---
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: 0.1205
- Precision: 0.9307
- Recall: 0.9389
- F1: 0.9348
- Accuracy: 0.9816

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3889        | 1.0   | 477  | 0.0832          | 0.8808    | 0.8987 | 0.8897 | 0.9743   |
| 0.0736        | 2.0   | 954  | 0.0703          | 0.9170    | 0.9226 | 0.9198 | 0.9796   |
| 0.0361        | 3.0   | 1431 | 0.0784          | 0.9227    | 0.9321 | 0.9274 | 0.9801   |
| 0.0216        | 4.0   | 1908 | 0.0863          | 0.9235    | 0.9328 | 0.9281 | 0.9801   |
| 0.0116        | 5.0   | 2385 | 0.0977          | 0.9292    | 0.9371 | 0.9332 | 0.9809   |
| 0.007         | 6.0   | 2862 | 0.1071          | 0.9270    | 0.9356 | 0.9313 | 0.9808   |
| 0.0046        | 7.0   | 3339 | 0.1123          | 0.9322    | 0.9378 | 0.9350 | 0.9818   |
| 0.0029        | 8.0   | 3816 | 0.1179          | 0.9310    | 0.9371 | 0.9340 | 0.9814   |
| 0.0021        | 9.0   | 4293 | 0.1187          | 0.9293    | 0.9375 | 0.9334 | 0.9812   |
| 0.0013        | 10.0  | 4770 | 0.1205          | 0.9307    | 0.9389 | 0.9348 | 0.9816   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2