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

# xlm-roberta-base-mongolian-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: 0.1166
- Precision: 0.9251
- Recall: 0.9335
- F1: 0.9293
- Accuracy: 0.9787

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2013        | 1.0   | 477  | 0.0958          | 0.8951    | 0.9124 | 0.9037 | 0.9731   |
| 0.0846        | 2.0   | 954  | 0.0825          | 0.9155    | 0.9240 | 0.9197 | 0.9774   |
| 0.0622        | 3.0   | 1431 | 0.0844          | 0.9109    | 0.9235 | 0.9172 | 0.9766   |
| 0.0456        | 4.0   | 1908 | 0.0940          | 0.9174    | 0.9266 | 0.9220 | 0.9767   |
| 0.0347        | 5.0   | 2385 | 0.1015          | 0.9184    | 0.9284 | 0.9234 | 0.9770   |
| 0.0253        | 6.0   | 2862 | 0.1117          | 0.9174    | 0.9254 | 0.9214 | 0.9764   |
| 0.0203        | 7.0   | 3339 | 0.1147          | 0.9225    | 0.9310 | 0.9267 | 0.9780   |
| 0.0152        | 8.0   | 3816 | 0.1129          | 0.9229    | 0.9316 | 0.9272 | 0.9779   |
| 0.0129        | 9.0   | 4293 | 0.1150          | 0.9245    | 0.9324 | 0.9285 | 0.9784   |
| 0.0102        | 10.0  | 4770 | 0.1166          | 0.9251    | 0.9335 | 0.9293 | 0.9787   |


### Framework versions

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