---
language:
- mn
base_model: tergel/bert-base-mongolian-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-mongolian-uncased-ner
  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. -->

# bert-base-mongolian-uncased-ner

This model is a fine-tuned version of [tergel/bert-base-mongolian-uncased](https://huggingface.co./tergel/bert-base-mongolian-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1610
- Precision: 0.8207
- Recall: 0.8426
- F1: 0.8315
- Accuracy: 0.9593

## 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: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3447        | 1.0   | 60   | 0.1578          | 0.7339    | 0.7793 | 0.7559 | 0.9468   |
| 0.136         | 2.0   | 120  | 0.1348          | 0.7915    | 0.8026 | 0.7970 | 0.9545   |
| 0.0997        | 3.0   | 180  | 0.1325          | 0.8020    | 0.8288 | 0.8152 | 0.9570   |
| 0.0761        | 4.0   | 240  | 0.1351          | 0.8086    | 0.8310 | 0.8196 | 0.9584   |
| 0.0595        | 5.0   | 300  | 0.1396          | 0.8173    | 0.8334 | 0.8253 | 0.9591   |
| 0.0485        | 6.0   | 360  | 0.1455          | 0.8084    | 0.8313 | 0.8197 | 0.9576   |
| 0.0399        | 7.0   | 420  | 0.1548          | 0.8135    | 0.8377 | 0.8254 | 0.9581   |
| 0.0354        | 8.0   | 480  | 0.1586          | 0.8179    | 0.8407 | 0.8292 | 0.9587   |
| 0.0315        | 9.0   | 540  | 0.1599          | 0.8165    | 0.8414 | 0.8288 | 0.9587   |
| 0.0283        | 10.0  | 600  | 0.1610          | 0.8207    | 0.8426 | 0.8315 | 0.9593   |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1