---
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: sentence
    dtype: string
  - name: named_entities
    sequence: string
  - name: entity_types
    sequence: string
  - name: entity_languages
    sequence: string
  splits:
  - name: test
    num_bytes: 17175733.0
    num_examples: 172
  download_size: 12084820
  dataset_size: 17175733.0
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
license: cc-by-4.0
language:
- de
size_categories:
- n<1K
---


**SwissNER-Spoken** is a curated collection of 173 short, spoken-style German sentences
    designed to evaluate Named-Entity Recognition (NER) and Automatic Speech Recognition (ASR)
    systems on Swiss-specific proper nouns.

Key features
------------
• **Focus on Switzerland** – Every sentence contains up to three named entities that appear
in everyday Swiss contexts: cities, villages, cantons, companies, mountains, lakes,
rivers, landmarks, organizations, events and well-known personalities.

• **Balanced regional coverage** – All 26 Swiss cantons are represented, with entities
drawn from German-, French-, Italian- and Romansh-speaking areas.

• **Multilingual entity labels** – For each entity, the dataset records the language of the
_name_ (``de``, ``fr``, ``it``, ``rm`` or ``en``) so models can test language-aware
recognition.

• **Compact schema** – Four CSV columns:
    1. ``text`` – the sentence in German.
    2. ``named_entities`` – comma-separated list of the entities in the sentence.
    3. ``entity_types`` – aligned list of coarse entity classes (e.g. *City*, *Company*,
    *Mountain*).
    4. ``entity_languages`` – aligned list of language tags for each entity name.

• **ASR-oriented style** – Sentences are intentionally short, natural, and pronunciation-
friendly, making the corpus ideal for measuring how well speech or text models handle
Swiss proper nouns in real-world utterances.

Typical row
-----------
"Nestlé hat seinen Sitz in Vevey im Kanton Waadt.",
"Nestlé, Vevey, Waadt",
"Company, City, Canton",
"fr, fr, de"

Use cases
---------
* Benchmarking NER models on Swiss entities
* Stress-testing ASR/voice pipelines for pronunciation and transcription accuracy
* Data augmentation or few-shot prompts for multilingual Swiss NLP tasks
* Educational demos for Swiss geography, culture and corporate landscape

License
-------
CC-BY-4.0 – attribution required; no additional restrictions.