---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: testingModel
  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. -->

# testingModel

This model is a fine-tuned version of [Davlan/distilbert-base-multilingual-cased-ner-hrl](https://huggingface.co/Davlan/distilbert-base-multilingual-cased-ner-hrl) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1368
- Precision: 0.8763
- Recall: 0.9000
- F1: 0.8880
- Accuracy: 0.9738

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1975        | 1.0   | 477  | 0.1150          | 0.8257    | 0.8574 | 0.8412 | 0.9642   |
| 0.1001        | 2.0   | 954  | 0.1046          | 0.8515    | 0.8798 | 0.8654 | 0.9682   |
| 0.0655        | 3.0   | 1431 | 0.0980          | 0.8632    | 0.8905 | 0.8766 | 0.9719   |
| 0.0453        | 4.0   | 1908 | 0.1088          | 0.8590    | 0.8944 | 0.8763 | 0.9718   |
| 0.0324        | 5.0   | 2385 | 0.1142          | 0.8673    | 0.8951 | 0.8810 | 0.9719   |
| 0.0223        | 6.0   | 2862 | 0.1244          | 0.8814    | 0.9036 | 0.8924 | 0.9737   |
| 0.0173        | 7.0   | 3339 | 0.1252          | 0.8739    | 0.9007 | 0.8871 | 0.9733   |
| 0.0131        | 8.0   | 3816 | 0.1328          | 0.8721    | 0.8965 | 0.8841 | 0.9731   |
| 0.0097        | 9.0   | 4293 | 0.1362          | 0.8783    | 0.9002 | 0.8891 | 0.9737   |
| 0.008         | 10.0  | 4770 | 0.1368          | 0.8763    | 0.9000 | 0.8880 | 0.9738   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3