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title: CrediNet |
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sdk: static |
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short_description: Network-based Credibility Modelling |
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<div align="center"> |
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<h1>CrediNet</h1> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/Mz_vqAW1rlfEDkx2Mv4fd.png" alt="credinet" width="150"/> |
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**CrediNet** is set of tools that use graph machine learning and computational methods for credibility modelling on the web. |
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We develop billion-scale data webgraphs and use them to assess credibility levels of websites, which can be used downstream to augment Retrieval-Augmented Generation robustness and fact-checking. |
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This involves large-scale web scraping and text processing, and developing model architectures to interpret the different types of signals we can find on the web (including structural, temporal and linguistic cues). |
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<p> |
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<a href="https://github.com/credi-net">See also: our Github codebases</a> |
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# Projects |
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- **CrediBench**: benchmark of billion-scale temporal webgraphs on a monthly granularity, sourced from Common Crawl. For the corresponding graph construction pipeline refer to [CrediGraph - GitHub](https://github.com/credi-net/CrediGraph). |
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- **CrediPred**: inferred scores from our developed model (for more details on the model architecture, refer to [CrediPred - GitHub](https://github.com/credi-net/CrediPred)). |
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- **CrediText**: text embeddings extracted from scraped web content. Find the corresponding scraping and embedding pipelines on [CrediText - GitHub](https://github.com/credi-net/CrediText). |
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- **CrediNet**: API set-up to query CrediPred scores easily on the client side (for more details on the API set up and examples usages, refer to [CrediNet - GitHub](https://github.com/credi-net/CrediNet)). |
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<div align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/681e3663829118a837bbaeb3/gAED3hY_AVkER7mLblJ4u.png" alt="suite" width="600"/> |
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</div> |
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