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] | p | The following code snippet shows how to generate embeddings using the InstructorXL model: | [
"documentation",
"documentation/datasets"
] | 6,573 |
[
"Practice Datasets - Qdrant",
"Arxiv.org titles"
] | /documentation/datasets/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(11) | [
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] | p | The snapshot of the dataset might be downloaded here. | [
"documentation",
"documentation/datasets"
] | 6,574 |
[
"Practice Datasets - Qdrant"
] | /documentation/datasets/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > h4:nth-of-type(1) | [
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[
"Practice Datasets - Qdrant",
"Importing the dataset"
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] | p | code snippet shows how to create a new collection and fill it with the snapshot data: | [
"documentation",
"documentation/datasets"
] | 6,580 |
[
"Practice Datasets - Qdrant"
] | /documentation/datasets/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > h3:nth-of-type(2) | [
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[
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] | p | The following code snippet shows how to generate embeddings using the InstructorXL model: | [
"documentation",
"documentation/datasets"
] | 6,588 |
[
"Practice Datasets - Qdrant",
"Arxiv.org abstracts"
] | /documentation/datasets/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(16) | [
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[
"Practice Datasets - Qdrant",
"Importing the dataset"
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"documentation",
"documentation/datasets"
] | 6,604 |
[
"Practice Datasets - Qdrant",
"Wolt food"
] | /documentation/datasets/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(19) | [
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[
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] | p | code snippet shows how to create a new collection and fill it with the snapshot data: | [
"documentation",
"documentation/datasets"
] | 6,612 |
[
"Community links - Qdrant"
] | /documentation/community-links/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > h1 | [
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[
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[
"Neural Search Tutorial - Qdrant",
"How to build a neural search service with BERT + Qdrant + FastAPI"
] | /articles/neural-search-tutorial/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(4) | [
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[
"Neural Search Tutorial - Qdrant"
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"articles/neural-search-tutorial"
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[
"Neural Search Tutorial - Qdrant",
"What is neural search?"
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] | p | The encoder must be trained so that similar objects, such as texts with the same meaning or alike pictures get a close vector representation. | [
"articles",
"articles/neural-search-tutorial"
] | 6,638 |
[
"Neural Search Tutorial - Qdrant",
"What is neural search?"
] | /articles/neural-search-tutorial/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6) | [
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[
"Neural Search Tutorial - Qdrant",
"What is neural search?"
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"articles",
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[
"Neural Search Tutorial - Qdrant",
"What is neural search?"
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[
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"What is neural search?"
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[
"Neural Search Tutorial - Qdrant",
"Which model could be used?"
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[
"Neural Search Tutorial - Qdrant",
"What tasks is neural search good for?"
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[
"Neural Search Tutorial - Qdrant",
"What tasks is neural search good for?"
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[
"Neural Search Tutorial - Qdrant",
"What tasks is neural search good for?"
] | /articles/neural-search-tutorial/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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"articles/neural-search-tutorial"
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[
"Neural Search Tutorial - Qdrant"
] | /articles/neural-search-tutorial/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(5) | [
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"articles",
"articles/neural-search-tutorial"
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[
"Neural Search Tutorial - Qdrant",
"Let’s build our own"
] | /articles/neural-search-tutorial/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12) | [
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"articles",
"articles/neural-search-tutorial"
] | 6,663 |
[
"Neural Search Tutorial - Qdrant",
"Let’s build our own"
] | /articles/neural-search-tutorial/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13) | [
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"articles/neural-search-tutorial"
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[
"Neural Search Tutorial - Qdrant",
"Let’s build our own"
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"articles",
"articles/neural-search-tutorial"
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[
"Neural Search Tutorial - Qdrant",
"Prepare data for neural search"
] | /articles/neural-search-tutorial/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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] | p | As mentioned above, for the task of text search there is a whole set of pre-trained models specifically tuned for semantic similarity. | [
"articles",
"articles/neural-search-tutorial"
] | 6,671 |
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