---
language:
- mn
base_model: bayartsogt/mongolian-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner-demo-turshilt3
  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-turshilt3

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: 0.1163
- Precision: 0.9235
- Recall: 0.9346
- F1: 0.9290
- Accuracy: 0.9806

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.78          | 0.9958  | 119  | 0.1344          | 0.7247    | 0.8054 | 0.7629 | 0.9518   |
| 0.1075        | 2.0     | 239  | 0.0992          | 0.8035    | 0.8679 | 0.8344 | 0.9656   |
| 0.0642        | 2.9958  | 358  | 0.0831          | 0.8306    | 0.8849 | 0.8569 | 0.9714   |
| 0.0412        | 4.0     | 478  | 0.0924          | 0.8641    | 0.9022 | 0.8827 | 0.9739   |
| 0.0214        | 4.9958  | 597  | 0.0918          | 0.9064    | 0.9225 | 0.9144 | 0.9778   |
| 0.0143        | 6.0     | 717  | 0.0932          | 0.9189    | 0.9301 | 0.9245 | 0.9801   |
| 0.01          | 6.9958  | 836  | 0.0951          | 0.9199    | 0.9325 | 0.9261 | 0.9803   |
| 0.0074        | 8.0     | 956  | 0.1077          | 0.9207    | 0.9299 | 0.9253 | 0.9795   |
| 0.0053        | 8.9958  | 1075 | 0.1081          | 0.9213    | 0.9329 | 0.9270 | 0.9805   |
| 0.0044        | 10.0    | 1195 | 0.1110          | 0.9223    | 0.9331 | 0.9276 | 0.9806   |
| 0.0037        | 10.9958 | 1314 | 0.1125          | 0.9273    | 0.9362 | 0.9317 | 0.9811   |
| 0.0027        | 12.0    | 1434 | 0.1146          | 0.9250    | 0.9344 | 0.9297 | 0.9807   |
| 0.0023        | 12.9958 | 1553 | 0.1155          | 0.9263    | 0.9361 | 0.9311 | 0.9812   |
| 0.0023        | 14.0    | 1673 | 0.1171          | 0.9242    | 0.9342 | 0.9292 | 0.9805   |
| 0.0021        | 14.9372 | 1785 | 0.1163          | 0.9235    | 0.9346 | 0.9290 | 0.9806   |


### Framework versions

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