---
language:
- mn
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-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. -->

# xlm-roberta-base-ner-demo

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0988
- Precision: 0.9333
- Recall: 0.9387
- F1: 0.9360
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.178         | 1.0   | 572  | 0.0840          | 0.8985    | 0.9121 | 0.9052 | 0.9751   |
| 0.0801        | 2.0   | 1144 | 0.0744          | 0.9095    | 0.9262 | 0.9178 | 0.9780   |
| 0.0566        | 3.0   | 1716 | 0.0797          | 0.9230    | 0.9312 | 0.9271 | 0.9787   |
| 0.0436        | 4.0   | 2288 | 0.0734          | 0.9157    | 0.9302 | 0.9229 | 0.9795   |
| 0.0333        | 5.0   | 2860 | 0.0871          | 0.9152    | 0.9270 | 0.9210 | 0.9772   |
| 0.0254        | 6.0   | 3432 | 0.0848          | 0.9216    | 0.9307 | 0.9261 | 0.9792   |
| 0.0186        | 7.0   | 4004 | 0.0992          | 0.9302    | 0.9367 | 0.9334 | 0.9806   |
| 0.0142        | 8.0   | 4576 | 0.0989          | 0.9288    | 0.9357 | 0.9322 | 0.9801   |
| 0.0119        | 9.0   | 5148 | 0.0965          | 0.9331    | 0.9387 | 0.9359 | 0.9807   |
| 0.0103        | 10.0  | 5720 | 0.0988          | 0.9333    | 0.9387 | 0.9360 | 0.9803   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3