---
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: 0.1307
- Precision: 0.9299
- Recall: 0.9402
- F1: 0.9350
- Accuracy: 0.9805

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7194        | 0.9958  | 119  | 0.1195          | 0.7550    | 0.8328 | 0.7920 | 0.9602   |
| 0.103         | 2.0     | 239  | 0.0894          | 0.8341    | 0.8782 | 0.8556 | 0.9695   |
| 0.0517        | 2.9958  | 358  | 0.0761          | 0.9138    | 0.9321 | 0.9228 | 0.9792   |
| 0.0255        | 4.0     | 478  | 0.0921          | 0.9118    | 0.9287 | 0.9202 | 0.9778   |
| 0.016         | 4.9958  | 597  | 0.0945          | 0.9242    | 0.9343 | 0.9292 | 0.9794   |
| 0.0102        | 6.0     | 717  | 0.0978          | 0.9266    | 0.9382 | 0.9324 | 0.9801   |
| 0.0066        | 6.9958  | 836  | 0.1092          | 0.9265    | 0.9368 | 0.9316 | 0.9800   |
| 0.005         | 8.0     | 956  | 0.1150          | 0.9228    | 0.9366 | 0.9297 | 0.9796   |
| 0.0034        | 8.9958  | 1075 | 0.1189          | 0.9274    | 0.9373 | 0.9323 | 0.9800   |
| 0.003         | 10.0    | 1195 | 0.1242          | 0.9215    | 0.9360 | 0.9287 | 0.9793   |
| 0.0025        | 10.9958 | 1314 | 0.1288          | 0.9256    | 0.9375 | 0.9315 | 0.9797   |
| 0.0016        | 12.0    | 1434 | 0.1318          | 0.9273    | 0.9365 | 0.9319 | 0.9799   |
| 0.0015        | 12.9958 | 1553 | 0.1314          | 0.9286    | 0.9394 | 0.9340 | 0.9801   |
| 0.0013        | 14.0    | 1673 | 0.1308          | 0.9290    | 0.9393 | 0.9341 | 0.9803   |
| 0.0012        | 14.9372 | 1785 | 0.1307          | 0.9299    | 0.9402 | 0.9350 | 0.9805   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1