---
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: 0.1275
- Precision: 0.9333
- Recall: 0.9402
- F1: 0.9367
- Accuracy: 0.9817

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1686        | 1.0   | 477  | 0.0937          | 0.8103    | 0.8792 | 0.8433 | 0.9681   |
| 0.0648        | 2.0   | 954  | 0.0895          | 0.8268    | 0.8916 | 0.8580 | 0.9706   |
| 0.0396        | 3.0   | 1431 | 0.0925          | 0.8418    | 0.8954 | 0.8678 | 0.9722   |
| 0.0264        | 4.0   | 1908 | 0.1052          | 0.8469    | 0.8929 | 0.8693 | 0.9722   |
| 0.0199        | 5.0   | 2385 | 0.1211          | 0.8441    | 0.8964 | 0.8695 | 0.9725   |
| 0.0091        | 6.0   | 2862 | 0.1105          | 0.9308    | 0.9384 | 0.9346 | 0.9813   |
| 0.0042        | 7.0   | 3339 | 0.1156          | 0.9329    | 0.9391 | 0.9360 | 0.9816   |
| 0.003         | 8.0   | 3816 | 0.1230          | 0.9316    | 0.9383 | 0.9350 | 0.9814   |
| 0.0017        | 9.0   | 4293 | 0.1257          | 0.9301    | 0.9393 | 0.9347 | 0.9815   |
| 0.0013        | 10.0  | 4770 | 0.1275          | 0.9333    | 0.9402 | 0.9367 | 0.9817   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1