metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base_finetuned_bluegennx_run2.21_3e
results: []
deberta-v3-base_finetuned_bluegennx_run2.21_3e
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: 0.0168
- Overall Precision: 0.9773
- Overall Recall: 0.9878
- Overall F1: 0.9825
- Overall Accuracy: 0.9959
- Aadhar Card F1: 0.9866
- Age F1: 0.9707
- City F1: 0.9868
- Country F1: 0.9865
- Creditcardcvv F1: 0.9888
- Creditcardnumber F1: 0.9587
- Date F1: 0.9643
- Dateofbirth F1: 0.9165
- Email F1: 0.9894
- Expirydate F1: 0.9921
- Organization F1: 0.9917
- Pan Card F1: 0.9856
- Person F1: 0.9883
- Phonenumber F1: 0.9868
- Pincode F1: 0.9936
- Secondaryaddress F1: 0.9861
- State F1: 0.9901
- Time F1: 0.9821
- Url F1: 0.9949
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: 3
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0308 | 1.0 | 16005 | 0.0359 | 0.9500 | 0.9755 | 0.9626 | 0.9920 | 0.9350 | 0.9315 | 0.9658 | 0.9706 | 0.9631 | 0.9282 | 0.9251 | 0.8430 | 0.9719 | 0.9842 | 0.9866 | 0.9696 | 0.9772 | 0.9503 | 0.9835 | 0.9680 | 0.9815 | 0.9739 | 0.9845 |
| 0.0202 | 2.0 | 32010 | 0.0195 | 0.9737 | 0.9836 | 0.9786 | 0.9950 | 0.9756 | 0.9586 | 0.9790 | 0.9826 | 0.9866 | 0.9497 | 0.9593 | 0.9060 | 0.9893 | 0.9872 | 0.9902 | 0.9716 | 0.9875 | 0.9830 | 0.9939 | 0.9831 | 0.9871 | 0.9799 | 0.9927 |
| 0.0107 | 3.0 | 48015 | 0.0168 | 0.9773 | 0.9878 | 0.9825 | 0.9959 | 0.9866 | 0.9707 | 0.9868 | 0.9865 | 0.9888 | 0.9587 | 0.9643 | 0.9165 | 0.9894 | 0.9921 | 0.9917 | 0.9856 | 0.9883 | 0.9868 | 0.9936 | 0.9861 | 0.9901 | 0.9821 | 0.9949 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1