metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - kh_pos
metrics:
  - precision
  - recall
  - f1
  - accuracy
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

khmer-pos-roberta-10

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