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"articles/faq-question-answering"
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[
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"Similarity option"
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[
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"Similarity option"
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[
"Q&A with Similarity Learning - Qdrant",
"Similarity option"
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[
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"articles",
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[
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"articles",
"articles/faq-question-answering"
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[
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"Similarity option"
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] | p | Similarity learning approach seems a lot simpler than classification in this case, and if you have some | [
"articles",
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[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8) | [
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"Q&A with Similarity Learning - Qdrant",
"Let’s build"
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"Q&A with Similarity Learning - Qdrant",
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"articles",
"articles/faq-question-answering"
] | 4,186 |
[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | 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",
"articles/faq-question-answering"
] | 4,187 |
[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10) | [
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[
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"Let’s build"
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[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
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[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
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] | p | However, it is better to produce embeddings with a model trained for semantic similarity tasks. | [
"articles",
"articles/faq-question-answering"
] | 4,191 |
[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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] | p | For instance, we can find such models at sentence-transformers. | [
"articles",
"articles/faq-question-answering"
] | 4,192 |
[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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] | p | Authors claim that all-mpnet-base-v2 provides the best quality, but let’s pick all-MiniLM-L6-v2 for our tutorial | [
"articles",
"articles/faq-question-answering"
] | 4,193 |
[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | 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",
"articles/faq-question-answering"
] | 4,194 |
[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | 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 | Having all this, we can test our approach. | [
"articles",
"articles/faq-question-answering"
] | 4,195 |
[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12) | [
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[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | 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 | for this experiment, let’s just launch it now. | [
"articles",
"articles/faq-question-answering"
] | 4,202 |
[
"Q&A with Similarity Learning - Qdrant",
"Let’s build"
] | /articles/faq-question-answering/ | 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 | That’s already quite decent quality, but maybe we can do better? | [
"articles",
"articles/faq-question-answering"
] | 4,203 |
[
"Q&A with Similarity Learning - Qdrant"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(4) | [
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"articles",
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[
"Q&A with Similarity Learning - Qdrant",
"Improving results with fine-tuning"
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"articles",
"articles/faq-question-answering"
] | 4,213 |
[
"Q&A with Similarity Learning - Qdrant",
"Improving results with fine-tuning"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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"articles",
"articles/faq-question-answering"
] | 4,214 |
[
"Q&A with Similarity Learning - Qdrant",
"Improving results with fine-tuning"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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"articles",
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[
"Q&A with Similarity Learning - Qdrant",
"Improving results with fine-tuning"
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"articles",
"articles/faq-question-answering"
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[
"Q&A with Similarity Learning - Qdrant",
"Improving results with fine-tuning"
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"articles",
"articles/faq-question-answering"
] | 4,220 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18) | [
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[
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] | p | All these features make Pytorch Lightning a perfect training backend for Quaterion. | [
"articles",
"articles/faq-question-answering"
] | 4,225 |
[
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"Configure training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20) | [
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"articles",
"articles/faq-question-answering"
] | 4,226 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20) | [
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"articles",
"articles/faq-question-answering"
] | 4,227 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20) | [
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[
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[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
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"articles",
"articles/faq-question-answering"
] | 4,233 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22) | [
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[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
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"articles",
"articles/faq-question-answering"
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[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
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"articles",
"articles/faq-question-answering"
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[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | 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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"articles",
"articles/faq-question-answering"
] | 4,240 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | 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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] | li | In this case it is possible to use PytorchMetricLearningWrapper | [
"articles",
"articles/faq-question-answering"
] | 4,244 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | 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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"articles",
"articles/faq-question-answering"
] | 4,245 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | 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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"articles",
"articles/faq-question-answering"
] | 4,246 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | 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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] | p | At our example we use MultipleNegativesRankingLoss. | [
"articles",
"articles/faq-question-answering"
] | 4,250 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23) | [
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] | p | This loss is especially good for training retrieval tasks. | [
"articles",
"articles/faq-question-answering"
] | 4,251 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23) | [
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] | p | It assumes that we pass only positive pairs (similar objects) and considers all other objects as negative examples. | [
"articles",
"articles/faq-question-answering"
] | 4,252 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24) | [
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[
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"Configure training"
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[
"Q&A with Similarity Learning - Qdrant",
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"articles/faq-question-answering"
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[
"Q&A with Similarity Learning - Qdrant",
"Configure training"
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"articles",
"articles/faq-question-answering"
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[
"Q&A with Similarity Learning - Qdrant"
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] | p | The encoder task is to convert objects into embeddings. | [
"articles",
"articles/faq-question-answering"
] | 4,258 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(26) | [
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] | p | They usually take advantage of some pre-trained models, in our case all-MiniLM-L6-v2 from sentence-transformers. | [
"articles",
"articles/faq-question-answering"
] | 4,259 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(26) | [
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] | p | In order to use it in Quaterion, we need to create a wrapper inherited from the Encoder class. | [
"articles",
"articles/faq-question-answering"
] | 4,260 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27) | [
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] | p | Let’s create our encoder in encoder.py | [
"articles",
"articles/faq-question-answering"
] | 4,261 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28) | [
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] | p | As you can notice, there are more methods implemented, then we’ve already discussed. | [
"articles",
"articles/faq-question-answering"
] | 4,262 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28) | [
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] | li | In __init__ we register our pre-trained layers, similar as you do in torch.nn.Module descendant. | [
"articles",
"articles/faq-question-answering"
] | 4,265 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(3) > li:nth-of-type(1) > p | [
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"articles/faq-question-answering"
] | 4,266 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
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[
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"Configure Encoder"
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"articles",
"articles/faq-question-answering"
] | 4,268 |
[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(3) > li:nth-of-type(2) > p | [
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"articles",
"articles/faq-question-answering"
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[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
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[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
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] | li | embedding_size is a size of encoder’s output, it is required for proper head configuration. | [
"articles",
"articles/faq-question-answering"
] | 4,271 |
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