---
language:
- mn
base_model: bayartsogt/mongolian-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner-demo-turshilt4
  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-turshilt4

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: 0.1101
- Precision: 0.9354
- Recall: 0.9427
- F1: 0.9390
- Accuracy: 0.9815

## 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: 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 |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9016        | 0.9958  | 119  | 0.1663          | 0.7064    | 0.7773 | 0.7401 | 0.9416   |
| 0.1182        | 2.0     | 239  | 0.1002          | 0.8118    | 0.8752 | 0.8423 | 0.9656   |
| 0.064         | 2.9958  | 358  | 0.0733          | 0.9162    | 0.9282 | 0.9221 | 0.9788   |
| 0.0366        | 4.0     | 478  | 0.0826          | 0.9221    | 0.9297 | 0.9259 | 0.9791   |
| 0.0233        | 4.9958  | 597  | 0.0843          | 0.9251    | 0.9359 | 0.9305 | 0.9798   |
| 0.0157        | 6.0     | 717  | 0.0898          | 0.9305    | 0.9395 | 0.9350 | 0.9802   |
| 0.0121        | 6.9958  | 836  | 0.0964          | 0.9269    | 0.9369 | 0.9319 | 0.9795   |
| 0.0093        | 8.0     | 956  | 0.0995          | 0.9332    | 0.9408 | 0.9370 | 0.9805   |
| 0.0072        | 8.9958  | 1075 | 0.1046          | 0.9306    | 0.9387 | 0.9346 | 0.9799   |
| 0.0057        | 10.0    | 1195 | 0.1060          | 0.9332    | 0.9410 | 0.9371 | 0.9804   |
| 0.0049        | 10.9958 | 1314 | 0.1052          | 0.9322    | 0.9399 | 0.9360 | 0.9810   |
| 0.0042        | 12.0    | 1434 | 0.1091          | 0.9350    | 0.9416 | 0.9383 | 0.9811   |
| 0.0037        | 12.9958 | 1553 | 0.1098          | 0.9350    | 0.9414 | 0.9382 | 0.9812   |
| 0.0035        | 14.0    | 1673 | 0.1099          | 0.9362    | 0.9426 | 0.9394 | 0.9815   |
| 0.0033        | 14.9372 | 1785 | 0.1101          | 0.9354    | 0.9427 | 0.9390 | 0.9815   |


### Framework versions

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