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Remove the <NEED MORE INFO> tag

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Nirant Kasliwal
2024-03-04 12:24:25 +05:30
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qdrant-landing/content/articles/binary-quantization-openai.md
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@@ -102,7 +102,7 @@ Here are some key observations, which analyzes the impact of rescoring (`True` o
- 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), <NEED MORE INFO>
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. This suggests a diminishing return on accuracy improvement with higher oversampling in lower dimension spaces.
3. **Influence of Search Limit**: