---
language:
- km
license: apache-2.0
library_name: transformers
tags:
- generated_from_trainer
datasets:
- seanghay/khPOS
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: គាត់ផឹកទឹកនៅភ្នំពេញ
- text: តើលោកស្រីបានសាកសួរទៅគាត់ទេ?
- text: នេត្រា មិនដឹងសោះថាអ្នកជាមនុស្ស!
- text: លោក វណ្ណ ម៉ូលីវណ្ណ ជាបិតាស្ថាបត្យកម្មដ៏ល្បីល្បាញរបស់ប្រទេសកម្ពុជានៅក្នុងសម័យសង្គមរាស្ត្រនិយម។
pipeline_tag: token-classification
base_model: xlm-roberta-base
model-index:
- name: khmer-pos-roberta-10
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: kh_pos
      type: kh_pos
      config: default
      split: train
      args: default
    metrics:
    - type: precision
      value: 0.9511876225757245
      name: Precision
    - type: recall
      value: 0.9526407682234832
      name: Recall
    - type: f1
      value: 0.9519136408243376
      name: F1
    - type: accuracy
      value: 0.9735370853522176
      name: Accuracy
---

<!-- 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. -->

# Khmer Part of Speech Tagging with XLM RoBERTa

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [khPOS](https://huggingface.co/datasets/seanghay/khPOS) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1063
- Precision: 0.9512
- Recall: 0.9526
- F1: 0.9519
- Accuracy: 0.9735

## Model description

The [original paper](https://arxiv.org/pdf/2103.16801.pdf) achieved 98.15% accuracy while this model achieved only 97.35% which is close. However, this is a multilingual model so it has more tokens than the original paper.

## Intended uses & limitations

This model can be used to extract useful information from Khmer text.

## Training and evaluation data

train: 90% / test: 10%

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 450  | 0.1347          | 0.9314    | 0.9333 | 0.9324 | 0.9603   |
| 0.4834        | 2.0   | 900  | 0.1183          | 0.9407    | 0.9377 | 0.9392 | 0.9653   |
| 0.1323        | 3.0   | 1350 | 0.1026          | 0.9484    | 0.9482 | 0.9483 | 0.9699   |
| 0.095         | 4.0   | 1800 | 0.0986          | 0.9502    | 0.9490 | 0.9496 | 0.9712   |
| 0.0774        | 5.0   | 2250 | 0.0978          | 0.9494    | 0.9491 | 0.9493 | 0.9712   |
| 0.0616        | 6.0   | 2700 | 0.0991          | 0.9493    | 0.9507 | 0.9500 | 0.9715   |
| 0.0494        | 7.0   | 3150 | 0.0989          | 0.9529    | 0.9540 | 0.9534 | 0.9731   |
| 0.0414        | 8.0   | 3600 | 0.1037          | 0.9499    | 0.9501 | 0.9500 | 0.9722   |
| 0.0339        | 9.0   | 4050 | 0.1056          | 0.9516    | 0.9517 | 0.9516 | 0.9734   |
| 0.029         | 10.0  | 4500 | 0.1063          | 0.9512    | 0.9526 | 0.9519 | 0.9735   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3