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! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
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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 (MTEB leaderboard)! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! * 12/28: We released a new checkpoint trained with hard negatives, which gives better performance. * 12/21: We released our paper, code, checkpoint and project page! Check them out! Then you can use the model like this to calculate domain-specific and task-aware embeddings:
翻訳待ち・原文を表示
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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! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
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入力 テキスト→ 出力 埋め込み
