---
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: 0.1352
- Precision: 0.9297
- Recall: 0.9366
- F1: 0.9331
- Accuracy: 0.9801

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1678        | 1.0   | 477  | 0.0929          | 0.8136    | 0.8806 | 0.8457 | 0.9679   |
| 0.0635        | 2.0   | 954  | 0.0894          | 0.8477    | 0.8933 | 0.8699 | 0.9708   |
| 0.0291        | 3.0   | 1431 | 0.0840          | 0.9262    | 0.9357 | 0.9309 | 0.9809   |
| 0.0163        | 4.0   | 1908 | 0.0928          | 0.9269    | 0.9357 | 0.9313 | 0.9805   |
| 0.0087        | 5.0   | 2385 | 0.1048          | 0.9259    | 0.9352 | 0.9305 | 0.9802   |
| 0.0059        | 6.0   | 2862 | 0.1179          | 0.9271    | 0.9339 | 0.9305 | 0.9794   |
| 0.0032        | 7.0   | 3339 | 0.1230          | 0.9278    | 0.9353 | 0.9316 | 0.9800   |
| 0.002         | 8.0   | 3816 | 0.1335          | 0.9285    | 0.9337 | 0.9311 | 0.9795   |
| 0.0016        | 9.0   | 4293 | 0.1341          | 0.9287    | 0.9358 | 0.9322 | 0.9799   |
| 0.0013        | 10.0  | 4770 | 0.1352          | 0.9297    | 0.9366 | 0.9331 | 0.9801   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0