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[
"Enhance OpenAI Embeddings with Qdrant's Binary Quantization - Qdrant",
"Matryoshka Representation Learning"
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"Matryoshka Representation Learning"
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] | p | Here, we show how the accuracy of binary quantization is quite good across different dimensions – for both the models. | [
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] | 135 |
[
"Enhance OpenAI Embeddings with Qdrant's Binary Quantization - Qdrant",
"Parameters: Oversampling, Rescoring, and Search Limits"
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"Parameters: Oversampling, Rescoring, and Search Limits"
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"Parameters: Oversampling, Rescoring, and Search Limits"
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[
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"Parameters: Oversampling, Rescoring, and Search Limits"
] | /articles/binary-quantization-openai/ | 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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] | p | Through this detailed setup, our experiment sought to shed light on the nuanced interplay between Binary Quantization and the high-quality embeddings produced by OpenAI’s models. | [
"articles",
"articles/binary-quantization-openai"
] | 164 |
[
"Enhance OpenAI Embeddings with Qdrant's Binary Quantization - Qdrant",
"Parameters: Oversampling, Rescoring, and Search Limits"
] | /articles/binary-quantization-openai/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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"Results: Binary Quantization’s Impact on OpenAI Embeddings"
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] | li | Model and Dimension Specific Observations:For the text-embedding-3-large model with 3072 dimensions, rescoring boosts the accuracy from an average of about 76-77% without rescoring to 97-99% with rescoring, depending on the search limit and oversampling rate.The accuracy improvement with increased oversampling is more pronounced when rescoring is enabled, indicating a better utilization of the additional binary codes in refining search results.With the text-embedding-3-small model at 512 dimensions, accuracy increases from around 53-55% without rescoring to 71-91% with rescoring, highlighting the significant impact of rescoring, especially at lower dimensions.For higher dimension models (such as text-embedding-3-large with 3072 dimensions), In contrast, for lower dimension models (such as text-embedding-3-small with 512 dimensions), the incremental accuracy gains from increased oversampling levels are less significant, even with rescoring enabled. | [
"articles",
"articles/binary-quantization-openai"
] | 175 |
[
"Enhance OpenAI Embeddings with Qdrant's Binary Quantization - Qdrant",
"Rescoring"
] | /articles/binary-quantization-openai/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(2) | [
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"Rescoring"
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[
"Enhance OpenAI Embeddings with Qdrant's Binary Quantization - Qdrant",
"Dataset Combinations"
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[
"Enhance OpenAI Embeddings with Qdrant's Binary Quantization - Qdrant",
"Dataset Combinations"
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[
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[
"Enhance OpenAI Embeddings with Qdrant's Binary Quantization - Qdrant",
"Dataset Combinations"
] | /articles/binary-quantization-openai/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(1) | [
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Subsets and Splits