TIL about cross encoders and colbert while reading about to introduce joint attention between query and candidates to prepare for my interview.

Cross Encoders:

• process query and document pairs together through a single transformer
• compute relevance scores directly without creating separate embeddings
• highly accurate but computationally expensive for large document collections
• cannot pre-compute document representations, requiring full processing for each query
• use case: re-ranking top-k search results from a first-pass retrieval system

ColBERT:

• uses late interaction architecture with separate encodings for queries and documents
• creates contextualized embeddings for each token rather than a single vector
• performs efficient token-level interactions between query and document representations
• enables both pre-computation of document representations and fast retrieval
• use case: semantic search over millions of documents with better accuracy than bi-encoders

some herbs that made me very heaty today and i will avoid in the future, especially when i'm stressed

1. 淮山 (huái shān) - Chinese Yam
2. 党参 (dǎng shēn) - Codonopsis Root
3. 北祈 (běi qí) - Probably a typo or local name; might mean Astragalus Root
4. 龙眼肉 (lóng yǎn ròu) - Longan Fruit
5. 首乌 (shòu wū) - Fo-Ti Root
6. 当归 (dāng guī) - Angelica Sinensis
7. 熟地 (shú dì) - Prepared Rehmannia Root
8. 黄精 (huáng jīng) - Polygonatum
9. 川芎 (chuān xiōng) - Ligusticum Wallichii
10. 构纪子 (gòu jì zǐ) - Possibly Goji Berries
11. 大枣 (dà zǎo) - Red Dates/Jujube