---
language:
- mn
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mn-bert-base-demo-named-entity
  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. -->

# mn-bert-base-demo-named-entity

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1468
- Precision: 0.9092
- Recall: 0.9187
- F1: 0.9139
- Accuracy: 0.9757

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.172         | 1.0   | 477  | 0.1117          | 0.8614    | 0.8811 | 0.8711 | 0.9662   |
| 0.0846        | 2.0   | 954  | 0.1033          | 0.8748    | 0.8957 | 0.8852 | 0.9698   |
| 0.0562        | 3.0   | 1431 | 0.1005          | 0.8808    | 0.9024 | 0.8915 | 0.9716   |
| 0.0398        | 4.0   | 1908 | 0.1105          | 0.8978    | 0.9073 | 0.9025 | 0.9731   |
| 0.0276        | 5.0   | 2385 | 0.1181          | 0.9031    | 0.9121 | 0.9076 | 0.9740   |
| 0.0204        | 6.0   | 2862 | 0.1309          | 0.9039    | 0.9153 | 0.9096 | 0.9747   |
| 0.0138        | 7.0   | 3339 | 0.1322          | 0.9023    | 0.9132 | 0.9077 | 0.9745   |
| 0.0103        | 8.0   | 3816 | 0.1434          | 0.9071    | 0.9153 | 0.9112 | 0.9748   |
| 0.0071        | 9.0   | 4293 | 0.1458          | 0.9041    | 0.9156 | 0.9098 | 0.9750   |
| 0.0052        | 10.0  | 4770 | 0.1468          | 0.9092    | 0.9187 | 0.9139 | 0.9757   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3