jupyter-notebook-chatcompletion
hyde
jupyter-notebook-chatcompletion | hyde | |
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2 | 2 | |
6 | 362 | |
- | 10.5% | |
7.9 | 10.0 | |
29 days ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
jupyter-notebook-chatcompletion
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Jupyter Notebook ChatCompletion = Notebooks + ChatGPT
You can also generate more code based on your project files - which I also did to generate more commands for the extension.
Cell outputs and problems detected by VSCode can be added to the prompt. You can, for example, feed code into the prompt - which I did to generate the first version of the Readme.
hyde
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Show HN: Hacker Search – A semantic search engine for Hacker News
HyDE apparently means “Hypothetical Document Embeddings”, which seems to be a kind of generative query expansion/pre-processing
https://arxiv.org/abs/2212.10496
https://github.com/texttron/hyde
From the abstract:
Given a query, HyDE first zero-shot instructs an instruction-following language model (e.g. InstructGPT) to generate a hypothetical document. The document captures relevance patterns but is unreal and may contain false details. Then, an unsupervised contrastively learned encoder~(e.g. Contriever) encodes the document into an embedding vector. This vector identifies a neighborhood in the corpus embedding space, where similar real documents are retrieved based on vector similarity. This second step ground the generated document to the actual corpus, with the encoder's dense bottleneck filtering out the incorrect details.
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Meet HyDE: An Effective Fully Zero-Shot Dense Retrieval Systems That Require No Relevance Supervision, Works Out-of-Box, And Generalize Across Tasks
Quick Read: https://www.marktechpost.com/2023/01/23/meet-hyde-an-effective-fully-zero-shot-dense-retrieval-systems-that-require-no-relevance-supervision-works-out-of-box-and-generalize-across-tasks/ Paper: https://arxiv.org/pdf/2212.10496.pdf Github: https://github.com/texttron/hyde
What are some alternatives?
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jupytemplate - Templates for jupyter notebooks
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
retrolab - JupyterLab distribution with a retro look and feel 🌅
llm-search - Querying local documents, powered by LLM
beakerx - Beaker Extensions for Jupyter Notebook