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Chrisneverdie
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OnlySports_Classifier

Text Classification
Transformers
Safetensors
English
bert
sports
Inference Endpoints
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OnlySports_Classifier
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---
license: cc-by-sa-4.0
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- sports
datasets:
- Chrisneverdie/OnlySports_Dataset
base_model: Snowflake/snowflake-arctic-embed-xs
---


# Sports Text Classifier

## Overview

This Sports Text Classifier is a crucial component of the OnlySports Dataset creation pipeline. It's designed to accurately identify and extract sports-related documents from a large corpus of web content.

## Model Architecture

- Base model: [Snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)
- Additional layer: Binary classification layer
- Training: 10 epochs with a learning rate of 3e-4

## Performance

The classifier achieves exceptional accuracy in distinguishing between sports and non-sports documents:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/656590bd40440ddcc051ade7/hK_a183i2_H5AfUF6ZXd6.png)

## Training Data

The classifier was trained on a balanced dataset of sports and non-sports content:

- 64k samples from seven prestigious sports websites
- 36k non-sports text documents classified using GPT-3.5

## Usage

This classifier is primarily used in the creation of the OnlySports Dataset, presented in this [paper](https://arxiv.org/abs/2409.00286). It can be applied to filter large text corpora for sports-related content with high accuracy.

## Integration

The classifier is integrated into a MapReduce architecture for efficient processing of large-scale datasets. It's used in conjunction with URL keyword filtering to create a comprehensive sports text dataset.

## Related Projects

This classifier is part of the larger OnlySports collection, which includes:

- [OnlySports Dataset](https://huggingface.co/collections/Chrisneverdie/onlysports-66b3e5cf595eb81220cc27a6)
- [OnlySportsLM](https://huggingface.co/Chrisneverdie/OnlySportsLM_196M)

For more information, check our [paper](https://arxiv.org/abs/2409.00286).