---
language:
- mn
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-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-large-ner-demo

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0976
- Precision: 0.9340
- Recall: 0.9404
- F1: 0.9372
- Accuracy: 0.9816

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1657        | 1.0   | 477  | 0.0866          | 0.8655    | 0.8978 | 0.8814 | 0.9752   |
| 0.0716        | 2.0   | 954  | 0.0801          | 0.9135    | 0.9283 | 0.9208 | 0.9796   |
| 0.0448        | 3.0   | 1431 | 0.0814          | 0.9244    | 0.9374 | 0.9309 | 0.9805   |
| 0.0283        | 4.0   | 1908 | 0.0870          | 0.9256    | 0.9367 | 0.9311 | 0.9808   |
| 0.017         | 5.0   | 2385 | 0.0976          | 0.9340    | 0.9404 | 0.9372 | 0.9816   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1