---
language:
- mn
base_model: bayartsogt/mongolian-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner-demo-turshilt2
  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-turshilt2

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: 0.1242
- Precision: 0.9296
- Recall: 0.9365
- F1: 0.9330
- Accuracy: 0.9802

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6386        | 0.9958  | 119  | 0.1340          | 0.7472    | 0.8012 | 0.7732 | 0.9536   |
| 0.1096        | 2.0     | 239  | 0.0939          | 0.8249    | 0.8791 | 0.8511 | 0.9686   |
| 0.0647        | 2.9958  | 358  | 0.0893          | 0.8356    | 0.8889 | 0.8614 | 0.9715   |
| 0.0455        | 4.0     | 478  | 0.0963          | 0.8452    | 0.8912 | 0.8676 | 0.9712   |
| 0.0306        | 4.9958  | 597  | 0.0909          | 0.9234    | 0.9314 | 0.9274 | 0.9795   |
| 0.0146        | 6.0     | 717  | 0.1046          | 0.9235    | 0.9302 | 0.9268 | 0.9789   |
| 0.0108        | 6.9958  | 836  | 0.1040          | 0.9204    | 0.9311 | 0.9257 | 0.9794   |
| 0.0079        | 8.0     | 956  | 0.1168          | 0.9245    | 0.9309 | 0.9277 | 0.9792   |
| 0.0063        | 8.9958  | 1075 | 0.1138          | 0.9258    | 0.9337 | 0.9297 | 0.9800   |
| 0.0051        | 10.0    | 1195 | 0.1165          | 0.9268    | 0.9330 | 0.9299 | 0.9800   |
| 0.0047        | 10.9958 | 1314 | 0.1199          | 0.9261    | 0.9359 | 0.9309 | 0.9803   |
| 0.0034        | 12.0    | 1434 | 0.1238          | 0.9284    | 0.9358 | 0.9321 | 0.9800   |
| 0.0027        | 12.9958 | 1553 | 0.1242          | 0.9267    | 0.9355 | 0.9311 | 0.9800   |
| 0.0026        | 14.0    | 1673 | 0.1232          | 0.9294    | 0.9365 | 0.9329 | 0.9804   |
| 0.0023        | 14.9372 | 1785 | 0.1242          | 0.9296    | 0.9365 | 0.9330 | 0.9802   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1