videberta-base_1024 – AI Model by tmnam20 | AlphaNeural AI

You can deploy this model and start earning money today!

tmnam20/
videberta-base_1024

like

0

text-classification

generated_from_trainer

Fsoft-AIC/videberta-base

autotrain_compatible

endpoints_compatible

Views

No views yet

videberta-base_1024

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

Loss: 0.5931
Accuracy: 0.75

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: 0.0003
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.18
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5982	0.1	50	0.6297	0.75
0.5505	0.21	100	0.5696	0.75
0.5838	0.31	150	0.5629	0.75
0.5925	0.41	200	0.5931	0.75
0.7003	0.52	250	0.5931	0.75
0.606	0.62	300	0.5931	0.75
0.6744	0.72	350	0.5931	0.75
0.6448	0.83	400	0.5931	0.75
0.7365	0.93	450	0.5931	0.75
0.6083	1.03	500	0.5931	0.75
0.6217	1.14	550	0.5931	0.75
0.642	1.24	600	0.5931	0.75
0.6433	1.34	650	0.5931	0.75
0.7497	1.45	700	0.5931	0.75
0.6385	1.55	750	0.5931	0.75
0.6581	1.65	800	0.5931	0.75
0.6201	1.76	850	0.5931	0.75
0.6424	1.86	900	0.5931	0.75
0.619	1.96	950	0.5931	0.75
0.6807	2.07	1000	0.5931	0.75

Framework versions

Transformers 4.35.0.dev0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.14.1