Showing 4 open source projects for "math with python learn"

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  • 1
    Learn Prompting

    Learn Prompting

    This website is a free, open-source guide on prompt engineering

    This website is a free, open-source guide on prompt engineering. Contributions are welcome! Harsh criticism is welcome too. We launched the first ever prompt hacking competition designed to enhance AI safety and education by challenging participants to outsmart large language models from May 5th to June 3rd! The competition featured 10 increasingly difficult levels of prompt hacking defenses and the chance to win over $35,000 in prizes. Coding is a great skill to learn alongside prompt...
    Downloads: 7 This Week
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  • 2
    Scikit-LLM

    Scikit-LLM

    Seamlessly integrate LLMs into scikit-learn

    Seamlessly integrate powerful language models like ChatGPT into sci-kit-learn for enhanced text analysis tasks. At the moment the majority of the Scikit-LLM estimators are only compatible with some of the OpenAI models. Hence, a user-provided OpenAI API key is required. Additionally, Scikit-LLM will ensure that the obtained response contains a valid label. If this is not the case, a label will be selected randomly (label probabilities are proportional to label occurrences in the training set...
    Downloads: 0 This Week
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  • 3
    ThoughtSource

    ThoughtSource

    A central, open resource for data and tools

    ThoughtSource is a central, open resource and community centered on data and tools for chain-of-thought reasoning in large language models (Wei 2022). Our long-term goal is to enable trustworthy and robust reasoning in advanced AI systems for driving scientific research and medical practice.
    Downloads: 0 This Week
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  • 4
    Emb-GAM

    Emb-GAM

    An interpretable and efficient predictor using pre-trained models

    ... for each input before learning a linear model in the embedding space. The final model (which we call Emb-GAM) is a transparent, linear function of its input features and feature interactions. Leveraging the language model allows Emb-GAM to learn far fewer linear coefficients, model larger interactions, and generalize well to novel inputs. Across a variety of natural-language-processing datasets, Emb-GAM achieves strong prediction performance without sacrificing interpretability.
    Downloads: 0 This Week
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