---
language:
- mn
base_model: bayartsogt/mongolian-roberta-base
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: 0.1227
- Precision: 0.9299
- Recall: 0.9375
- F1: 0.9337
- Accuracy: 0.9818

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1679        | 1.0   | 477  | 0.0860          | 0.8327    | 0.8878 | 0.8594 | 0.9716   |
| 0.0622        | 2.0   | 954  | 0.0739          | 0.9227    | 0.9300 | 0.9264 | 0.9807   |
| 0.029         | 3.0   | 1431 | 0.0770          | 0.9241    | 0.9347 | 0.9294 | 0.9820   |
| 0.0159        | 4.0   | 1908 | 0.0877          | 0.9327    | 0.9397 | 0.9362 | 0.9824   |
| 0.0091        | 5.0   | 2385 | 0.1074          | 0.9253    | 0.9324 | 0.9288 | 0.9805   |
| 0.0054        | 6.0   | 2862 | 0.1114          | 0.9277    | 0.9363 | 0.9320 | 0.9812   |
| 0.0042        | 7.0   | 3339 | 0.1137          | 0.9289    | 0.9359 | 0.9324 | 0.9815   |
| 0.0026        | 8.0   | 3816 | 0.1203          | 0.9301    | 0.9368 | 0.9334 | 0.9819   |
| 0.0021        | 9.0   | 4293 | 0.1222          | 0.9291    | 0.9373 | 0.9332 | 0.9818   |
| 0.0014        | 10.0  | 4770 | 0.1227          | 0.9299    | 0.9375 | 0.9337 | 0.9818   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1