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metadata
license: mit
dataset_info:
  features:
    - name: name
      dtype: string
    - name: language
      dtype: string
    - name: description
      dtype: string
    - name: source
      dtype: string
    - name: public
      dtype: bool
    - name: id
      dtype: int64
    - name: lastModification
      dtype: timestamp[us, tz=UTC]
    - name: keywords
      sequence: string
    - name: embedding
      sequence: float64
    - name: content
      dtype: string
  splits:
    - name: train
      num_bytes: 43495906
      num_examples: 1446
  download_size: 39682949
  dataset_size: 43495906
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - feature-extraction
language:
  - de
  - en
tags:
  - government
  - german
  - munich
  - services
  - administration
  - citizen
pretty_name: Munich Public Services / Münchner Verwaltungs-Dienstleistungen
size_categories:
  - 1K<n<10K

Dataset Card for Munich Public Services (🇩🇪 below)

This dataset contains information about the services provided to the public by the City of Munich in the form of written articles, corresponding metadata as well as embeddings.

Dataset Details

Dataset Description

The Munich Public Services dataset contains around 1.400 articles about various public services provided by the City of Munich. Next to the content of the articles, the dataset also includes metadata such the language of the article, the last modification date, and the keywords associated with the article. The dataset also includes embedding vectors computed from each article, which can be used for Retrieval or other NLP tasks.

  • Curated by: City of Munich, it@M, KI Competence Center
  • Language(s) (NLP): German, English
  • License: MIT

Dataset Sources

The dataset is sourced from the city of Munich's public services website and it's underlying CMS. All articles were written by the city of Munich's employees. The other metadata was extracted from the CMS. The embeddings were generated using OPENAI's text-embedding-3-large model.

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