Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
t5
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
prompt-retrieval
text-reranking
feature-extraction
English
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Eval Results
tianbaoxiexxx
commited on
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README.md
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# hkunlp/instructor-xl
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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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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
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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-xl
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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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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**.
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