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 on the atco2_corpus_1h.
It achieves the following results on the development set:
- Loss: 1.4282
- Precision: 0.6195
- Recall: 0.7071
- F1: 0.6604
- Accuracy: 0.8182
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 Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications. We described there the data used to fine-tune our NER model.
- We use the ATCO2 corpus to fine-tune this model. You can download a free sample here: https://www.atco2.org/data
- However, do not worry, we have prepared a script in our repository for preparing this databases:
- Dataset preparation folder: https://github.com/idiap/atco2-corpus/tree/main/data/databases/atco2_test_set_1h/data_prepare_atco2_corpus_other.sh
- Get the data in the format required by HuggingFace: speaker_role/data_preparation/prepare_spkid_atco2_corpus_test_set_1h.sh
Writing your own inference script
The snippet of code:
from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-ner-atc-en-atco2-1h")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-ner-atc-en-atco2-1h")
##### Process text sample
from transformers import pipeline
nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first")
nlp("lufthansa three two five cleared to land runway three four left")
# output:
[{'entity_group': 'callsign', 'score': 0.8753265,
'word': 'lufthansa three two five',
'start': 0, 'end': 24},
{'entity_group': 'command', 'score': 0.99988264,
'word': 'cleared to land', 'start': 25, 'end': 40},
{'entity_group': 'value', 'score': 0.9999145,
'word': 'runway three four left', 'start': 41, 'end': 63}]
Cite us
If you use this code for your research, please cite our paper with:
@article{zuluaga2022bertraffic,
title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
and,
@article{zuluaga2022how,
title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
and,
@article{zuluaga2022atco2,
title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
journal={arXiv preprint arXiv:2211.04054},
year={2022}
}
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 125.0 | 500 | 0.8692 | 0.6396 | 0.7172 | 0.6762 | 0.8307 |
0.2158 | 250.0 | 1000 | 1.0074 | 0.5702 | 0.6970 | 0.6273 | 0.8245 |
0.2158 | 375.0 | 1500 | 1.3560 | 0.6577 | 0.7374 | 0.6952 | 0.8119 |
0.0184 | 500.0 | 2000 | 1.3393 | 0.6182 | 0.6869 | 0.6507 | 0.8056 |
0.0184 | 625.0 | 2500 | 1.3528 | 0.6087 | 0.7071 | 0.6542 | 0.8213 |
0.0175 | 750.0 | 3000 | 1.4282 | 0.6195 | 0.7071 | 0.6604 | 0.8182 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2
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Model tree for Jzuluaga/bert-base-ner-atc-en-atco2-1h
Base model
google-bert/bert-base-uncasedDataset used to train Jzuluaga/bert-base-ner-atc-en-atco2-1h
Evaluation results
- TEST F1 (callsign) on ATCO2 corpus (Air Traffic Control Communications)test set self-reported0.940
- TEST F1 (command) on ATCO2 corpus (Air Traffic Control Communications)test set self-reported0.740
- TEST F1 (value) on ATCO2 corpus (Air Traffic Control Communications)test set self-reported0.810