readme: ---
  license: apache-2.0
  language: en
  datasets:
  - Jzuluaga/atco2_corpus_1h
  tags:
  - text
  - token-classification
  - en-atc
  - en
  - generated_from_trainer
  - bert
  - ner-for-atc
  metrics:
  - Precision
  - Recall
  - Accuracy
  - F1
  widget:
  - text: "csa two nine six startup approved mike current qnh one zero one eight time check one seven"
  - text: "swiss four eight seven november runway three one cleared for takeoff wind one three zero degrees seven knots"
  - text: "lufthansa five yankee victor runway one three clear to land wind zero seven zero degrees"
  - text: "austrian seven one zulu hello to you reduce one six zero knots"
  - text: "sky travel one nine two approaching holding point three one ready for departure"
  model-index:
  - name: bert-base-ner-atc-en-atco2-1h
    results:
    - task:
          type: token-classification
          name: ner
      dataset:
          type: Jzuluaga/atco2_corpus_1h
          name: ATCO2 corpus (Air Traffic Control Communications)
          config: test
          split: test
      metrics:
      - type: F1
        value: 0.94
        name: TEST F1 (callsign)
        verified: False
      - type: F1
        value: 0.74
        name: TEST F1 (command)
        verified: False
      - type: F1
        value: 0.81
        name: TEST F1 (value)
        verified: False     
  ---
  
  # bert-base-ner-atc-en-atco2-1h
  
  This model allow to perform named-entity recognition (NER) on air traffic control communications data. We solve this challenge by performing token classification (NER) with a BERT model. 
  We fine-tune a pretrained BERT model on the ner task. 
  
  For instance, if you have the following transcripts/gold annotations:
  
  - **Utterance**: lufthansa three two five cleared to land runway three four left
  
  Could you tell what are the main entities in the communication? The desired output is shown below:
  
  - **Named-entity module output**: [call] lufthansa three two five [/call] [cmd] cleared to land [/cmd] [val] runway three four left [/val]
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [atco2_corpus_1h](https://huggingface.co/datasets/Jzuluaga/atco2_corpus_1h). 
  
  <a href="https://github.com/idiap/atco2-corpus">
      <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\">
  </a>
  
  It achieves the following results on the development set:
  - Loss: 1.4282
  - Precision: 0.6195
  - Recall: 0.7071
  - F1: 0.6604
  - Accuracy: 0.8182
  
  **Paper**: [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://arxiv.org/abs/2211.04054)
  
  Authors: Juan Zuluaga-Gomez, Karel Veselý, Igor Szöke, Petr Motlicek, Martin Kocour, Mickael Rigault, Khalid Choukri, Amrutha Prasad and others
  
  Abstract: Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at this http URL. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at this url: https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community. 
  
  Code — GitHub repository: https://github.com/idiap/atco2-corpus
  
  
  ## Intended uses & limitations
  
  This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets where BERT was pre-trained or fine-tuned.
  
  ## Training and evaluation data
  
  See Table 6 (page 18) in our paper: [ATCO2 corpus: A