"model card" meaning in All languages combined

See model card on Wiktionary

Noun [English]

Forms: model cards [plural]
Etymology: Described in a 2019 paper, see quotations. Head templates: {{en-noun}} model card (plural model cards)
  1. (artificial intelligence) A document describing a machine learning model, often including information about training datasets, biases, benchmarks, and ethical considerations. Synonyms: system card

Inflected forms

{
  "etymology_text": "Described in a 2019 paper, see quotations.",
  "forms": [
    {
      "form": "model cards",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {},
      "expansion": "model card (plural model cards)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        {
          "kind": "other",
          "name": "English entries with incorrect language header",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with 1 entry",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with entries",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "langcode": "en",
          "name": "Artificial intelligence",
          "orig": "en:Artificial intelligence",
          "parents": [],
          "source": "w"
        }
      ],
      "examples": [
        {
          "bold_text_offsets": [
            [
              11,
              22
            ]
          ],
          "ref": "2019 January 29, Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, “Model Cards for Model Reporting”, in Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19), New York, NY, USA: Association for Computing Machinery, →DOI, →ISBN, pages 220–229:",
          "text": "We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works.",
          "type": "quotation"
        },
        {
          "bold_text_offsets": [
            [
              13,
              23
            ]
          ],
          "ref": "2020 July 29, Huanming Fang, Hui Miao, “Introducing the Model Card Toolkit for Easier Model Transparency Reporting”, in Google Research, Google:",
          "text": "To guide the Model Card creator to organize model information",
          "type": "quotation"
        },
        {
          "bold_text_offsets": [
            [
              0,
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            ],
            [
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            ]
          ],
          "ref": "2020, Abhishek Wadhwani, Priyank Jain, “Machine Learning Model Cards Transparency Review : Using model card toolkit”, in 2020 IEEE Pune Section International Conference (PuneCon), →DOI, pages 133-137:",
          "text": "Model cards are a very recent and hot topic of research. In Machine Learning (ML), transparency with model cards is relevant as it affects a wide range of domains, from health care to finance and jobs, etc.",
          "type": "quotation"
        },
        {
          "bold_text_offsets": [
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              11,
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          ],
          "ref": "2025, Fabian Ferrari, “The Governanceof Generative AI: Three Conditions for Research and Policy”, in José Dijck, Karin Es, Anne Helmond, Fernando Vlist, editors, Governing the Digital Society: Platforms, Artificial Intelligence, and Public Values, Taylor & Francis, →ISBN:",
          "text": "So-called “model cards” have emerged as a standardization tool for AI developers to comprehensively document all key aspects of generative AI systems, including domainspecific training datasets, biases, and ethical considerations (Mitchell et al. 2019).",
          "type": "quotation"
        }
      ],
      "glosses": [
        "A document describing a machine learning model, often including information about training datasets, biases, benchmarks, and ethical considerations."
      ],
      "id": "en-model_card-en-noun-ZgKxV1jn",
      "links": [
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          "artificial intelligence",
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        ],
        [
          "machine learning",
          "machine learning"
        ],
        [
          "model",
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        ]
      ],
      "qualifier": "artificial intelligence",
      "raw_glosses": [
        "(artificial intelligence) A document describing a machine learning model, often including information about training datasets, biases, benchmarks, and ethical considerations."
      ],
      "synonyms": [
        {
          "word": "system card"
        }
      ]
    }
  ],
  "word": "model card"
}
{
  "etymology_text": "Described in a 2019 paper, see quotations.",
  "forms": [
    {
      "form": "model cards",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {},
      "expansion": "model card (plural model cards)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        "English countable nouns",
        "English entries with incorrect language header",
        "English lemmas",
        "English multiword terms",
        "English nouns",
        "English terms with quotations",
        "Pages with 1 entry",
        "Pages with entries",
        "en:Artificial intelligence"
      ],
      "examples": [
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          "bold_text_offsets": [
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            ]
          ],
          "ref": "2019 January 29, Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, “Model Cards for Model Reporting”, in Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19), New York, NY, USA: Association for Computing Machinery, →DOI, →ISBN, pages 220–229:",
          "text": "We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works.",
          "type": "quotation"
        },
        {
          "bold_text_offsets": [
            [
              13,
              23
            ]
          ],
          "ref": "2020 July 29, Huanming Fang, Hui Miao, “Introducing the Model Card Toolkit for Easier Model Transparency Reporting”, in Google Research, Google:",
          "text": "To guide the Model Card creator to organize model information",
          "type": "quotation"
        },
        {
          "bold_text_offsets": [
            [
              0,
              11
            ],
            [
              101,
              112
            ]
          ],
          "ref": "2020, Abhishek Wadhwani, Priyank Jain, “Machine Learning Model Cards Transparency Review : Using model card toolkit”, in 2020 IEEE Pune Section International Conference (PuneCon), →DOI, pages 133-137:",
          "text": "Model cards are a very recent and hot topic of research. In Machine Learning (ML), transparency with model cards is relevant as it affects a wide range of domains, from health care to finance and jobs, etc.",
          "type": "quotation"
        },
        {
          "bold_text_offsets": [
            [
              11,
              22
            ]
          ],
          "ref": "2025, Fabian Ferrari, “The Governanceof Generative AI: Three Conditions for Research and Policy”, in José Dijck, Karin Es, Anne Helmond, Fernando Vlist, editors, Governing the Digital Society: Platforms, Artificial Intelligence, and Public Values, Taylor & Francis, →ISBN:",
          "text": "So-called “model cards” have emerged as a standardization tool for AI developers to comprehensively document all key aspects of generative AI systems, including domainspecific training datasets, biases, and ethical considerations (Mitchell et al. 2019).",
          "type": "quotation"
        }
      ],
      "glosses": [
        "A document describing a machine learning model, often including information about training datasets, biases, benchmarks, and ethical considerations."
      ],
      "links": [
        [
          "artificial intelligence",
          "artificial intelligence"
        ],
        [
          "machine learning",
          "machine learning"
        ],
        [
          "model",
          "model"
        ]
      ],
      "qualifier": "artificial intelligence",
      "raw_glosses": [
        "(artificial intelligence) A document describing a machine learning model, often including information about training datasets, biases, benchmarks, and ethical considerations."
      ],
      "synonyms": [
        {
          "word": "system card"
        }
      ]
    }
  ],
  "word": "model card"
}

Download raw JSONL data for model card meaning in All languages combined (3.1kB)


This page is a part of the kaikki.org machine-readable All languages combined dictionary. This dictionary is based on structured data extracted on 2026-03-07 from the enwiktionary dump dated 2026-03-03 using wiktextract (d146717 and 59dc20b). The data shown on this site has been post-processed and various details (e.g., extra categories) removed, some information disambiguated, and additional data merged from other sources. See the raw data download page for the unprocessed wiktextract data.

If you use this data in academic research, please cite Tatu Ylonen: Wiktextract: Wiktionary as Machine-Readable Structured Data, Proceedings of the 13th Conference on Language Resources and Evaluation (LREC), pp. 1317-1325, Marseille, 20-25 June 2022. Linking to the relevant page(s) under https://kaikki.org would also be greatly appreciated.