bert-base-cased-2025 – AI Model by raulgdp | AlphaNeural AI

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raulgdp/
bert-base-cased-2025

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

endpoints_compatible

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bert-base-cased-2025

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: 0.1722
Precision: 0.7441
Recall: 0.775
F1: 0.7592
Accuracy: 0.9592

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: 16
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1764	1.0	521	0.1863	0.6678	0.7021	0.6845	0.9460
0.1187	2.0	1042	0.1648	0.7014	0.7371	0.7188	0.9536
0.0855	3.0	1563	0.1552	0.7224	0.7673	0.7442	0.9562
0.068	4.0	2084	0.1619	0.7308	0.7694	0.7496	0.9576
0.0531	5.0	2605	0.1641	0.7431	0.7764	0.7594	0.9594
0.0428	6.0	3126	0.1722	0.7441	0.775	0.7592	0.9592

Framework versions

Transformers 4.57.1
Pytorch 2.7.1+cu118
Datasets 4.2.0
Tokenizers 0.22.1