metadata

base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: wcBERTaAttSpmm-ggTranslate-senticPolarEmotion-bully-f1
    results: []

wcBERTaAttSpmm-ggTranslate-senticPolarEmotion-bully-f1

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: 0.5229
Accuracy: 0.7448
Precision: 0.7291
Recall: 0.7448
F1 Score: 0.7303

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
0.551	1.0	120	0.5535	0.7427	0.7285	0.7427	0.7311
0.5476	2.0	240	0.5530	0.7406	0.7259	0.7406	0.7285
0.5352	3.0	360	0.5528	0.7354	0.7261	0.7354	0.7294
0.5482	4.0	480	0.5531	0.7312	0.7278	0.7312	0.7293
0.5386	5.0	600	0.5547	0.7228	0.7236	0.7228	0.7232
0.5391	6.0	720	0.5467	0.7427	0.7303	0.7427	0.7335
0.5495	7.0	840	0.5506	0.7395	0.7305	0.7395	0.7337
0.5305	8.0	960	0.5444	0.7427	0.7321	0.7427	0.7353
0.5183	9.0	1080	0.5326	0.7448	0.7320	0.7448	0.7349
0.5065	10.0	1200	0.5218	0.7479	0.7314	0.7479	0.7297
0.4753	11.0	1320	0.5207	0.7469	0.7317	0.7469	0.7330
0.4731	12.0	1440	0.5233	0.7458	0.7302	0.7458	0.7312
0.4828	13.0	1560	0.5243	0.7458	0.7302	0.7458	0.7312
0.4662	14.0	1680	0.5229	0.7458	0.7306	0.7458	0.7321
0.472	15.0	1800	0.5229	0.7448	0.7291	0.7448	0.7303

Framework versions

Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1