---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base_finetuned_bluegennx_run2.19_5e
  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_5e

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: 0.0196
- Overall Precision: 0.9773
- Overall Recall: 0.9870
- Overall F1: 0.9822
- Overall Accuracy: 0.9957
- Aadhar Card F1: 0.9908
- Age F1: 0.9708
- City F1: 0.9879
- Country F1: 0.9825
- Creditcardcvv F1: 0.9915
- Creditcardnumber F1: 0.9428
- Date F1: 0.9626
- Dateofbirth F1: 0.9056
- Email F1: 0.9928
- Expirydate F1: 0.9898
- Organization F1: 0.9925
- Pan Card F1: 0.9866
- Person F1: 0.9887
- Phonenumber F1: 0.9880
- Pincode F1: 0.9897
- Secondaryaddress F1: 0.9891
- State F1: 0.9912
- Time F1: 0.9831
- Url F1: 0.9955

## 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: 5

### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:------:|:-------:|:----------:|:----------------:|:-------------------:|:-------:|:--------------:|:--------:|:-------------:|:---------------:|:-----------:|:---------:|:--------------:|:----------:|:-------------------:|:--------:|:-------:|:------:|
| 0.0356        | 1.0   | 15321 | 0.0383          | 0.9535            | 0.9675         | 0.9604     | 0.9915           | 0.9542         | 0.9221 | 0.9617  | 0.9816     | 0.9243           | 0.9195              | 0.9235  | 0.8262         | 0.9826   | 0.9477        | 0.9882          | 0.9529      | 0.9785    | 0.9684         | 0.9187     | 0.9734              | 0.9665   | 0.9723  | 0.9888 |
| 0.0231        | 2.0   | 30642 | 0.0265          | 0.9607            | 0.9814         | 0.9709     | 0.9937           | 0.9586         | 0.9437 | 0.9808  | 0.9821     | 0.9799           | 0.9006              | 0.9488  | 0.8788         | 0.9864   | 0.9768        | 0.9843          | 0.9837      | 0.9824    | 0.9809         | 0.9840     | 0.9820              | 0.9906   | 0.9749  | 0.9784 |
| 0.0182        | 3.0   | 45963 | 0.0219          | 0.9726            | 0.9854         | 0.9789     | 0.9951           | 0.9842         | 0.9631 | 0.9856  | 0.9843     | 0.9854           | 0.9424              | 0.9553  | 0.8962         | 0.9890   | 0.9878        | 0.9921          | 0.9869      | 0.9859    | 0.9815         | 0.9867     | 0.9884              | 0.9917   | 0.9767  | 0.9962 |
| 0.0106        | 4.0   | 61284 | 0.0196          | 0.9773            | 0.9870         | 0.9822     | 0.9957           | 0.9908         | 0.9708 | 0.9879  | 0.9825     | 0.9915           | 0.9428              | 0.9626  | 0.9056         | 0.9928   | 0.9898        | 0.9925          | 0.9866      | 0.9887    | 0.9880         | 0.9897     | 0.9891              | 0.9912   | 0.9831  | 0.9955 |
| 0.0044        | 5.0   | 76605 | 0.0214          | 0.9787            | 0.9876         | 0.9831     | 0.9959           | 0.9934         | 0.9710 | 0.9885  | 0.9846     | 0.9915           | 0.9453              | 0.9646  | 0.9125         | 0.9931   | 0.9898        | 0.9937          | 0.9875      | 0.9886    | 0.9893         | 0.9907     | 0.9903              | 0.9924   | 0.9837  | 0.9958 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2