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@@ -9,16 +9,11 @@ base_model:
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  pipeline_tag: text-classification
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  ---
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- # Name Validation AI
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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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- Name Validation AI is an intelligent system that classifies first names as **real** or **fake**. This project demonstrates two primary approaches:
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-
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- - **Reinforcement Learning Approach:** A custom Gym environment coupled with a PPO agent.
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- - **Transformer-based Approach:** Fine-tuning a transformer model (using Hugging Face Transformers) for binary classification with the final model saved in the `.safetensors` format.
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-
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- Both models are equipped with detailed testing (including confusion matrix visualization) and API deployment capabilities.
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  ---
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@@ -63,25 +58,38 @@ The goal of this project is to determine if a given first name is "real" (from a
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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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- > **Note:** If you're using Google Colab, you can run each provided code block directly.
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-
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- 3. **Dependencies:**
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-
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- - `transformers`
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- - `datasets`
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- - `safetensors`
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- - `stable-baselines3`
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- - `gym`
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- - `flask`
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- - `scikit-learn`
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- - `seaborn`
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- - `huggingface_hub`
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-
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  ---
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  ## Usage
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  ### Training
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  - **Reinforcement Learning Model:**
 
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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-Me-brightgreen)](https://www.example.com/donate?crypto=YOUR_CRYPTO_ID)
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+ A simple 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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  ```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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+ ```bash
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+ from transformers import pipeline
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+
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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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+
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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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+
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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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  ### Training
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  - **Reinforcement Learning Model:**