keisukegoda3804
keisukegoda3804 t1_jbgrbmx wrote
Reply to comment by jobeta in [D] Text embedding model for financial documents by [deleted]
More accurate, as in it can more accurately capture the semantic meaning of jargon that is typical in financial documents.
keisukegoda3804 t1_j8rpe1w wrote
Check out liquid neural networks: https://news.mit.edu/2022/solving-brain-dynamics-gives-rise-flexible-machine-learning-models-1115
keisukegoda3804 t1_j6z5ppy wrote
Reply to [N] Microsoft integrates GPT 3.5 into Teams by bikeskata
This is devastating to startups in the meeting transcription market. Solutions like Otter and Fireflies cost $15-20 per month and only have a fraction of the featureset of Teams Premium. Really interested to see how this develops.
keisukegoda3804 t1_j5xy9x3 wrote
Reply to comment by Kacper-Lukawski in [D] Efficient retrieval of research information for graduate research by [deleted]
Do you happen to know how fast it is compared to other services that build-in filtering inside their vector search (pinecone, milvus, etc.)?
keisukegoda3804 t1_j5v6c7q wrote
Reply to comment by Kacper-Lukawski in [D] Efficient retrieval of research information for graduate research by [deleted]
How does qdrant compare to other offerings with regards to filtered search?
keisukegoda3804 t1_jbgrgx5 wrote
Reply to comment by ok531441 in [D] Text embedding model for financial documents by [deleted]
It’s a retrieve/rerank model