| --- |
| 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 |
|
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| ## Overview |
|
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| 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. |
|
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| ## Model Architecture |
|
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| - 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 |
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| The classifier achieves exceptional accuracy in distinguishing between sports and non-sports documents: |
|
|
|  |
|
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| ## Training Data |
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| The classifier was trained on a balanced dataset of sports and non-sports content: |
|
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| - 64k samples from seven prestigious sports websites |
| - 36k non-sports text documents classified using GPT-3.5 |
|
|
| ## Usage |
|
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| 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. |
|
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| ## Integration |
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| 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. |
|
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| ## Related Projects |
|
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| This classifier is part of the larger OnlySports collection, which includes: |
|
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| - [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). |
