bert_callback_test – AI Model by iTroned | AlphaNeural AI | AlphaNeural AI

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iTroned/
bert_callback_test

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google-bert/bert-base-uncased

endpoints_compatible

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bert_callback_test

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2098
Accuracy Offensive: 0.9441
F1 Offensive: 0.9425
Accuracy Targeted: 0.9441
F1 Targeted: 0.9173
Accuracy Stance: 0.9079
F1 Stance: 0.8717

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Offensive	F1 Offensive	Accuracy Targeted	F1 Targeted	Accuracy Stance	F1 Stance
0.2639	1.0	1490	0.2196	0.9441	0.9425	0.9441	0.9173	0.9079	0.8717
0.2297	2.0	2980	0.2128	0.9441	0.9425	0.9441	0.9173	0.9079	0.8717
0.2168	3.0	4470	0.2098	0.9441	0.9425	0.9441	0.9173	0.9079	0.8717
0.2082	4.0	5960	0.2118	0.9441	0.9425	0.9441	0.9173	0.9079	0.8717
0.2142	5.0	7450	0.2107	0.9441	0.9425	0.9441	0.9173	0.9079	0.8717

Framework versions

Transformers 4.50.2
Pytorch 2.6.0+cu124
Datasets 3.0.1
Tokenizers 0.21.1