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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: 0.1307
- Precision: 0.9299
- Recall: 0.9402
- F1: 0.9350
- 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7194 | 0.9958 | 119 | 0.1195 | 0.7550 | 0.8328 | 0.7920 | 0.9602 |
| 0.103 | 2.0 | 239 | 0.0894 | 0.8341 | 0.8782 | 0.8556 | 0.9695 |
| 0.0517 | 2.9958 | 358 | 0.0761 | 0.9138 | 0.9321 | 0.9228 | 0.9792 |
| 0.0255 | 4.0 | 478 | 0.0921 | 0.9118 | 0.9287 | 0.9202 | 0.9778 |
| 0.016 | 4.9958 | 597 | 0.0945 | 0.9242 | 0.9343 | 0.9292 | 0.9794 |
| 0.0102 | 6.0 | 717 | 0.0978 | 0.9266 | 0.9382 | 0.9324 | 0.9801 |
| 0.0066 | 6.9958 | 836 | 0.1092 | 0.9265 | 0.9368 | 0.9316 | 0.9800 |
| 0.005 | 8.0 | 956 | 0.1150 | 0.9228 | 0.9366 | 0.9297 | 0.9796 |
| 0.0034 | 8.9958 | 1075 | 0.1189 | 0.9274 | 0.9373 | 0.9323 | 0.9800 |
| 0.003 | 10.0 | 1195 | 0.1242 | 0.9215 | 0.9360 | 0.9287 | 0.9793 |
| 0.0025 | 10.9958 | 1314 | 0.1288 | 0.9256 | 0.9375 | 0.9315 | 0.9797 |
| 0.0016 | 12.0 | 1434 | 0.1318 | 0.9273 | 0.9365 | 0.9319 | 0.9799 |
| 0.0015 | 12.9958 | 1553 | 0.1314 | 0.9286 | 0.9394 | 0.9340 | 0.9801 |
| 0.0013 | 14.0 | 1673 | 0.1308 | 0.9290 | 0.9393 | 0.9341 | 0.9803 |
| 0.0012 | 14.9372 | 1785 | 0.1307 | 0.9299 | 0.9402 | 0.9350 | 0.9805 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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