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[
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"Furniture web-marketplace"
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"articles",
"articles/dataset-quality"
] | 4,073 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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] | p | One can arrange all the items manually and spend a lot of money and time on this. | [
"articles",
"articles/dataset-quality"
] | 4,074 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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] | p | There is also another way: train a classification or similarity model and rely on it. | [
"articles",
"articles/dataset-quality"
] | 4,075 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
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] | p | You can analyse the most uncertain predictions and fix them, but the other errors will still leak to the site. | [
"articles",
"articles/dataset-quality"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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"Furniture web-marketplace"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
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] | p | When you are sure that there are not many objects placed in the wrong category, they can be considered outliers or anomalies. | [
"articles",
"articles/dataset-quality"
] | 4,083 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
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"articles",
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
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[
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"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6) | [
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] | p | E.g. between category title and item images. | [
"articles",
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] | 4,089 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6) | [
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] | p | The hypothesis is, that unsuitable items will be less similar. | [
"articles",
"articles/dataset-quality"
] | 4,090 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7) | [
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] | p | We can’t directly compare text and image data. | [
"articles",
"articles/dataset-quality"
] | 4,091 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7) | [
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[
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[
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"articles",
"articles/dataset-quality"
] | 4,094 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7) | [
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] | p | After embeddings are created, we can measure the distances between them. | [
"articles",
"articles/dataset-quality"
] | 4,095 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8) | [
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] | p | Assume we want to search for something other than a single bed in «Single beds» category. | [
"articles",
"articles/dataset-quality"
] | 4,096 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > figure:nth-of-type(2) > figcaption > p | [
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"articles",
"articles/dataset-quality"
] | 4,097 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9) | [
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] | p | One of the possible pipelines would look like this: | [
"articles",
"articles/dataset-quality"
] | 4,098 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1) | [
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] | li | Take the name of the category as an anchor and calculate the anchor embedding. | [
"articles",
"articles/dataset-quality"
] | 4,099 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(3) | [
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[
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
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"articles",
"articles/dataset-quality"
] | 4,103 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > figure:nth-of-type(3) > figcaption > p | [
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[
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] | p | We can also calculate embeddings for titles instead of images, or even for both of them to find more errors. | [
"articles",
"articles/dataset-quality"
] | 4,105 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > figure:nth-of-type(4) > figcaption > p | [
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"articles/dataset-quality"
] | 4,106 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | 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/dataset-quality"
] | 4,107 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12) | [
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] | p | Stacking several techniques or even the same techniques with different models may provide better coverage. | [
"articles",
"articles/dataset-quality"
] | 4,108 |
[
"Finding errors in datasets with Similarity Search - Qdrant",
"Similarity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12) | [
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] | p | Hint: Caching embeddings for the same models and reusing them among different methods can significantly speed up your lookup. | [
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] | 4,109 |
[
"Finding errors in datasets with Similarity Search - Qdrant"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(2) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
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[
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"Diversity search"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(3) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(2) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Diversity search"
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"Diversity search"
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] | p | If the quality remains insufficient, you can fine-tune the models using a similarity learning approach (e.g. with Quaterion both to provide a better representation of your data and pull apart dissimilar objects in space. | [
"articles",
"articles/dataset-quality"
] | 4,136 |
[
"Finding errors in datasets with Similarity Search - Qdrant"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(2) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Conclusion"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20) | [
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] | p | In this article, we enlightened distance-based methods to find errors in categorized datasets. | [
"articles",
"articles/dataset-quality"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Conclusion"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20) | [
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Conclusion"
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"articles/dataset-quality"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Conclusion"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Conclusion"
] | /articles/dataset-quality/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22) | [
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"Question-answering system with Similarity Learning and Quaterion"
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"articles/faq-question-answering"
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"Q&A with Similarity Learning - Qdrant"
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] | p | Let’s now take a look at the concrete example. | [
"articles",
"articles/faq-question-answering"
] | 4,166 |
[
"Q&A with Similarity Learning - Qdrant",
"Automating customer support"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4) | [
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"articles",
"articles/faq-question-answering"
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[
"Q&A with Similarity Learning - Qdrant",
"Automating customer support"
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[
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] | p | And then, each time you have a new topic in your documentation, you have to re-train the whole pile of classifiers with additionally labeled data. | [
"articles",
"articles/faq-question-answering"
] | 4,171 |
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