---
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: 0.1321
- Precision: 0.9301
- Recall: 0.9415
- F1: 0.9358
- Accuracy: 0.9804

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1687        | 1.0   | 477  | 0.0866          | 0.8398    | 0.8904 | 0.8643 | 0.9712   |
| 0.0533        | 2.0   | 954  | 0.0790          | 0.9129    | 0.9279 | 0.9203 | 0.9777   |
| 0.0285        | 3.0   | 1431 | 0.0809          | 0.9263    | 0.9337 | 0.9300 | 0.9795   |
| 0.0164        | 4.0   | 1908 | 0.0932          | 0.9240    | 0.9374 | 0.9306 | 0.9794   |
| 0.0093        | 5.0   | 2385 | 0.1020          | 0.9281    | 0.9401 | 0.9341 | 0.9800   |
| 0.0055        | 6.0   | 2862 | 0.1137          | 0.9320    | 0.9424 | 0.9372 | 0.9808   |
| 0.0035        | 7.0   | 3339 | 0.1218          | 0.9265    | 0.9384 | 0.9325 | 0.9799   |
| 0.0026        | 8.0   | 3816 | 0.1240          | 0.9329    | 0.9422 | 0.9375 | 0.9809   |
| 0.0018        | 9.0   | 4293 | 0.1306          | 0.9297    | 0.9413 | 0.9355 | 0.9802   |
| 0.001         | 10.0  | 4770 | 0.1321          | 0.9301    | 0.9415 | 0.9358 | 0.9804   |


### Framework versions

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