metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base_finetuned_bluegennx_run2.19_2e
results: []
deberta-v3-base_finetuned_bluegennx_run2.19_2e
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: 0.0201
- Overall Precision: 0.9745
- Overall Recall: 0.9862
- Overall F1: 0.9803
- Overall Accuracy: 0.9952
- Aadhar Card F1: 0.9837
- Age F1: 0.9633
- City F1: 0.9842
- Country F1: 0.9843
- Creditcardcvv F1: 0.9879
- Creditcardnumber F1: 0.9416
- Date F1: 0.9600
- Dateofbirth F1: 0.9023
- Email F1: 0.9900
- Expirydate F1: 0.9912
- Organization F1: 0.9910
- Pan Card F1: 0.9867
- Person F1: 0.9878
- Phonenumber F1: 0.9858
- Pincode F1: 0.9907
- Secondaryaddress F1: 0.9878
- State F1: 0.9909
- Time F1: 0.9820
- 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: 2
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0261 | 1.0 | 15321 | 0.0287 | 0.9619 | 0.9781 | 0.9700 | 0.9934 | 0.9613 | 0.9463 | 0.9541 | 0.9832 | 0.9793 | 0.9270 | 0.9481 | 0.8767 | 0.9793 | 0.9809 | 0.9882 | 0.9751 | 0.9840 | 0.9747 | 0.9835 | 0.9831 | 0.9620 | 0.9780 | 0.9873 |
| 0.0152 | 2.0 | 30642 | 0.0201 | 0.9745 | 0.9862 | 0.9803 | 0.9952 | 0.9837 | 0.9633 | 0.9842 | 0.9843 | 0.9879 | 0.9416 | 0.9600 | 0.9023 | 0.9900 | 0.9912 | 0.9910 | 0.9867 | 0.9878 | 0.9858 | 0.9907 | 0.9878 | 0.9909 | 0.9820 | 0.9949 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2