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deberta-v3-base_finetuned_bluegennx_run2.6 – AI Model by C4Scale | AlphaNeural AI
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C4Scale
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deberta-v3-base_finetuned_bluegennx_run2.6
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transformers
safetensors
deberta-v2
token-classification
generated_from_trainer
microsoft/deberta-v3-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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deberta-v3-base_finetuned_bluegennx_run2.6
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: 0.0968
Overall Precision: 0.6921
Overall Recall: 0.7279
Overall F1: 0.7096
Overall Accuracy: 0.9662
Aadhar Card F1: 0.7499
Age F1: 0.5816
City F1: 0.7306
Country F1: 0.6782
Creditcardcvv F1: 0.7220
Creditcardnumber F1: 0.7364
Currency F1: 0.6681
Currencyname F1: 0.0887
Date F1: 0.6695
Dateofbirth F1: 0.6437
Email F1: 0.6486
Expiry Date F1: 0.5623
Organization F1: 0.7393
Pan Card F1: 0.7191
Person F1: 0.8088
Phonenumber F1: 0.7218
Secondary Address F1: 0.6801
State F1: 0.7525
Street F1: 0.8529
Time F1: 0.7545
Url F1: 0.5520
Us Ssn F1: 0.9069
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: 36
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: 3
Training results
Framework versions
Transformers 4.38.2
Pytorch 2.1.0+cu118
Datasets 2.18.0
Tokenizers 0.15.2
