---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base_finetuned_bluegennx_run2.19_2e
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# deberta-v3-base_finetuned_bluegennx_run2.19_2e

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/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