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@@ -8,24 +8,20 @@ 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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- # <h2>Last Name Classification Model</h2>
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- [![Support](https://img.shields.io/badge/Support-Me-brightgreen)](https://www.example.com/donate?crypto=YOUR_CRYPTO_ID)
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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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-
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- ---
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  ## Table of Contents
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-
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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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-
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- ```
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  Last_Name_Prediction/
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  β”œβ”€β”€ .gitattributes
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  β”œβ”€β”€ README.md
@@ -36,72 +32,54 @@ Last_Name_Prediction/
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  β”œβ”€β”€ tokenizer.json
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  β”œβ”€β”€ tokenizer_config.json
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  └── vocab.txt
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-
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  ```
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- ---
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  ## Installation
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-
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  1. **Clone the Repository:**
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-
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- ```bash
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- git clone https://github.com/your_username/name-validation-ai.git
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- cd name-validation-ai
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- ```
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  2. **Set Up the Environment:**
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-
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  Install the required packages using pip:
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-
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- ```bash
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- pip install -r requirements.txt
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- ```
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- ---
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  ## Usage
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- Python
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  ```python
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  from transformers import pipeline
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- # Replace with your model directory or Hugging Face model hub link
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- model_dir = "/kaggle/input/name-dataset/transformers_name_classifier_safetensors"
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- # Load the model pipeline
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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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- framework="pt"
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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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- ```bash
 
 
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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 Me
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  If you find this project helpful and would like to support my work, please consider donating using crypto.
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- <a href="https://www.example.com/donate?crypto=YOUR_CRYPTO_ID" target="_blank">
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- <img src="https://img.shields.io/badge/Support-Me-brightgreen" alt="Support Me">
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- </a>
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-
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- ---
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  ## License
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-
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- This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
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-
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- ---
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-
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- *Contributions, issues, and feature requests are welcome! Feel free to fork the repository and open a pull request.*
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- ```
 
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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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+ [![Support](https://img.shields.io/badge/Support-Us-brightgreen)](https://nowpayments.io/donation/Vishodi)
13
 
14
+ 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.
 
15
 
 
 
 
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  ## Table of Contents
17
+ - [Project Structure](#project-structure)
 
18
  - [Installation](#installation)
19
  - [Usage](#usage)
20
  - [Support Me](#support-me)
21
  - [License](#license)
22
 
23
  ## Project Structure
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+ ```plaintext
 
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  Last_Name_Prediction/
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  β”œβ”€β”€ .gitattributes
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  β”œβ”€β”€ README.md
 
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  β”œβ”€β”€ tokenizer.json
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  β”œβ”€β”€ tokenizer_config.json
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  └── 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:**
 
44
  Install the required packages using pip:
45
+ ```bash
46
+ 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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  )
62
 
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  # Test the model
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+ test_names = ["kiara", "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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+
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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 Me
81
  If you find this project helpful and would like to support my work, please consider donating using crypto.
82
+ [![Support Me](https://img.shields.io/badge/Support-Us-brightgreen)](https://nowpayments.io/donation/Vishodi)
 
 
 
 
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  ## License
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+ This project is licensed under the MIT License. See the LICENSE file for details.