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---
language:
- mn
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mongolian-xlm-roberta-base-demo
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-xlm-roberta-base-demo
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: 0.1177
- Precision: 0.9262
- Recall: 0.9332
- F1: 0.9297
- Accuracy: 0.9785
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1979 | 1.0 | 477 | 0.1015 | 0.8713 | 0.8958 | 0.8834 | 0.9692 |
| 0.0839 | 2.0 | 954 | 0.0965 | 0.9050 | 0.9125 | 0.9088 | 0.9743 |
| 0.0604 | 3.0 | 1431 | 0.0844 | 0.9217 | 0.9258 | 0.9237 | 0.9771 |
| 0.0455 | 4.0 | 1908 | 0.0955 | 0.9154 | 0.9283 | 0.9218 | 0.9774 |
| 0.0337 | 5.0 | 2385 | 0.0923 | 0.9228 | 0.9318 | 0.9273 | 0.9787 |
| 0.0254 | 6.0 | 2862 | 0.1055 | 0.9213 | 0.9303 | 0.9258 | 0.9776 |
| 0.02 | 7.0 | 3339 | 0.1075 | 0.9244 | 0.9329 | 0.9286 | 0.9785 |
| 0.0149 | 8.0 | 3816 | 0.1142 | 0.9262 | 0.9329 | 0.9295 | 0.9788 |
| 0.0126 | 9.0 | 4293 | 0.1149 | 0.9219 | 0.9306 | 0.9262 | 0.9780 |
| 0.01 | 10.0 | 4770 | 0.1177 | 0.9262 | 0.9332 | 0.9297 | 0.9785 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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