phobert-cls – AI Model by nhaantelex | AlphaNeural AI

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nhaantelex/
phobert-cls

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text-classification

generated_from_trainer

vinai/phobert-base

endpoints_compatible

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phobert-cls

This model is a fine-tuned version of vinai/phobert-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3035
Accuracy: 0.7478
F1: 0.7021
Precision: 0.6760
Recall: 0.7478

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: 1e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	325	1.5652	0.7695	0.6841	0.6759	0.7083
0.0620	2.0	650	1.5034	0.7270	0.6716	0.6606	0.7051
0.0620	3.0	975	1.3828	0.7487	0.6811	0.6561	0.7233
0.1215	4.0	1300	1.3519	0.7522	0.6869	0.6633	0.7248
0.1442	5.0	1625	1.3035	0.7478	0.7021	0.6760	0.7478

Framework versions

Transformers 5.5.4
Pytorch 2.10.0+cu128
Datasets 4.8.4
Tokenizers 0.22.2