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  # hkunlp/instructor-base
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  We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks!
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- The model is easy to use with `sentence-transformer` library.
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  ## Quick start
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  <hr />
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  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`; Input:
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  * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
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  * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
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- * `task_objective` is optional, and it specifies the objective of emebdding, e.g., retrieve a document, classify the sentence, etc.
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  ## Calculate Sentence similarities
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  You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
 
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  # hkunlp/instructor-base
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  We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks!
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+ The model is easy to use with the `sentence-transformer` library.
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  ## Quick start
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  <hr />
 
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  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`; Input:
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  * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
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  * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
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+ * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
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  ## Calculate Sentence similarities
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  You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.