---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Mongolian-xlm-roberta-base-ner-hrl
  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-hrl

This model is a fine-tuned version of [Davlan/xlm-roberta-base-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-base-ner-hrl) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1224
- Precision: 0.9303
- Recall: 0.9375
- F1: 0.9339
- Accuracy: 0.9794

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1449        | 1.0   | 477  | 0.0884          | 0.8968    | 0.9156 | 0.9061 | 0.9730   |
| 0.0737        | 2.0   | 954  | 0.0840          | 0.9205    | 0.9283 | 0.9244 | 0.9771   |
| 0.0503        | 3.0   | 1431 | 0.0843          | 0.9229    | 0.9312 | 0.9270 | 0.9788   |
| 0.0367        | 4.0   | 1908 | 0.0959          | 0.9232    | 0.9326 | 0.9279 | 0.9781   |
| 0.0268        | 5.0   | 2385 | 0.0991          | 0.9297    | 0.9357 | 0.9327 | 0.9797   |
| 0.02          | 6.0   | 2862 | 0.1067          | 0.9246    | 0.9316 | 0.9281 | 0.9783   |
| 0.0149        | 7.0   | 3339 | 0.1147          | 0.9265    | 0.9345 | 0.9305 | 0.9786   |
| 0.0115        | 8.0   | 3816 | 0.1193          | 0.9289    | 0.9362 | 0.9325 | 0.9795   |
| 0.0095        | 9.0   | 4293 | 0.1208          | 0.9304    | 0.9369 | 0.9336 | 0.9794   |
| 0.008         | 10.0  | 4770 | 0.1224          | 0.9303    | 0.9375 | 0.9339 | 0.9794   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3