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bert-base-uncased_fold_6 – AI Model by OliverHeine | AlphaNeural AI
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OliverHeine
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bert-base-uncased_fold_6
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transformers
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
text-embeddings-inference
endpoints_compatible
us
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bert-base-uncased_fold_6
This model is a fine-tuned version of
bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0347
Accuracy: 0.9945
F1: 0.9898
Precision: 0.9971
Recall: 0.9826
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: 38
eval_batch_size: 38
seed: 42
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
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
0.0318
1.0
3983
0.0386
0.9921
0.9853
0.9993
0.9716
0.0185
2.0
7966
0.0274
0.9948
0.9902
0.9975
0.9830
0.0131
3.0
11949
0.0347
0.9945
0.9898
0.9971
0.9826
Framework versions
Transformers 5.3.0
Pytorch 2.10.0+cu128
Datasets 4.6.1
Tokenizers 0.22.2
