NASA-AIML / MIKA_SafeAeroBERT

Manager for Intelligent Knowledge Acess (MIKA)

SafeAeroBERT: A Safety-Informed Aviation-Specific Langauge Model

base-bert-uncased model first further pre-trained on the set of Aviation Safety Reporting System (ASRS) documents up to November of 2022 and National Trasportation Safety Board (NTSB) accident reports up to November 2022. A total of 2,283,435 narrative sections are split 90/10 for training and validation, with 1,052,207,104 tokens from over 350,000 NTSB and ASRS documents used for pre-training.

The model was trained on two epochs using AutoModelForMaskedLM.from_pretrained with a learning_rate=1e-5 , and total batch size of 128 for just over 32100 training steps.

An earlier version of the model was evaluted on a downstream binary document classification task by fine-tuning the model with AutoModelForSequenceClassification.from_pretrained . SafeAeroBERT was compared to SciBERT and base-BERT on this task, with the following performance:

More infomation on training data, evaluation, and intended use can be found in the original publication

Citation: Sequoia R. Andrade and Hannah S. Walsh. "SafeAeroBERT: Towards a Safety-Informed Aerospace-Specific Language Model," AIAA 2023-3437. AIAA AVIATION 2023 Forum. June 2023.

Notices:

Copyright © 2023 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved.

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