bert-small-spm – AI Model by schnell | AlphaNeural AI

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bert-small-spm

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bert-small-spm

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

Loss: 2.5919
Accuracy: 0.5095

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: 0.0001
train_batch_size: 256
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 3
total_train_batch_size: 768
total_eval_batch_size: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.01
num_epochs: 14
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.3946	1.0	69473	3.2473	0.4299
3.1526	2.0	138946	2.9987	0.4583
3.0496	3.0	208419	2.8875	0.4715
2.9923	4.0	277892	2.8258	0.4788
2.9429	5.0	347365	2.7765	0.4849
2.912	6.0	416838	2.7482	0.4890
2.8813	7.0	486311	2.7103	0.4938
2.8609	8.0	555784	2.6881	0.4963
2.8352	9.0	625257	2.6702	0.4991
2.8163	10.0	694730	2.6510	0.5010
2.8026	11.0	764203	2.6246	0.5046
2.7894	12.0	833676	2.6172	0.5055
2.7728	13.0	903149	2.5994	0.5083
2.761	14.0	972622	2.5919	0.5095

Framework versions

Transformers 4.19.2
Pytorch 1.12.0+cu116
Datasets 2.2.2
Tokenizers 0.12.1