796   |
| 0.0024        | 8.0   | 3816 | 0.1310          | 0.9250    | 0.9315 | 0.9282 | 0.9797   |
| 0.0016        | 9.0   | 4293 | 0.1308          | 0.9243    | 0.9322 | 0.9283 | 0.9799   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3