---
library_name: transformers
license: mit
base_model: somukandula/maskara-tiny
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: maskara-tiny
  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. -->

# maskara-tiny

This model is a fine-tuned version of [somukandula/maskara-tiny](https://huggingface.co/somukandula/maskara-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5828
- Precision: 0.8369
- Recall: 0.9271
- F1: 0.8797
- Overall F1: 0.8172
- Address F1: 0.6667
- Api Key F1: 1.0
- Credit Card F1: 0.0
- Date Of Birth F1: 1.0
- Driver License F1: 0.9875
- Email F1: 1.0
- Ip Address F1: 1.0
- Password F1: 0.0
- Person Name F1: 0.8378
- Phone F1: 0.8267
- Ssn F1: 0.0
- Username F1: 1.0
- Aadhaar F1: 0.9637
- Pan Card F1: 1.0
- Passport F1: 0.0
- Upi Id F1: 1.0
- Vehicle Reg F1: 0.975
- Indian Id F1: 0.9847

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Overall F1 | Address F1 | Api Key F1 | Credit Card F1 | Date Of Birth F1 | Driver License F1 | Email F1 | Ip Address F1 | Password F1 | Person Name F1 | Phone F1 | Ssn F1 | Username F1 | Aadhaar F1 | Pan Card F1 | Passport F1 | Upi Id F1 | Vehicle Reg F1 | Indian Id F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:----------:|:----------:|:----------:|:--------------:|:----------------:|:-----------------:|:--------:|:-------------:|:-----------:|:--------------:|:--------:|:------:|:-----------:|:----------:|:-----------:|:-----------:|:---------:|:--------------:|:------------:|
| 0.0006        | 1.0   | 14283 | 0.6201          | 0.7070    | 0.7883 | 0.7454 | 0.7770     | 0.9996     | 0.9888     | 0.2740         | 0.9882           | 0.5042            | 0.6678   | 0.9995        | 0.5025      | 0.8300         | 0.6457   | 1.0    | 0.9623      | 0.6661     | 0.8804      | 0.9544      | 0.9077    | 0.4370         | 0.7228       |
| 0.0003        | 2.0   | 28566 | 0.4654          | 0.7210    | 0.8069 | 0.7615 | 0.7900     | 0.9998     | 0.9880     | 0.3006         | 0.9835           | 0.5868            | 0.7152   | 0.9667        | 0.6032      | 0.8403         | 0.6424   | 1.0    | 0.9833      | 0.6575     | 0.8424      | 0.8378      | 0.9728    | 0.5097         | 0.7456       |
| 0.0003        | 3.0   | 42849 | 0.5373          | 0.7026    | 0.8087 | 0.7519 | 0.7755     | 0.9992     | 0.9857     | 0.2915         | 0.9859           | 0.5949            | 0.7581   | 0.9446        | 0.4776      | 0.8277         | 0.6466   | 1.0    | 0.9880      | 0.6732     | 0.8555      | 0.8258      | 0.9161    | 0.4128         | 0.7144       |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1