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---
language:
- mn
base_model: bayartsogt/mongolian-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner-demo
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-demo
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: 0.1261
- Precision: 0.9332
- Recall: 0.9397
- F1: 0.9364
- Accuracy: 0.9817
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1689 | 1.0 | 477 | 0.0718 | 0.9058 | 0.9211 | 0.9134 | 0.9784 |
| 0.0551 | 2.0 | 954 | 0.0718 | 0.9231 | 0.9311 | 0.9271 | 0.9808 |
| 0.0297 | 3.0 | 1431 | 0.0821 | 0.9303 | 0.9362 | 0.9332 | 0.9819 |
| 0.0166 | 4.0 | 1908 | 0.0946 | 0.9261 | 0.9318 | 0.9290 | 0.9802 |
| 0.0089 | 5.0 | 2385 | 0.0996 | 0.9266 | 0.9357 | 0.9311 | 0.9811 |
| 0.0061 | 6.0 | 2862 | 0.1183 | 0.9309 | 0.9392 | 0.9350 | 0.9812 |
| 0.0035 | 7.0 | 3339 | 0.1204 | 0.9353 | 0.9392 | 0.9372 | 0.9816 |
| 0.0025 | 8.0 | 3816 | 0.1202 | 0.9308 | 0.9391 | 0.9349 | 0.9815 |
| 0.0019 | 9.0 | 4293 | 0.1251 | 0.9329 | 0.9401 | 0.9365 | 0.9816 |
| 0.0013 | 10.0 | 4770 | 0.1261 | 0.9332 | 0.9397 | 0.9364 | 0.9817 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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