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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy 97.9% |
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base_model: |
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- distilbert/distilbert-base-uncased |
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pipeline_tag: text-classification |
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--- |
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# Last Name Classification Model |
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[](https://nowpayments.io/donation/Vishodi) |
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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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## Table of Contents |
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- [Project Structure](#project-structure) |
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- [Installation](#installation) |
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- [Usage](#usage) |
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- [Support Me](#support-me) |
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- [License](#license) |
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## Project Structure |
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```plaintext |
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Last_Name_Prediction/ |
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βββ .gitattributes |
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βββ README.md |
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βββ config.json |
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βββ model.safetensors |
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βββ requirements.txt |
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βββ special_tokens_map.json |
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βββ tokenizer.json |
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βββ tokenizer_config.json |
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βββ vocab.txt |
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``` |
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## Installation |
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1. **Clone the Repository:** |
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```bash |
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git clone https://github.com/Vishodi/Last-Name-Classification.git |
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``` |
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2. **Set Up the Environment:** |
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Install the required packages using pip: |
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```bash |
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pip install -r requirements.txt |
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``` |
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## Usage |
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```python |
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from transformers import pipeline |
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# Replace with your model repository |
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model_dir = "vishodi/Last-Name-Classification" |
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# Load the model pipeline with authentication |
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classifier = pipeline( |
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"text-classification", |
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model=model_dir, |
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tokenizer=model_dir, |
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) |
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# Test the model |
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test_names = ["musk", "zzzzzz", "uhyhu", "trump"] |
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for name in test_names: |
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result = classifier(name) |
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label = result[0]['label'] |
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score = result[0]['score'] |
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print(f"Name: {name} => Prediction: {label}, Score: {score:.4f}") |
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``` |
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**Output:** |
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``` |
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Name: musk => Prediction: LABEL_1, Score: 0.9167 |
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Name: zzzzzz => Prediction: LABEL_0, Score: 0.9991 |
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Name: uhyhu => Prediction: LABEL_0, Score: 0.9944 |
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Name: trump => Prediction: LABEL_1, Score: 0.9998 |
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``` |
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## Support Us |
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[](https://nowpayments.io/donation/Vishodi) |
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## License |
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This project is licensed under the MIT License. |