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---
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
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