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[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
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"articles",
"articles/faq-question-answering"
] | 4,275 |
[
"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(4) | [
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[
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[
"Q&A with Similarity Learning - Qdrant",
"Configure Encoder"
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] | p | Here you should return a method which prepares a batch of raw | [
"articles",
"articles/faq-question-answering"
] | 4,278 |
[
"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(4) > p | [
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] | p | data into the input, suitable for the encoder. | [
"articles",
"articles/faq-question-answering"
] | 4,279 |
[
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] | p | The remaining methods are considered self-describing. | [
"articles",
"articles/faq-question-answering"
] | 4,281 |
[
"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(30) | [
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] | p | As our encoder is ready, we now are able to fill configure_encoders. | [
"articles",
"articles/faq-question-answering"
] | 4,282 |
[
"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(30) | [
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] | p | Just insert the following code into model.py: | [
"articles",
"articles/faq-question-answering"
] | 4,283 |
[
"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 > h3:nth-of-type(3) | [
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[
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"Data preparation"
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"articles",
"articles/faq-question-answering"
] | 4,286 |
[
"Q&A with Similarity Learning - Qdrant",
"Data preparation"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(32) | [
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"articles",
"articles/faq-question-answering"
] | 4,287 |
[
"Q&A with Similarity Learning - Qdrant",
"Data preparation"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(33) | [
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[
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"Data preparation"
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[
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"articles",
"articles/faq-question-answering"
] | 4,294 |
[
"Q&A with Similarity Learning - Qdrant",
"Data preparation"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(37) | [
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] | p | SimilarityPairSample could be used to represent pairs. | [
"articles",
"articles/faq-question-answering"
] | 4,295 |
[
"Q&A with Similarity Learning - Qdrant",
"Data preparation"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(37) | [
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[
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[
"Q&A with Similarity Learning - Qdrant",
"Data preparation"
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"articles/faq-question-answering"
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[
"Q&A with Similarity Learning - Qdrant",
"Data preparation"
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[
"Q&A with Similarity Learning - Qdrant",
"Data preparation"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(41) | [
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"articles",
"articles/faq-question-answering"
] | 4,306 |
[
"Q&A with Similarity Learning - Qdrant",
"Data preparation"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(42) | [
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[
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] | p | Quaterion has some popular retrieval metrics implemented - such as precision @ k or mean reciprocal rank. | [
"articles",
"articles/faq-question-answering"
] | 4,312 |
[
"Q&A with Similarity Learning - Qdrant",
"Evaluation Metric"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(44) | [
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"articles",
"articles/faq-question-answering"
] | 4,313 |
[
"Q&A with Similarity Learning - Qdrant",
"Evaluation Metric"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(44) | [
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"articles",
"articles/faq-question-answering"
] | 4,314 |
[
"Q&A with Similarity Learning - Qdrant",
"Evaluation Metric"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(44) | [
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] | p | You will probably need to inherit from PairMetric or GroupMetric to implement a new one. | [
"articles",
"articles/faq-question-answering"
] | 4,315 |
[
"Q&A with Similarity Learning - Qdrant",
"Evaluation Metric"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(45) | [
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] | p | In configure_metrics we need to return a list of AttachedMetric. | [
"articles",
"articles/faq-question-answering"
] | 4,316 |
[
"Q&A with Similarity Learning - Qdrant",
"Evaluation Metric"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(45) | [
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"articles",
"articles/faq-question-answering"
] | 4,317 |
[
"Q&A with Similarity Learning - Qdrant",
"Evaluation Metric"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(45) | [
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[
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"articles",
"articles/faq-question-answering"
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[
"Q&A with Similarity Learning - Qdrant",
"Evaluation Metric"
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[
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"Evaluation Metric"
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] | p | Let’s add mentioned metrics for our FAQModel. | [
"articles",
"articles/faq-question-answering"
] | 4,321 |
[
"Q&A with Similarity Learning - Qdrant",
"Evaluation Metric"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(46) | [
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] | p | Add this code to model.py: | [
"articles",
"articles/faq-question-answering"
] | 4,322 |
[
"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 > h3:nth-of-type(5) | [
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[
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[
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"Fast training with Cache"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(48) | [
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"articles",
"articles/faq-question-answering"
] | 4,331 |
[
"Q&A with Similarity Learning - Qdrant",
"Fast training with Cache"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(49) | [
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"articles",
"articles/faq-question-answering"
] | 4,332 |
[
"Q&A with Similarity Learning - Qdrant",
"Fast training with Cache"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(50) | [
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[
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] | p | Now we need to combine all our code together in train.py and launch a training process. | [
"articles",
"articles/faq-question-answering"
] | 4,336 |
[
"Q&A with Similarity Learning - Qdrant",
"Training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(53) | [
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] | p | Here are a couple of unseen classes, PairsSimilarityDataLoader, which is a native dataloader for | [
"articles",
"articles/faq-question-answering"
] | 4,337 |
[
"Q&A with Similarity Learning - Qdrant",
"Training"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(53) | [
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"articles",
"articles/faq-question-answering"
] | 4,338 |
[
"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 > h3:nth-of-type(7) | [
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"articles",
"articles/faq-question-answering"
] | 4,341 |
[
"Q&A with Similarity Learning - Qdrant",
"Dataset-wise evaluation"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(54) | [
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[
"Q&A with Similarity Learning - Qdrant",
"Dataset-wise evaluation"
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[
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[
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[
"Q&A with Similarity Learning - Qdrant",
"Dataset-wise evaluation"
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[
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"Dataset-wise evaluation"
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[
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"Dataset-wise evaluation"
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[
"Q&A with Similarity Learning - Qdrant",
"Dataset-wise evaluation"
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] | p | If sample size is not specified, evaluation is performed on all embeddings. | [
"articles",
"articles/faq-question-answering"
] | 4,352 |
[
"Q&A with Similarity Learning - Qdrant",
"Dataset-wise evaluation"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(57) | [
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[
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] | p | At this point we can train our model, I do it via python3 -m faq.train. | [
"articles",
"articles/faq-question-answering"
] | 4,356 |
[
"Q&A with Similarity Learning - Qdrant",
"Train Results"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(59) | [
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"articles/faq-question-answering"
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[
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"Train Results"
] | /articles/faq-question-answering/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(60) | [
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[
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"Model serving"
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[
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"Model serving"
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] | p | The very last row of train.py - faq_model.save_servable(...) saves encoders and the model in a fashion that eliminates all Quaterion dependencies and stores only the most necessary data to run a model in production. | [
"articles",
"articles/faq-question-answering"
] | 4,371 |
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