Genderize

🚀 Gender Classification by Name

This model classifies gender based on the input name, leveraging a pre - trained BERT model and fine - tuning on a name - gender dataset.

🚀 Quick Start

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the model and tokenizer from the Hub
model_name = "imranali291/genderize"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example inference function
def predict_gender(name):
    inputs = tokenizer(name, return_tensors="pt", padding=True, truncation=True, max_length=32)
    outputs = model(**inputs)
    predicted_label = outputs.logits.argmax(dim=-1).item()
    return label_encoder.inverse_transform([predicted_label])[0]

print(predict_gender("Alex"))  # Example output: 'M'
print(predict_gender("Maria"))  # Example output: 'F'

✨ Features

• Classify the gender of a given name.
• Enhance applications that require gender identification based on names, such as personalized marketing and user profiling.

📚 Documentation

Model Details

Description

This model classifies gender based on the input name. It uses a pre-trained BERT model as the base and has been fine-tuned on a dataset of names and their associated genders.

Training Details

Evaluation

Uses

• Direct Use: Classifying the gender of a given name.
• Downstream Use: Enhancing applications that require gender identification based on names (e.g., personalized marketing, user profiling).
• Out - of - Scope Use: Using the model for purposes other than gender classification without proper validation.

Bias, Risks, and Limitations

• Bias: The model may reflect biases present in the training data. It is important to validate its performance across diverse datasets.
• Risks: Misclassification can occur, especially for names that are unisex or less common.
• Limitations: The model's accuracy may vary depending on the cultural and linguistic context of the names.

Recommendations

⚠️ Important Note

Users should be aware of the potential biases and limitations of the model.

💡 Usage Tip

Further validation is recommended for specific use cases and datasets.

📄 License

This project is licensed under the MIT license.