checkpoint – AI Model by namngo | AlphaNeural AI

You can deploy this model and start earning money today!

namngo/
checkpoint

like

0

multiple-choice

generated_from_trainer

vinai/phobert-base

endpoints_compatible

Views

No views yet

checkpoint

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: 1.1674
Accuracy: 0.4286

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-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.01
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	7	1.3602	0.5714
No log	2.0	14	1.3269	0.5714
No log	3.0	21	1.2438	0.2857
No log	4.0	28	1.1971	0.4286
No log	5.0	35	1.2036	0.2857
No log	6.0	42	1.1996	0.2857
No log	7.0	49	1.1651	0.4286
No log	8.0	56	1.1406	0.4286
No log	9.0	63	1.1620	0.4286
No log	10.0	70	1.1674	0.4286

Framework versions

Transformers 4.31.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3