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Gender Prediction Model From Text

Developed by fc63
This model is built on DeBERTa-v3-large and can predict the gender of anonymous speakers or authors based on the content of English texts.
Downloads 106
Release Time : 6/7/2025

Model Overview

This is a text classification model specifically designed to predict the gender of the author based on the content of English texts. The model has been fine-tuned on diverse, multilingual, and multi-domain datasets and is suitable for both formal and informal texts.

Model Features

Multi-domain adaptability
The model performs well on both formal and informal texts and is suitable for various text types.
Training with multilingual data
Although it only supports English prediction, the training data contains multilingual texts (after translation processing).
Balanced dataset
Random undersampling is used to ensure a balanced number of male and female samples and reduce bias.

Model Capabilities

English text gender prediction
Formal text analysis
Informal text analysis

Use Cases

Social media analysis
Anonymous user gender analysis
Analyze the posting content of anonymous users on social media to predict their gender.
Accuracy is approximately 69%
Market research
Consumer review analysis
Predict the gender distribution of consumers through product reviews.
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