MixGPT2_10K_fromB_BFall_30KGen_topP_0.75_noaddedB – AI Model by AustinCarthy | AlphaNeural AI

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AustinCarthy/
MixGPT2_10K_fromB_BFall_30KGen_topP_0.75_noaddedB

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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MixGPT2_10K_fromB_BFall_30KGen_topP_0.75_noaddedB

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: 0.0595
Accuracy: 0.9942
F1: 0.9358
Precision: 0.9968
Recall: 0.8818
Roc Auc Score: 0.9408
Tpr At Fpr 0.01: 0.8582

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc Score	Tpr At Fpr 0.01
0.0134	1.0	7500	0.0271	0.9934	0.9272	0.9830	0.8774	0.9383	0.7528
0.0056	2.0	15000	0.0291	0.9946	0.9406	0.9907	0.8954	0.9475	0.8226
0.0038	3.0	22500	0.0312	0.9941	0.9341	0.9937	0.8812	0.9405	0.8302
0.0016	4.0	30000	0.0390	0.9951	0.9463	0.9945	0.9026	0.9512	0.852
0.0	5.0	37500	0.0595	0.9942	0.9358	0.9968	0.8818	0.9408	0.8582

Framework versions

Transformers 4.29.1
Pytorch 1.9.0+cu111
Datasets 2.10.1
Tokenizers 0.13.2