---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner-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. -->

# roberta-base-ner-demo

This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1349
- Precision: 0.9210
- Recall: 0.9330
- F1: 0.9269
- Accuracy: 0.9788

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1615        | 1.0   | 477  | 0.0917          | 0.8327    | 0.8817 | 0.8565 | 0.9680   |
| 0.0615        | 2.0   | 954  | 0.0917          | 0.8432    | 0.8918 | 0.8668 | 0.9701   |
| 0.0404        | 3.0   | 1431 | 0.0961          | 0.8411    | 0.8970 | 0.8682 | 0.9710   |
| 0.0227        | 4.0   | 1908 | 0.1056          | 0.9081    | 0.9262 | 0.9171 | 0.9770   |
| 0.0133        | 5.0   | 2385 | 0.1117          | 0.8781    | 0.9108 | 0.8942 | 0.9744   |
| 0.0083        | 6.0   | 2862 | 0.1231          | 0.9111    | 0.9288 | 0.9199 | 0.9775   |
| 0.0061        | 7.0   | 3339 | 0.1263          | 0.9110    | 0.9293 | 0.9200 | 0.9776   |
| 0.0071        | 8.0   | 3816 | 0.1316          | 0.9197    | 0.9302 | 0.9249 | 0.9783   |
| 0.0031        | 9.0   | 4293 | 0.1335          | 0.9228    | 0.9327 | 0.9277 | 0.9790   |
| 0.0021        | 10.0  | 4770 | 0.1349          | 0.9210    | 0.9330 | 0.9269 | 0.9788   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3