Locally Name Verification

Introduction

A Transformer-based classifier that checks if a provided first/last name is likely to be real (LABEL_1) or fake (LABEL_0). This can be helpful in validating contact form submissions, preventing bot entries, or for general name classification tasks.

accuracy: 97.9%
model:
- base: ‘distilbert/distilbert-base-uncased’
- type: ‘text-classification’
license: ‘mit’

First Name Classification Model

A Transformer-based classifier that checks if a provided first name is likely to be real (LABEL_1) or fake (LABEL_0). This can be helpful in validating contact form submissions, preventing bot entries, or for general name classification tasks.

Project Structure

First_Name_Prediction/
├── .gitattributes
├── README.md
├── config.json
├── model.safetensors
├── requirements.txt
├── special_tokens_map.json
├── tokenizer.json
├── tokenizer_config.json
└── vocab.txt

Installation

Clone the Repository:

git clone https://github.com/Vishodi/First-Name-Classification.git

Set Up the Environment:

pip install -r requirements.txt

Usage

Python

from transformers import pipeline

# Replace with your model repository
model_dir = "vishodi/First-Name-Classification"

# Load the model pipeline with authentication
classifier = pipeline(
    "text-classification",
    model=model_dir,
    tokenizer=model_dir,
)

# Test the model
test_names = ["Mark", "vcbcvb", "uhyhu", "elon"]
for name in test_names:
    result = classifier(name)
    label = result[0]['label']
    score = result[0]['score']
    print(f"Name: {name} => Prediction: {label}, Score: {score:.4f}")

Example Output:

Name: Mark => Prediction: LABEL_1, Score: 0.9994
Name: vcbcvb => Prediction: LABEL_0, Score: 0.9985
Name: uhyhu => Prediction: LABEL_0, Score: 0.9982
Name: elon => Prediction: LABEL_1, Score: 0.9987

Last Name Classification Model

A Transformer-based classifier that checks if a provided last name is likely to be real (LABEL_1) or fake (LABEL_0). This can be helpful in validating contact form submissions, preventing bot entries, or for general name classification tasks.

Project Structure

Last_Name_Prediction/
├── .gitattributes
├── README.md
├── config.json
├── model.safetensors
├── requirements.txt
├── special_tokens_map.json
├── tokenizer.json
├── tokenizer_config.json
└── vocab.txt

Installation

Clone the Repository:

git clone https://github.com/Vishodi/Last-Name-Classification.git

Set Up the Environment:

pip install -r requirements.txt

Usage

Python

from transformers import pipeline

# Replace with your model repository
model_dir = "vishodi/Last-Name-Classification"

# Load the model pipeline with authentication
classifier = pipeline(
    "text-classification",
    model=model_dir,
    tokenizer=model_dir,
)

# Test the model
test_names = ["musk", "zzzzzz", "uhyhu", "trump"]
for name in test_names:
    result = classifier(name)
    label = result[0]['label']
    score = result[0]['score']
    print(f"Name: {name} => Prediction: {label}, Score: {score:.4f}")

Example Output:

Name: musk   => Prediction: LABEL_1, Score: 0.9167
Name: zzzzzz => Prediction: LABEL_0, Score: 0.9991
Name: uhyhu  => Prediction: LABEL_0, Score: 0.9944
Name: trump  => Prediction: LABEL_1, Score: 0.9998

License

This project is licensed under the MIT License.

Get Started

User Verification

Essentials

Introduction

First Name Classification Model

Project Structure

Installation

Usage

Last Name Classification Model

Project Structure

Installation

Usage

License

​Introduction

​First Name Classification Model

​Project Structure

​Installation

​Usage

​Last Name Classification Model

​Project Structure

​Installation

​Usage

​License

Introduction

First Name Classification Model

Project Structure

Installation

Usage

Last Name Classification Model

Project Structure

Installation

Usage

License