{
  "id": "sherlock",
  "name": "Sherlock",
  "category": "detector",
  "doc": "Sherlock is a deep learning model from the MIT Media Lab that predicts the semantic type of a table column from its values, without using the column header. It was trained on 686,765 columns of the VizNet corpus whose headers matched one of 78 semantic types taken from DBpedia properties, such as city, birth Date or isbn. Many of these types are header words rather than value types, such as code, type or rank. The registry records that carry a Sherlock type in mappings.sherlock are listed here. The code and the trained model are released under the MIT license.",
  "website": "https://sherlock.media.mit.edu",
  "supported_types": [
    "address",
    "age",
    "birthday",
    "birthplace",
    "brand",
    "category",
    "city",
    "comment",
    "continent",
    "country",
    "currency",
    "dc_creator",
    "dc_publisher",
    "description",
    "duration",
    "elevation",
    "filesize",
    "gender",
    "genre",
    "industry",
    "isbn",
    "language",
    "name",
    "nationality",
    "orgname",
    "person_fullname",
    "religion",
    "species",
    "weight",
    "year"
  ],
  "source": {
    "path": "data/tools/detectors/sherlock.yaml",
    "url": "https://github.com/apicrafter/metacrafter-registry/blob/main/data/tools/detectors/sherlock.yaml",
    "edit_url": "https://github.com/apicrafter/metacrafter-registry/edit/main/data/tools/detectors/sherlock.yaml"
  },
  "urls": {
    "html": "http://registry.apicrafter.io/tool/sherlock",
    "json": "http://registry.apicrafter.io/api/v1/tools/sherlock.json"
  }
}