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BERT_test_graident_accumulation – AI Model by BrianHsu | AlphaNeural AI
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BERT_test_graident_accumulation
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transformers
tensorboard
safetensors
bert
multiple-choice
generated_from_trainer
google-bert/bert-base-chinese
finetune
endpoints_compatible
us
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BERT_test_graident_accumulation
This model is a fine-tuned version of
bert-base-chinese
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.3780
Accuracy: 0.6384
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
1.0
188
0.9354
0.5987
No log
2.0
376
0.9827
0.6208
0.7728
3.0
564
1.1462
0.6298
0.7728
4.0
752
1.3019
0.6323
0.7728
5.0
940
1.3780
0.6384
Framework versions
Transformers 4.36.0
Pytorch 2.1.1+cu118
Datasets 2.15.0
Tokenizers 0.15.0
