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[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/entity-matching-qdrant"
] | 13,973 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(20) | [
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"blog",
"blog/entity-matching-qdrant"
] | 13,974 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(20) | [
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] | p | I’m going to go straight into Qdrant questions and ask you to elaborate on how the unique modification of Qdrant of the HNSW algorithm benefits your solution. | [
"blog",
"blog/entity-matching-qdrant"
] | 13,975 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(20) | [
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"blog",
"blog/entity-matching-qdrant"
] | 13,976 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(20) | [
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[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And how also to add another layer to this question, this ridiculously long question that I’m starting to get myself into, how do you handle geo filtering based on longitude and latitude? | [
"blog",
"blog/entity-matching-qdrant"
] | 13,978 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(20) | [
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"blog",
"blog/entity-matching-qdrant"
] | 13,979 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(20) | [
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"blog",
"blog/entity-matching-qdrant"
] | 13,980 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | So to begin with, I will give you a little backstory. | [
"blog",
"blog/entity-matching-qdrant"
] | 13,983 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(21) | [
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"blog",
"blog/entity-matching-qdrant"
] | 13,984 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(21) | [
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"blog",
"blog/entity-matching-qdrant"
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[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
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"blog",
"blog/entity-matching-qdrant"
] | 13,986 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(21) | [
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"blog",
"blog/entity-matching-qdrant"
] | 13,987 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(21) | [
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"blog",
"blog/entity-matching-qdrant"
] | 13,988 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/entity-matching-qdrant"
] | 13,989 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(22) | [
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[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And also, it was open source and really easy to host and use. | [
"blog",
"blog/entity-matching-qdrant"
] | 13,991 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(22) | [
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"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(23) | [
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] | p | Did you guys do your own benchmarks too? | [
"blog",
"blog/entity-matching-qdrant"
] | 13,994 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(23) | [
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"blog",
"blog/entity-matching-qdrant"
] | 13,997 |
[
"Building a High-Performance Entity Matching Solution with Qdrant - Rishabh Bhardwaj | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/entity-matching-qdrant/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(24) | [
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[
"Triplet Loss - Advanced Intro - Qdrant",
"What is Triplet Loss?"
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"What is Triplet Loss?"
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[
"Triplet Loss - Advanced Intro - Qdrant",
"What is Triplet Loss?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5) | [
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[
"Triplet Loss - Advanced Intro - Qdrant",
"What is Triplet Loss?"
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[
"Triplet Loss - Advanced Intro - Qdrant",
"What is Triplet Loss?"
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[
"Triplet Loss - Advanced Intro - Qdrant",
"What is Triplet Loss?"
] | /articles/triplet-loss/ | 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 | Animation that shows how Contrastive Loss moves points in the course of training. | [
"articles",
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[
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"What is Triplet Loss?"
] | /articles/triplet-loss/ | 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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[
"Triplet Loss - Advanced Intro - Qdrant",
"What is Triplet Loss?"
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] | p | From mathematical interpretations of the two-loss functions, it is clear that Triplet Loss is theoretically stronger, | [
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[
"Triplet Loss - Advanced Intro - Qdrant",
"What is Triplet Loss?"
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] | p | but Triplet Loss has additional tricks that help it work better. | [
"articles",
"articles/triplet-loss"
] | 6,015 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"What is Triplet Loss?"
] | /articles/triplet-loss/ | 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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"Why triplet mining matters?"
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"articles",
"articles/triplet-loss"
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[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(3) > li:nth-of-type(3) | [
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] | p | and then feed batches of such triplets to the model throughout that epoch. | [
"articles",
"articles/triplet-loss"
] | 6,023 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9) | [
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"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
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"Why triplet mining matters?"
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[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(4) > li:nth-of-type(2) | [
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[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(4) > li:nth-of-type(3) | [
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] | li | Even if we form “useful” triplets at the beginning of each epoch with one of the methods that I will be implementing in this series, | [
"articles",
"articles/triplet-loss"
] | 6,028 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(4) > li:nth-of-type(3) | [
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] | 6,029 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10) | [
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] | p | Instead, we can get a batch of $n$ samples and their associated labels, | [
"articles",
"articles/triplet-loss"
] | 6,030 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10) | [
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[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10) | [
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"articles",
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] | 6,032 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10) | [
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[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10) | [
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] | p | we will have a loss value calculated from much more triplets than the offline strategy. | [
"articles",
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] | 6,036 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(5) > li:nth-of-type(3) | [
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[
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"Why triplet mining matters?"
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] | p | but it can be efficiently implemented with tricks such as distance matrix, masking, and broadcasting. | [
"articles",
"articles/triplet-loss"
] | 6,042 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Why triplet mining matters?"
] | /articles/triplet-loss/ | 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 | The rest of this series will focus on the implementation of these tricks. | [
"articles",
"articles/triplet-loss"
] | 6,043 |
[
"Triplet Loss - Advanced Intro - Qdrant"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(3) | [
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[
"Triplet Loss - Advanced Intro - Qdrant",
"Distance matrix"
] | /articles/triplet-loss/ | 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/triplet-loss"
] | 6,048 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Distance matrix"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13) | [
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] | p | explained by Samuel Albanie. | [
"articles",
"articles/triplet-loss"
] | 6,049 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Distance matrix"
] | /articles/triplet-loss/ | 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",
"articles/triplet-loss"
] | 6,050 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Distance matrix"
] | /articles/triplet-loss/ | 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",
"articles/triplet-loss"
] | 6,051 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Distance matrix"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(6) > li:nth-of-type(1) | [
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] | li | Calculate the dot product of two collections of vectors, e.g., embeddings in our case. | [
"articles",
"articles/triplet-loss"
] | 6,052 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Distance matrix"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(6) > li:nth-of-type(2) | [
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] | li | Extract the diagonal from this matrix that holds the squared Euclidean norm of each embedding. | [
"articles",
"articles/triplet-loss"
] | 6,053 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Distance matrix"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(6) > li:nth-of-type(3) | [
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] | li | Calculate the squared Euclidean distance matrix based on the following equation: $||a - b||^2 = ||a||^2 - 2 ⟨a, b⟩ + ||b||^2$ | [
"articles",
"articles/triplet-loss"
] | 6,054 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Distance matrix"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(6) > li:nth-of-type(4) | [
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] | li | Get the square root of this matrix for non-squared distances. | [
"articles",
"articles/triplet-loss"
] | 6,055 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Distance matrix"
] | /articles/triplet-loss/ | 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 | We will implement it in PyTorch, so let’s start with imports. | [
"articles",
"articles/triplet-loss"
] | 6,056 |
[
"Triplet Loss - Advanced Intro - Qdrant"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(4) | [
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] | p | Now that we can compute a distance matrix for all possible pairs of embeddings in a batch, | [
"articles",
"articles/triplet-loss"
] | 6,058 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Invalid triplet masking"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | we can apply broadcasting to enumerate distance differences for all possible triplets and represent them in a tensor of shape (batch_size, batch_size, batch_size). | [
"articles",
"articles/triplet-loss"
] | 6,059 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Invalid triplet masking"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | However, only a subset of these $n^3$ triplets are actually valid as I mentioned earlier, | [
"articles",
"articles/triplet-loss"
] | 6,060 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Invalid triplet masking"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | and we need a corresponding mask to compute the loss value correctly. | [
"articles",
"articles/triplet-loss"
] | 6,061 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Invalid triplet masking"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | We will implement such a helper function in three steps: | [
"articles",
"articles/triplet-loss"
] | 6,062 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Invalid triplet masking"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(7) > li:nth-of-type(1) | [
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[
"Triplet Loss - Advanced Intro - Qdrant",
"Invalid triplet masking"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(7) > li:nth-of-type(2) | [
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[
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"Invalid triplet masking"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(7) > li:nth-of-type(3) | [
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] | p | Triplet Loss involves several strategies to form or select triplets, and the simplest one is | [
"articles",
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] | 6,068 |
[
"Triplet Loss - Advanced Intro - Qdrant",
"Batch-all strategy for online triplet mining"
] | /articles/triplet-loss/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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"Triplet Loss - Advanced Intro - Qdrant",
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] | li | Get a distance matrix of all possible pairs that can be formed from embeddings in a batch. | [
"articles",
"articles/triplet-loss"
] | 6,071 |
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