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

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: 0.1051
- Precision: 0.9154
- Recall: 0.9295
- F1: 0.9224
- Accuracy: 0.9778

## 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: 128
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4118        | 1.0   | 60   | 0.1230          | 0.7683    | 0.8344 | 0.8000 | 0.9584   |
| 0.1013        | 2.0   | 120  | 0.0996          | 0.8134    | 0.8677 | 0.8397 | 0.9649   |
| 0.0694        | 3.0   | 180  | 0.0961          | 0.8295    | 0.8783 | 0.8532 | 0.9676   |
| 0.0523        | 4.0   | 240  | 0.0861          | 0.9030    | 0.9198 | 0.9113 | 0.9762   |
| 0.0309        | 5.0   | 300  | 0.0847          | 0.9088    | 0.9239 | 0.9163 | 0.9775   |
| 0.0236        | 6.0   | 360  | 0.0950          | 0.9103    | 0.9253 | 0.9177 | 0.9772   |
| 0.019         | 7.0   | 420  | 0.0974          | 0.9158    | 0.9277 | 0.9217 | 0.9775   |
| 0.0153        | 8.0   | 480  | 0.0996          | 0.9139    | 0.9278 | 0.9208 | 0.9781   |
| 0.0122        | 9.0   | 540  | 0.1029          | 0.9143    | 0.9284 | 0.9213 | 0.9781   |
| 0.0104        | 10.0  | 600  | 0.1051          | 0.9154    | 0.9295 | 0.9224 | 0.9778   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2