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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:

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The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.5153	1.0	1154	0.5267	0.7811	0.8214	0.7811	0.7825
0.2142	2.0	2308	0.2754	0.9223	0.9285	0.9223	0.9233
0.1629	3.0	3462	0.3064	0.9242	0.9337	0.9242	0.9258
0.0114	4.0	4616	0.2063	0.9546	0.9562	0.9546	0.9547
0.0168	5.0	5770	0.2266	0.9521	0.9540	0.9521	0.9523
0.0166	6.0	6924	0.2503	0.9543	0.9556	0.9543	0.9544
0.0716	7.0	8078	0.2382	0.9607	0.9618	0.9607	0.9609
0.0006	8.0	9232	0.2849	0.9536	0.9551	0.9536	0.9538
0.0001	9.0	10386	0.2501	0.9641	0.9649	0.9641	0.9642
0.0005	10.0	11540	0.2606	0.9610	0.9619	0.9610	0.9611