---
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: 0.1566
- Precision: 0.6857
- Recall: 0.7725
- F1: 0.7265
- Accuracy: 0.9453

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9745        | 1.0   | 477  | 0.5080          | 0.2164    | 0.1205 | 0.1548 | 0.8187   |
| 0.425         | 2.0   | 954  | 0.3128          | 0.5213    | 0.5929 | 0.5548 | 0.9038   |
| 0.2943        | 3.0   | 1431 | 0.2337          | 0.5905    | 0.6781 | 0.6313 | 0.9237   |
| 0.2393        | 4.0   | 1908 | 0.2000          | 0.6303    | 0.7224 | 0.6732 | 0.9333   |
| 0.2134        | 5.0   | 2385 | 0.1813          | 0.6526    | 0.7434 | 0.6951 | 0.9384   |
| 0.1978        | 6.0   | 2862 | 0.1704          | 0.6629    | 0.7527 | 0.7050 | 0.9412   |
| 0.1885        | 7.0   | 3339 | 0.1647          | 0.6737    | 0.7625 | 0.7154 | 0.9429   |
| 0.1823        | 8.0   | 3816 | 0.1595          | 0.6816    | 0.7680 | 0.7222 | 0.9443   |
| 0.1792        | 9.0   | 4293 | 0.1576          | 0.6843    | 0.7713 | 0.7252 | 0.9451   |
| 0.1778        | 10.0  | 4770 | 0.1566          | 0.6857    | 0.7725 | 0.7265 | 0.9453   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1