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README.md
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# Graphlet-AI/eridu
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Deep fuzzy matching people and company names for multilingual entity resolution using representation learning... that incorporates a deep understanding of people and company names and works _much better_ than string distance methods.
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) for person and company name matching using the [Open Sanctions matcher training data](https://www.opensanctions.org/docs/pairs/). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used as part of a deep, fuzzy entity resolution process.
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# Graphlet-AI/eridu
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NOTE: this model is a work in progress. It is not yet ready for production use.
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Deep fuzzy matching people and company names for multilingual entity resolution using representation learning... that incorporates a deep understanding of people and company names and works _much better_ than string distance methods.
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) for person and company name matching using the [Open Sanctions matcher training data](https://www.opensanctions.org/docs/pairs/). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used as part of a deep, fuzzy entity resolution process.
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