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albert-base-v2_fold_8 – AI Model by OliverHeine | AlphaNeural AI
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OliverHeine
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albert-base-v2_fold_8
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transformers
safetensors
albert
text-classification
generated_from_trainer
albert/albert-base-v2
finetune
apache-2.0
endpoints_compatible
us
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albert-base-v2_fold_8
This model is a fine-tuned version of
albert-base-v2
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1439
Accuracy: 0.9594
F1: 0.9554
Precision: 0.9596
Recall: 0.9512
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: 40
eval_batch_size: 40
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
0.1205
1.0
15481
0.1264
0.9510
0.9462
0.9504
0.9421
0.0986
2.0
30962
0.1204
0.9577
0.9533
0.9624
0.9445
0.0500
3.0
46443
0.1439
0.9594
0.9554
0.9596
0.9512
Framework versions
Transformers 5.3.0
Pytorch 2.10.0+cu128
Datasets 4.6.1
Tokenizers 0.22.2
