blocker_detector – AI Model by Ayodelesamuel1 | AlphaNeural AI

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Ayodelesamuel1/
blocker_detector

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

text-embeddings-inference

endpoints_compatible

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blocker_detector

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1857
Accuracy: 0.9724
F1 Macro: 0.6748

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 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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
0.1290	1.0	90	0.0900	0.9685	0.492
0.1417	2.0	180	0.0905	0.9528	0.6544
0.0301	3.0	270	0.1158	0.9646	0.7261
0.0090	4.0	360	0.1403	0.9724	0.6748
0.0016	5.0	450	0.1868	0.9567	0.6222
0.0014	6.0	540	0.1769	0.9606	0.6327
0.0007	7.0	630	0.1827	0.9724	0.6748
0.0007	8.0	720	0.1861	0.9646	0.6448
0.0010	9.0	810	0.1849	0.9685	0.6586
0.0006	10.0	900	0.1857	0.9724	0.6748

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2