bert-base-uncased-test – AI Model by tillschwoerer | AlphaNeural AI

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tillschwoerer/
bert-base-uncased-test

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

text-embeddings-inference

endpoints_compatible

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bert-base-uncased-test

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: 3.3027
Accuracy: 0.725
Precision: 0.7153
Recall: 0.7372
F1: 0.7210

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: 3e-05
train_batch_size: 16
eval_batch_size: 16
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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.0	1.0	30	3.2153	0.7333	0.7226	0.7478	0.7299
0.0	2.0	60	3.2642	0.725	0.7153	0.7372	0.7210
0.0	3.0	90	3.2930	0.725	0.7153	0.7372	0.7210
0.0	4.0	120	3.3027	0.725	0.7153	0.7372	0.7210

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu128
Datasets 4.2.0
Tokenizers 0.22.1