Gender Classification

padmajabfrl

Gender-Classification

The Gender-Classification model is a fine-tuned version of distilbert-base-uncased. It achieves 100% accuracy on the evaluation set, although no specific information about the dataset or training procedure is provided. The model uses Transformers 4.25.1, Pytorch 1.13.0+cu116, Datasets 2.8.0, and Tokenizers 0.13.2.

Use cases

This Gender-Classification model can be used in a variety of applications where determining the gender of a text input is necessary. It can be implemented in social media platforms to automatically categorize users based on their gender, helping to target specific advertisements or content. Additionally, this model can be used in customer support systems to analyze customer feedback and identify the gender of the customers, allowing for personalized responses and service. It can also be applied in market research to analyze and understand gender-specific trends and preferences. Possible products or practical uses of this model could include gender-based recommendation systems, targeted advertising platforms, or gender-specific content filtering.

text-classification

Pricing

Cost per run
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Avg run time
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Creator Models

ModelCostRuns
Ethnicity Classification$?16
Religion Classification Custom Model$?12
Demo$?11
Distilbert Base Uncased Finetuned_gender_classification$?19
Religion Classification$?48

Similar Models

Try it!

You can use this area to play around with demo applications that incorporate the Gender Classification model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.

Currently, there are no demos available for this model.

Overview

Summary of this model and related resources.

PropertyValue
Creatorpadmajabfrl
Model NameGender Classification
Description

Gender-Classification This model is a fine-tuned version of...

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Tagstext-classification
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Runs86,249
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Cost per Run$-
Prediction Hardware-
Average Completion Time-