README.md

metadata

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

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

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: 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