metadata

license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
model-index:
  - name: deberta-v3-base_finetuned_bluegennx_run2
    results: []

deberta-v3-base_finetuned_bluegennx_run2

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: 0.0737
Overall Precision: 0.7273
Overall Recall: 0.7428
Overall F1: 0.7350
Overall Accuracy: 0.9752
Aadhar F1: 0.8128
Age F1: 0.4700
City F1: 0.7686
Country F1: 0.7226
Creditcardcvv F1: 0.7531
Creditcardnumber F1: 0.8109
Date F1: 0.7126
Dateofbirth F1: 0.7262
Email F1: 0.6935
Expiry F1: 0.6621
Organization F1: 0.7623
Pan F1: 0.7772
Person F1: 0.7568
Phonenumber F1: 0.8194
Secondary F1: 0.6278
State F1: 0.7735
Time F1: 0.7856
Url F1: 0.5824

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: 8
eval_batch_size: 8
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 F1	Age F1	City F1	Country F1	Creditcardcvv F1	Creditcardnumber F1	Date F1	Dateofbirth F1	Email F1	Expiry F1	Organization F1	Pan F1	Person F1	Phonenumber F1	Secondary F1	State F1	Time F1	Url F1
0.1576	1.0	3893	0.1289	0.5166	0.5445	0.5302	0.9559	0.6073	0.1745	0.5790	0.5463	0.5707	0.6816	0.4834	0.4489	0.4808	0.5009	0.6085	0.5667	0.5383	0.5811	0.4273	0.6592	0.5824	0.2314
0.1075	2.0	7786	0.1151	0.5991	0.6001	0.5996	0.9610	0.7012	0.2439	0.6649	0.5689	0.6735	0.6950	0.5229	0.6065	0.5176	0.4904	0.6910	0.7248	0.5493	0.6810	0.5406	0.6382	0.6816	0.3492
0.0804	3.0	11679	0.0841	0.6783	0.7045	0.6911	0.9709	0.7826	0.3554	0.7372	0.6909	0.7276	0.7621	0.6459	0.7272	0.6303	0.6235	0.7329	0.7324	0.6816	0.7855	0.5912	0.7620	0.7529	0.4652
0.0532	4.0	15572	0.0737	0.7273	0.7428	0.7350	0.9752	0.8128	0.4700	0.7686	0.7226	0.7531	0.8109	0.7126	0.7262	0.6935	0.6621	0.7623	0.7772	0.7568	0.8194	0.6278	0.7735	0.7856	0.5824
0.0381	5.0	19465	0.0753	0.7372	0.7589	0.7479	0.9768	0.8278	0.4925	0.7705	0.7185	0.7832	0.8258	0.7231	0.7605	0.7027	0.6676	0.7700	0.8011	0.7591	0.8305	0.6558	0.7828	0.7978	0.6144

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2