---
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: 0.1261
- Precision: 0.9332
- Recall: 0.9397
- F1: 0.9364
- Accuracy: 0.9817

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1689        | 1.0   | 477  | 0.0718          | 0.9058    | 0.9211 | 0.9134 | 0.9784   |
| 0.0551        | 2.0   | 954  | 0.0718          | 0.9231    | 0.9311 | 0.9271 | 0.9808   |
| 0.0297        | 3.0   | 1431 | 0.0821          | 0.9303    | 0.9362 | 0.9332 | 0.9819   |
| 0.0166        | 4.0   | 1908 | 0.0946          | 0.9261    | 0.9318 | 0.9290 | 0.9802   |
| 0.0089        | 5.0   | 2385 | 0.0996          | 0.9266    | 0.9357 | 0.9311 | 0.9811   |
| 0.0061        | 6.0   | 2862 | 0.1183          | 0.9309    | 0.9392 | 0.9350 | 0.9812   |
| 0.0035        | 7.0   | 3339 | 0.1204          | 0.9353    | 0.9392 | 0.9372 | 0.9816   |
| 0.0025        | 8.0   | 3816 | 0.1202          | 0.9308    | 0.9391 | 0.9349 | 0.9815   |
| 0.0019        | 9.0   | 4293 | 0.1251          | 0.9329    | 0.9401 | 0.9365 | 0.9816   |
| 0.0013        | 10.0  | 4770 | 0.1261          | 0.9332    | 0.9397 | 0.9364 | 0.9817   |


### Framework versions

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