يعرض 1 - 20 نتائج من 190 نتيجة بحث عن '((((python model) OR (python tool))) OR (python code)) represent', وقت الاستعلام: 0.35s تنقيح النتائج
  1. 1

    Resolving Harvesting Errors in Institutional Repository Migration : Using Python Scripts with VS Code and LLM Integration. حسب satoshi hashimoto(橋本 郷史) (18851272)

    منشور في 2025
    "…Therefore, we decided to create a dedicated Python program using Large Language Model (LLM)-assisted coding.…"
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    Multi-Version PYZ Builder Script: A Universal Python Module Creation Tool حسب Pavel Izosimov (20096259)

    منشور في 2024
    "…This tool represents a significant advancement in the realm of <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank"><b>secure code sharing</b></a>, providing a robust solution for modern Python programming challenges.…"
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    System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool حسب Pavel Izosimov (20096259)

    منشور في 2024
    "…</p><ul><li>For advanced <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">Python code protection tools</a>, consider using the <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">Local Python Code Protector Script</a>. …"
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    City-level GDP estimates for China under alternative pathways from 2020 to 2100-python code حسب Jinjie Sun (11791715)

    منشور في 2025
    "…The dataset is complemented by processing code and raw input data in the "Python_Code" directory to ensure full reproducibility. …"
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    Python code for hierarchical cluster analysis of detected R-strategies from rule-based NLP on 500 circular economy definitions حسب Zahir Barahmand (18008947)

    منشور في 2025
    "…</p><p dir="ltr">This Python code was optimized and debugged using ChatGPT-4o to ensure implementation efficiency, accuracy, and clarity.…"
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    Comparison of tools with features similar to <i>bmdrc,</i> and a descriptions of the modules within the <i>bmdrc</i> package.  حسب David J. Degnan (13886280)

    منشور في 2025
    "…<p>(A) Highlighted tool features from a selection of benchmark dose modeling tools to contextualize the needs bmdrc and other existing tools fill. …"
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    Code program. حسب Honglei Pang (22693724)

    منشور في 2025
    "…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …"
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    Python implementation of a wildfire propagation example using m:n-CAk over Z and R. حسب Pau Fonseca i Casas (9507338)

    منشور في 2025
    "…</p><p dir="ltr"><br></p><p dir="ltr">## Files in the Project</p><p dir="ltr"><br></p><p dir="ltr">### Python Scripts</p><p dir="ltr">- **Wildfire_on_m_n-CAk.py**: This file contains the main code for the fire cellular automaton. …"
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    Code interpreter with LLM. حسب Jin Lu (428513)

    منشور في 2025
    "…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"
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    Datasets To EVAL. حسب Jin Lu (428513)

    منشور في 2025
    "…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"
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    Statistical significance test results. حسب Jin Lu (428513)

    منشور في 2025
    "…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"
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    How RAG work. حسب Jin Lu (428513)

    منشور في 2025
    "…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"
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    OpenBookQA experimental results. حسب Jin Lu (428513)

    منشور في 2025
    "…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"
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    AI2_ARC experimental results. حسب Jin Lu (428513)

    منشور في 2025
    "…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"
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    TQA experimental results. حسب Jin Lu (428513)

    منشور في 2025
    "…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"