---
language:
- mn
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mongolian-xlm-roberta-base-ner
  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-ner

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: 0.1298
- Precision: 0.9227
- Recall: 0.9298
- F1: 0.9262
- Accuracy: 0.9770

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.203         | 1.0   | 477  | 0.0961          | 0.8798    | 0.8986 | 0.8891 | 0.9708   |
| 0.0807        | 2.0   | 954  | 0.0912          | 0.8989    | 0.9173 | 0.9080 | 0.9734   |
| 0.0581        | 3.0   | 1431 | 0.0860          | 0.9087    | 0.9219 | 0.9152 | 0.9754   |
| 0.0433        | 4.0   | 1908 | 0.0954          | 0.9133    | 0.9255 | 0.9194 | 0.9763   |
| 0.0316        | 5.0   | 2385 | 0.1010          | 0.9183    | 0.9265 | 0.9224 | 0.9767   |
| 0.0234        | 6.0   | 2862 | 0.1077          | 0.9178    | 0.9286 | 0.9232 | 0.9770   |
| 0.0178        | 7.0   | 3339 | 0.1195          | 0.9223    | 0.9291 | 0.9257 | 0.9765   |
| 0.0142        | 8.0   | 3816 | 0.1263          | 0.9154    | 0.9280 | 0.9216 | 0.9767   |
| 0.0108        | 9.0   | 4293 | 0.1284          | 0.9204    | 0.9297 | 0.9250 | 0.9769   |
| 0.0088        | 10.0  | 4770 | 0.1298          | 0.9227    | 0.9298 | 0.9262 | 0.9770   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3