---
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: 0.1304
- Precision: 0.9271
- Recall: 0.9357
- F1: 0.9314
- Accuracy: 0.9803

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1666        | 1.0   | 477  | 0.0838          | 0.8642    | 0.9063 | 0.8847 | 0.9749   |
| 0.0532        | 2.0   | 954  | 0.0818          | 0.9114    | 0.9271 | 0.9192 | 0.9780   |
| 0.0272        | 3.0   | 1431 | 0.0847          | 0.9178    | 0.9318 | 0.9247 | 0.9798   |
| 0.0148        | 4.0   | 1908 | 0.0945          | 0.9151    | 0.9321 | 0.9235 | 0.9796   |
| 0.0082        | 5.0   | 2385 | 0.1051          | 0.9269    | 0.9364 | 0.9316 | 0.9807   |
| 0.0053        | 6.0   | 2862 | 0.1092          | 0.9240    | 0.9365 | 0.9302 | 0.9807   |
| 0.0031        | 7.0   | 3339 | 0.1259          | 0.9262    | 0.9364 | 0.9312 | 0.9801   |
| 0.002         | 8.0   | 3816 | 0.1262          | 0.9270    | 0.9359 | 0.9314 | 0.9803   |
| 0.0012        | 9.0   | 4293 | 0.1305          | 0.9275    | 0.9367 | 0.9320 | 0.9805   |
| 0.0013        | 10.0  | 4770 | 0.1304          | 0.9271    | 0.9357 | 0.9314 | 0.9803   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1