---
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-mongolian-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: 0.1225
- Precision: 0.9338
- Recall: 0.9396
- F1: 0.9367
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.169         | 1.0   | 477  | 0.0846          | 0.8408    | 0.8852 | 0.8625 | 0.9713   |
| 0.0586        | 2.0   | 954  | 0.0753          | 0.9263    | 0.9347 | 0.9305 | 0.9801   |
| 0.0288        | 3.0   | 1431 | 0.0813          | 0.9262    | 0.9355 | 0.9308 | 0.9808   |
| 0.0158        | 4.0   | 1908 | 0.0937          | 0.9318    | 0.9384 | 0.9351 | 0.9814   |
| 0.0102        | 5.0   | 2385 | 0.0967          | 0.9331    | 0.9386 | 0.9358 | 0.9820   |
| 0.006         | 6.0   | 2862 | 0.1072          | 0.9318    | 0.9382 | 0.9350 | 0.9817   |
| 0.0046        | 7.0   | 3339 | 0.1139          | 0.9354    | 0.9408 | 0.9381 | 0.9821   |
| 0.0025        | 8.0   | 3816 | 0.1185          | 0.9341    | 0.9402 | 0.9371 | 0.9820   |
| 0.0021        | 9.0   | 4293 | 0.1217          | 0.9347    | 0.9397 | 0.9372 | 0.9819   |
| 0.0011        | 10.0  | 4770 | 0.1225          | 0.9338    | 0.9396 | 0.9367 | 0.9818   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3