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

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: 0.0737
- Overall Precision: 0.7273
- Overall Recall: 0.7428
- Overall F1: 0.7350
- Overall Accuracy: 0.9752
- Aadhar F1: 0.8128
- Age F1: 0.4700
- City F1: 0.7686
- Country F1: 0.7226
- Creditcardcvv F1: 0.7531
- Creditcardnumber F1: 0.8109
- Date F1: 0.7126
- Dateofbirth F1: 0.7262
- Email F1: 0.6935
- Expiry F1: 0.6621
- Organization F1: 0.7623
- Pan F1: 0.7772
- Person F1: 0.7568
- Phonenumber F1: 0.8194
- Secondary F1: 0.6278
- State F1: 0.7735
- Time F1: 0.7856
- Url F1: 0.5824

## 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: 8
- eval_batch_size: 8
- 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 F1 | Age F1 | City F1 | Country F1 | Creditcardcvv F1 | Creditcardnumber F1 | Date F1 | Dateofbirth F1 | Email F1 | Expiry F1 | Organization F1 | Pan F1 | Person F1 | Phonenumber F1 | Secondary F1 | State F1 | Time F1 | Url F1 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:---------:|:------:|:-------:|:----------:|:----------------:|:-------------------:|:-------:|:--------------:|:--------:|:---------:|:---------------:|:------:|:---------:|:--------------:|:------------:|:--------:|:-------:|:------:|
| 0.1576        | 1.0   | 3893  | 0.1289          | 0.5166            | 0.5445         | 0.5302     | 0.9559           | 0.6073    | 0.1745 | 0.5790  | 0.5463     | 0.5707           | 0.6816              | 0.4834  | 0.4489         | 0.4808   | 0.5009    | 0.6085          | 0.5667 | 0.5383    | 0.5811         | 0.4273       | 0.6592   | 0.5824  | 0.2314 |
| 0.1075        | 2.0   | 7786  | 0.1151          | 0.5991            | 0.6001         | 0.5996     | 0.9610           | 0.7012    | 0.2439 | 0.6649  | 0.5689     | 0.6735           | 0.6950              | 0.5229  | 0.6065         | 0.5176   | 0.4904    | 0.6910          | 0.7248 | 0.5493    | 0.6810         | 0.5406       | 0.6382   | 0.6816  | 0.3492 |
| 0.0804        | 3.0   | 11679 | 0.0841          | 0.6783            | 0.7045         | 0.6911     | 0.9709           | 0.7826    | 0.3554 | 0.7372  | 0.6909     | 0.7276           | 0.7621              | 0.6459  | 0.7272         | 0.6303   | 0.6235    | 0.7329          | 0.7324 | 0.6816    | 0.7855         | 0.5912       | 0.7620   | 0.7529  | 0.4652 |
| 0.0532        | 4.0   | 15572 | 0.0737          | 0.7273            | 0.7428         | 0.7350     | 0.9752           | 0.8128    | 0.4700 | 0.7686  | 0.7226     | 0.7531           | 0.8109              | 0.7126  | 0.7262         | 0.6935   | 0.6621    | 0.7623          | 0.7772 | 0.7568    | 0.8194         | 0.6278       | 0.7735   | 0.7856  | 0.5824 |
| 0.0381        | 5.0   | 19465 | 0.0753          | 0.7372            | 0.7589         | 0.7479     | 0.9768           | 0.8278    | 0.4925 | 0.7705  | 0.7185     | 0.7832           | 0.8258              | 0.7231  | 0.7605         | 0.7027   | 0.6676    | 0.7700          | 0.8011 | 0.7591    | 0.8305         | 0.6558       | 0.7828   | 0.7978  | 0.6144 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2