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--- |
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dataset_info: |
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features: |
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- name: audio |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: sentence |
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dtype: string |
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- name: named_entities |
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sequence: string |
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- name: entity_types |
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sequence: string |
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- name: entity_languages |
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sequence: string |
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splits: |
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- name: test |
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num_bytes: 17175733.0 |
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num_examples: 172 |
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download_size: 12084820 |
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dataset_size: 17175733.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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license: cc-by-4.0 |
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language: |
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- de |
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size_categories: |
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- n<1K |
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--- |
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**SwissNER-Spoken** is a curated collection of 173 short, spoken-style German sentences |
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designed to evaluate Named-Entity Recognition (NER) and Automatic Speech Recognition (ASR) |
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systems on Swiss-specific proper nouns. |
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Key features |
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------------ |
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• **Focus on Switzerland** – Every sentence contains up to three named entities that appear |
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in everyday Swiss contexts: cities, villages, cantons, companies, mountains, lakes, |
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rivers, landmarks, organizations, events and well-known personalities. |
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• **Balanced regional coverage** – All 26 Swiss cantons are represented, with entities |
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drawn from German-, French-, Italian- and Romansh-speaking areas. |
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• **Multilingual entity labels** – For each entity, the dataset records the language of the |
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_name_ (``de``, ``fr``, ``it``, ``rm`` or ``en``) so models can test language-aware |
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recognition. |
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• **Compact schema** – Four CSV columns: |
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1. ``text`` – the sentence in German. |
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2. ``named_entities`` – comma-separated list of the entities in the sentence. |
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3. ``entity_types`` – aligned list of coarse entity classes (e.g. *City*, *Company*, |
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*Mountain*). |
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4. ``entity_languages`` – aligned list of language tags for each entity name. |
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• **ASR-oriented style** – Sentences are intentionally short, natural, and pronunciation- |
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friendly, making the corpus ideal for measuring how well speech or text models handle |
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Swiss proper nouns in real-world utterances. |
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Typical row |
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----------- |
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"Nestlé hat seinen Sitz in Vevey im Kanton Waadt.", |
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"Nestlé, Vevey, Waadt", |
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"Company, City, Canton", |
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"fr, fr, de" |
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Use cases |
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--------- |
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* Benchmarking NER models on Swiss entities |
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* Stress-testing ASR/voice pipelines for pronunciation and transcription accuracy |
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* Data augmentation or few-shot prompts for multilingual Swiss NLP tasks |
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* Educational demos for Swiss geography, culture and corporate landscape |
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License |
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------- |
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CC-BY-4.0 – attribution required; no additional restrictions. |
