metadata

base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
  - generated_from_trainer
datasets:
  - universal_dependencies
metrics:
  - accuracy
model-index:
  - name: wangchanberta-ud-thai-pud-upos
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: universal_dependencies
          type: universal_dependencies
          config: th_pud
          split: test
          args: th_pud
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9883334914161055

wangchanberta-ud-thai-pud-upos

This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the universal_dependencies dataset. It achieves the following results on the evaluation set:

Loss: 0.0442
Macro avg precision: 0.9221
Macro avg recall: 0.9178
Macro avg f1: 0.9199
Weighted avg precision: 0.9883
Weighted avg recall: 0.9883
Weighted avg f1: 0.9883
Accuracy: 0.9883

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: 8
eval_batch_size: 8
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	Macro avg precision	Macro avg recall	Macro avg f1	Weighted avg precision	Weighted avg recall	Weighted avg f1	Accuracy
No log	1.0	125	0.5563	0.8103	0.7235	0.7552	0.8574	0.8522	0.8495	0.8522
No log	2.0	250	0.2316	0.8701	0.8460	0.8564	0.9320	0.9315	0.9310	0.9315
No log	3.0	375	0.1635	0.8903	0.8729	0.8809	0.9511	0.9511	0.9508	0.9511
0.5782	4.0	500	0.1112	0.9037	0.8964	0.8998	0.9687	0.9685	0.9685	0.9685
0.5782	5.0	625	0.0860	0.9110	0.9050	0.9079	0.9752	0.9752	0.9751	0.9752
0.5782	6.0	750	0.0675	0.9160	0.9103	0.9131	0.9815	0.9814	0.9814	0.9814
0.5782	7.0	875	0.0588	0.9189	0.9138	0.9163	0.9839	0.9839	0.9839	0.9839
0.1073	8.0	1000	0.0514	0.9214	0.9155	0.9184	0.9858	0.9858	0.9858	0.9858
0.1073	9.0	1125	0.0463	0.9225	0.9171	0.9197	0.9877	0.9876	0.9876	0.9876
0.1073	10.0	1250	0.0442	0.9221	0.9178	0.9199	0.9883	0.9883	0.9883	0.9883

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1