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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’

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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.

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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

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Installation

  1. Clone the Repository:
git clone https://github.com/Vishodi/First-Name-Classification.git
  1. Set Up the Environment:
pip install -r requirements.txt

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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

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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.

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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

  1. Clone the Repository:
git clone https://github.com/Vishodi/Last-Name-Classification.git
  1. 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

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License

This project is licensed under the MIT License.
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