Gender Prediction Model From Text

🚀 Gender Prediction from Text

This model predicts the likely gender of an anonymous speaker or writer based solely on the content of an English text. It is built upon DeBERTa-v3-large and fine-tuned on a diverse, multilingual, and multi-domain dataset with both formal and informal texts.

🚀 Quick Start

• Space link: 🔗 Try it out on Hugging Face Spaces
• Model repo: 🔗 View on Hugging Face Hub

✨ Features

• Predicts the gender of an anonymous speaker or writer from English text.
• Built on DeBERTa-v3-large and fine - tuned on a diverse dataset.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model_name = "fc63/gender_prediction_model_from_text"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(model_name).eval().to(device)

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128).to(device)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits, dim=1)
    pred = torch.argmax(probs, dim=1).item()
    confidence = round(probs[0][pred].item() * 100, 1)
    gender = "Female" if pred == 0 else "Male"
    return f"{gender} (Confidence: {confidence}%)"

sample_text = "I love writing in my journal every night. It helps me reflect on the day and plan for tomorrow."
print(predict(sample_text))

The output of this sample:

Female (Confidence: 84.1%)

📚 Documentation

Model Information

Model Results

The model named gender_prediction_model_from_text has the following results in text classification:

Datasets Used

All datasets were normalized, translated if necessary, deduplicated, and balanced via random undersampling to ensure equal representation of both genders.

Preprocessing & Training

• Normalization: Cleaned quotes, dashes, placeholders, noise, and HTML/code from all datasets.
• Translation: Used Helsinki-NLP/opus-mt-* models for Polish and Russian data.
• Undersampling: Random undersampling to balance male and female samples.
• Training Strategy:
  • LR Finder used to optimize learning rate (2.66e-6)
  • Fine-tuned using early stopping on both F1 and loss
  • Step-based evaluation every 250 steps
  • Best checkpoint at step 24,750 saved and evaluated
• Second Phase Fine-tuning:
  • Performed on full merged dataset for 2 epochs
  • Used cosine learning rate scheduler and warm-up steps

Performance (on full merged test set)

Future Work & Limitations

The current model has an accuracy and F1 score of 0.69. There is a bias in prediction, where emotional, psychological, and introspective texts are often predicted as female, and more direct and result - oriented writings are often predicted as male. A large, carefully labeled dataset that reflects the opposite pattern is needed.

The datasets were obtained from open - source platforms, which limited the data range. To make further progress, creating and labeling a larger dataset is required, which is time - consuming, labor - intensive, and costly.

Before creating a new dataset, the author plans to try more approaches with the current dataset. If none of them work, building a new dataset will be the next step, which may also be the end of development due to high costs.

🔧 Technical Details

The model is based on the DeBERTa - v3 - large architecture. It uses various pre - processing steps such as normalization, translation, and undersampling during training. The training strategy includes using an LR Finder, early stopping, step - based evaluation, and a cosine learning rate scheduler in the second - phase fine - tuning.

📄 License

This project is licensed under the MIT license.

Author: Furkan Şoban
Project: CENG - 481 Gender Prediction Model

Citations

• "@misc{fc63_gender1_2025,\n title = {Gender Prediction from Text},\n author = {Şoban, Furkan},\n year = {2025},\n howpublished = {\url{https://doi.org/10.5281/zenodo.15619489}},\n note = {DeBERTa-v3-large model fine-tuned on multi-domain gender-labeled texts}\n}"