---
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: 0.1320
- Precision: 0.9254
- Recall: 0.9350
- F1: 0.9302
- Accuracy: 0.9800

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1711        | 1.0   | 477  | 0.0834          | 0.8308    | 0.8820 | 0.8556 | 0.9697   |
| 0.0642        | 2.0   | 954  | 0.0817          | 0.8323    | 0.8888 | 0.8596 | 0.9720   |
| 0.0401        | 3.0   | 1431 | 0.0935          | 0.8404    | 0.8930 | 0.8659 | 0.9720   |
| 0.0272        | 4.0   | 1908 | 0.1043          | 0.8506    | 0.8959 | 0.8727 | 0.9721   |
| 0.0148        | 5.0   | 2385 | 0.1051          | 0.9195    | 0.9309 | 0.9252 | 0.9793   |
| 0.007         | 6.0   | 2862 | 0.1181          | 0.9190    | 0.9298 | 0.9243 | 0.9789   |
| 0.0054        | 7.0   | 3339 | 0.1185          | 0.9250    | 0.9337 | 0.9293 | 0.9796   |
| 0.0027        | 8.0   | 3816 | 0.1252          | 0.9239    | 0.9342 | 0.9290 | 0.9801   |
| 0.0018        | 9.0   | 4293 | 0.1298          | 0.9247    | 0.9335 | 0.9291 | 0.9799   |
| 0.0015        | 10.0  | 4770 | 0.1320          | 0.9254    | 0.9350 | 0.9302 | 0.9800   |


### Framework versions

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