---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mongolian-ner-test-xlm-roberta-large-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-ner-test-xlm-roberta-large-ner-hrl

This model is a fine-tuned version of [bayartsogt/albert-mongolian](https://huggingface.co/bayartsogt/albert-mongolian) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5337
- Precision: 0.3060
- Recall: 0.1406
- F1: 0.1927
- Accuracy: 0.8591

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6123        | 1.0   | 477  | 0.5570          | 0.2422    | 0.0999 | 0.1414 | 0.8536   |
| 0.5411        | 2.0   | 954  | 0.5407          | 0.2914    | 0.1294 | 0.1792 | 0.8572   |
| 0.5288        | 3.0   | 1431 | 0.5394          | 0.2944    | 0.1309 | 0.1812 | 0.8576   |
| 0.5212        | 4.0   | 1908 | 0.5346          | 0.3015    | 0.1324 | 0.1840 | 0.8581   |
| 0.5156        | 5.0   | 2385 | 0.5298          | 0.3131    | 0.1394 | 0.1929 | 0.8595   |
| 0.5103        | 6.0   | 2862 | 0.5301          | 0.3086    | 0.1419 | 0.1944 | 0.8595   |
| 0.5041        | 7.0   | 3339 | 0.5318          | 0.3083    | 0.1411 | 0.1936 | 0.8592   |
| 0.4981        | 8.0   | 3816 | 0.5308          | 0.3117    | 0.1421 | 0.1952 | 0.8595   |
| 0.4931        | 9.0   | 4293 | 0.5329          | 0.3062    | 0.1400 | 0.1922 | 0.8592   |
| 0.4885        | 10.0  | 4770 | 0.5337          | 0.3060    | 0.1406 | 0.1927 | 0.8591   |


### Framework versions

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