metadata

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

Khmer Part of Speech Tagging with XLM RoBERTa

This model is a fine-tuned version of xlm-roberta-base on the 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 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