---
language:
- mn
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-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. -->

# xlm-roberta-base

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1144
- Precision: 0.9244
- Recall: 0.9343
- F1: 0.9293
- Accuracy: 0.9789

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1647        | 1.0   | 477  | 0.0849          | 0.8983    | 0.9111 | 0.9046 | 0.9749   |
| 0.0832        | 2.0   | 954  | 0.0877          | 0.9040    | 0.9193 | 0.9116 | 0.9752   |
| 0.0606        | 3.0   | 1431 | 0.0851          | 0.9101    | 0.9246 | 0.9173 | 0.9772   |
| 0.0459        | 4.0   | 1908 | 0.0857          | 0.9174    | 0.9255 | 0.9214 | 0.9776   |
| 0.0351        | 5.0   | 2385 | 0.0920          | 0.9189    | 0.9288 | 0.9238 | 0.9773   |
| 0.0265        | 6.0   | 2862 | 0.0979          | 0.9225    | 0.9323 | 0.9274 | 0.9786   |
| 0.0197        | 7.0   | 3339 | 0.1047          | 0.9204    | 0.9310 | 0.9257 | 0.9783   |
| 0.0154        | 8.0   | 3816 | 0.1088          | 0.9178    | 0.9319 | 0.9248 | 0.9782   |
| 0.0116        | 9.0   | 4293 | 0.1131          | 0.9255    | 0.9343 | 0.9299 | 0.9791   |
| 0.0096        | 10.0  | 4770 | 0.1144          | 0.9244    | 0.9343 | 0.9293 | 0.9789   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2