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---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-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. -->
# roberta-base-ner
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: 0.1328
- Precision: 0.9248
- Recall: 0.9325
- F1: 0.9286
- Accuracy: 0.9805
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.17 | 1.0 | 477 | 0.0823 | 0.8652 | 0.9001 | 0.8823 | 0.9739 |
| 0.0567 | 2.0 | 954 | 0.0883 | 0.9070 | 0.9296 | 0.9182 | 0.9778 |
| 0.0278 | 3.0 | 1431 | 0.0904 | 0.9165 | 0.9302 | 0.9233 | 0.9789 |
| 0.0158 | 4.0 | 1908 | 0.0945 | 0.9220 | 0.9301 | 0.9260 | 0.9798 |
| 0.0089 | 5.0 | 2385 | 0.1118 | 0.9227 | 0.9287 | 0.9257 | 0.9799 |
| 0.0061 | 6.0 | 2862 | 0.1154 | 0.9212 | 0.9309 | 0.9260 | 0.9803 |
| 0.0037 | 7.0 | 3339 | 0.1240 | 0.9253 | 0.9320 | 0.9286 | 0.9806 |
| 0.0023 | 8.0 | 3816 | 0.1293 | 0.9232 | 0.9316 | 0.9274 | 0.9803 |
| 0.0013 | 9.0 | 4293 | 0.1323 | 0.9253 | 0.9332 | 0.9292 | 0.9806 |
| 0.0012 | 10.0 | 4770 | 0.1328 | 0.9248 | 0.9325 | 0.9286 | 0.9805 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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