---
language:
- mn
base_model: bayartsogt/mongolian-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner-test-2
  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-test-2

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: 0.1207
- Precision: 0.9273
- Recall: 0.9357
- F1: 0.9315
- Accuracy: 0.9802

## 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: 128
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0259        | 1.0   | 60   | 0.0856          | 0.9222    | 0.9308 | 0.9265 | 0.9792   |
| 0.0145        | 2.0   | 120  | 0.0951          | 0.9200    | 0.9296 | 0.9248 | 0.9788   |
| 0.0104        | 3.0   | 180  | 0.1018          | 0.9143    | 0.9303 | 0.9222 | 0.9784   |
| 0.0073        | 4.0   | 240  | 0.1062          | 0.9224    | 0.9319 | 0.9272 | 0.9791   |
| 0.0068        | 5.0   | 300  | 0.1133          | 0.9246    | 0.9340 | 0.9293 | 0.9794   |
| 0.0108        | 6.0   | 360  | 0.1055          | 0.9207    | 0.9306 | 0.9256 | 0.9788   |
| 0.0078        | 7.0   | 420  | 0.1170          | 0.9207    | 0.9334 | 0.9270 | 0.9786   |
| 0.0061        | 8.0   | 480  | 0.1114          | 0.9226    | 0.9348 | 0.9286 | 0.9803   |
| 0.005         | 9.0   | 540  | 0.1165          | 0.9255    | 0.9341 | 0.9298 | 0.9798   |
| 0.0038        | 10.0  | 600  | 0.1207          | 0.9273    | 0.9357 | 0.9315 | 0.9802   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2