deberta-v3-base_finetuned_bluegennx_run2.19_5e – AI Model by C4Scale | AlphaNeural AI

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C4Scale/
deberta-v3-base_finetuned_bluegennx_run2.19_5e

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token-classification

generated_from_trainer

microsoft/deberta-v3-base

autotrain_compatible

endpoints_compatible

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deberta-v3-base_finetuned_bluegennx_run2.19_5e

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0196
Overall Precision: 0.9773
Overall Recall: 0.9870
Overall F1: 0.9822
Overall Accuracy: 0.9957
Aadhar Card F1: 0.9908
Age F1: 0.9708
City F1: 0.9879
Country F1: 0.9825
Creditcardcvv F1: 0.9915
Creditcardnumber F1: 0.9428
Date F1: 0.9626
Dateofbirth F1: 0.9056
Email F1: 0.9928
Expirydate F1: 0.9898
Organization F1: 0.9925
Pan Card F1: 0.9866
Person F1: 0.9887
Phonenumber F1: 0.9880
Pincode F1: 0.9897
Secondaryaddress F1: 0.9891
State F1: 0.9912
Time F1: 0.9831
Url F1: 0.9955

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: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_ratio: 0.2
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Aadhar Card F1	Age F1	City F1	Country F1	Creditcardcvv F1	Creditcardnumber F1	Date F1	Dateofbirth F1	Email F1	Expirydate F1	Organization F1	Pan Card F1	Person F1	Phonenumber F1	Pincode F1	Secondaryaddress F1	State F1	Time F1	Url F1
0.0356	1.0	15321	0.0383	0.9535	0.9675	0.9604	0.9915	0.9542	0.9221	0.9617	0.9816	0.9243	0.9195	0.9235	0.8262	0.9826	0.9477	0.9882	0.9529	0.9785	0.9684	0.9187	0.9734	0.9665	0.9723	0.9888
0.0231	2.0	30642	0.0265	0.9607	0.9814	0.9709	0.9937	0.9586	0.9437	0.9808	0.9821	0.9799	0.9006	0.9488	0.8788	0.9864	0.9768	0.9843	0.9837	0.9824	0.9809	0.9840	0.9820	0.9906	0.9749	0.9784
0.0182	3.0	45963	0.0219	0.9726	0.9854	0.9789	0.9951	0.9842	0.9631	0.9856	0.9843	0.9854	0.9424	0.9553	0.8962	0.9890	0.9878	0.9921	0.9869	0.9859	0.9815	0.9867	0.9884	0.9917	0.9767	0.9962
0.0106	4.0	61284	0.0196	0.9773	0.9870	0.9822	0.9957	0.9908	0.9708	0.9879	0.9825	0.9915	0.9428	0.9626	0.9056	0.9928	0.9898	0.9925	0.9866	0.9887	0.9880	0.9897	0.9891	0.9912	0.9831	0.9955
0.0044	5.0	76605	0.0214	0.9787	0.9876	0.9831	0.9959	0.9934	0.9710	0.9885	0.9846	0.9915	0.9453	0.9646	0.9125	0.9931	0.9898	0.9937	0.9875	0.9886	0.9893	0.9907	0.9903	0.9924	0.9837	0.9958

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2