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A comparative framework for multimodal recommender systems with a focus on models leveraging auxiliary data.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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A curation of awesome tools, documents and projects about LLM Security.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Scalable Machine Learning in Scalding.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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OS-agnostic, system-level binary package manager and ecosystem.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Plugin for CMS Adobe Experience Manager (AEM) or Composum Pages helping the editor to create / edit / translate texts.
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Track, log, visualize and evaluate your LLM prompts and prompt chains.
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Track your datasets, code changes, experimentation history, and models.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Open Code LLMs for Code Understanding and Generation.
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A curated list of language modeling researches for code and related datasets.
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Open-source library for implementing CI/CD in machine learning projects.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Natural Language Processing in Clojure (opennlp).
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Interop with R and Renjin (R on the JVM).
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A machine learning library for Clojure built on top of Weka and friends.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others.
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Python library for data-centric AI and machine learning with messy, real-world data and labels.