---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base_finetuned_bluegennx_run2.21_3e
  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_run2.21_3e

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: 0.0168
- 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