metadata

base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: wcBERTaAttSpmm-ggTranslate-senticPolarEmotion-bully-f2
    results: []

wcBERTaAttSpmm-ggTranslate-senticPolarEmotion-bully-f2

This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5206
Accuracy: 0.7354
Precision: 0.7320
Recall: 0.7354
F1 Score: 0.7335

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: 5e-06
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1 Score
0.6386	1.0	120	0.6121	0.7008	0.4912	0.7008	0.5776
0.603	2.0	240	0.5663	0.7144	0.6835	0.7144	0.6762
0.5758	3.0	360	0.5828	0.7029	0.6989	0.7029	0.7007
0.5724	4.0	480	0.5518	0.7238	0.6978	0.7238	0.6894
0.5412	5.0	600	0.5336	0.7082	0.6926	0.7082	0.6975
0.5295	6.0	720	0.5222	0.7280	0.7111	0.7280	0.7145
0.4851	7.0	840	0.5090	0.7312	0.7136	0.7312	0.7162
0.4639	8.0	960	0.5153	0.7259	0.7164	0.7259	0.7200
0.4606	9.0	1080	0.5233	0.7029	0.7241	0.7029	0.7104
0.4412	10.0	1200	0.5501	0.6998	0.7496	0.6998	0.7116
0.4106	11.0	1320	0.5262	0.7155	0.7398	0.7155	0.7235
0.3976	12.0	1440	0.5148	0.7374	0.7300	0.7374	0.7329
0.3917	13.0	1560	0.5194	0.7364	0.7338	0.7364	0.7350
0.3858	14.0	1680	0.5225	0.7259	0.7293	0.7259	0.7275
0.3853	15.0	1800	0.5206	0.7354	0.7320	0.7354	0.7335

Framework versions

Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1