---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner
  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

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: 0.1328
- Precision: 0.9248
- Recall: 0.9325
- F1: 0.9286
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.17          | 1.0   | 477  | 0.0823          | 0.8652    | 0.9001 | 0.8823 | 0.9739   |
| 0.0567        | 2.0   | 954  | 0.0883          | 0.9070    | 0.9296 | 0.9182 | 0.9778   |
| 0.0278        | 3.0   | 1431 | 0.0904          | 0.9165    | 0.9302 | 0.9233 | 0.9789   |
| 0.0158        | 4.0   | 1908 | 0.0945          | 0.9220    | 0.9301 | 0.9260 | 0.9798   |
| 0.0089        | 5.0   | 2385 | 0.1118          | 0.9227    | 0.9287 | 0.9257 | 0.9799   |
| 0.0061        | 6.0   | 2862 | 0.1154          | 0.9212    | 0.9309 | 0.9260 | 0.9803   |
| 0.0037        | 7.0   | 3339 | 0.1240          | 0.9253    | 0.9320 | 0.9286 | 0.9806   |
| 0.0023        | 8.0   | 3816 | 0.1293          | 0.9232    | 0.9316 | 0.9274 | 0.9803   |
| 0.0013        | 9.0   | 4293 | 0.1323          | 0.9253    | 0.9332 | 0.9292 | 0.9806   |
| 0.0012        | 10.0  | 4770 | 0.1328          | 0.9248    | 0.9325 | 0.9286 | 0.9805   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1