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HRVibeCheck-BERT – AI Model by Cheykong | AlphaNeural AI
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Cheykong
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HRVibeCheck-BERT
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transformers
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
endpoints_compatible
us
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HRVibeCheck-BERT
This model is a fine-tuned version of
google-bert/bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.4412
Accuracy: 0.8040
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: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
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
lr_scheduler_warmup_steps: 0.1
num_epochs: 4
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
5.3513
1.0
310
0.6149
0.6935
4.5175
2.0
620
0.4793
0.7653
3.9709
3.0
930
0.4676
0.7685
2.9567
4.0
1240
0.4412
0.8040
Framework versions
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2
