transformers pytorch bert token-classification NER en license:other autotrain_compatible endpoints_compatible region:us
---
language:
- en
library_name: transformers
license: other
pipeline_tag: token-classification
tags:
- token-classification
- NER
widget:
- text: While flying a fire, the UAS experienced an issue of unknown sorts and crashed
    to the ground. From the people watching the aircraft near the fire, they seem
    to think it was some sort of motor failure due to no more noise coming from the
    aircraft and it falling straight to the ground.
  example_title: Example 1
- text: During a pre-flight engine run-up, a battery hatch cover disengaged from the
    fuselage and hit one of the vertical takeoff and landing {VTOL} propellers. The
    motor failsafe activated and the motors shut down.
  example_title: Example 2
- text: 'UAS was climbing to 11,000 ft. msl on a reconnaissance mission when it experienced
    a rapid and uncommanded descent. The pilot took no action but monitored instruments
    until the aircraft regained a stable profile. '
  example_title: Example 3
---
# Manager for Intelligent Knowledge Access (MIKA)
## Custom Named-Entity Recognition (NER) for Failure Modes and Effects Analysis (FMEA)

base-bert-uncased model first further pre-trained then fine-tuned for custom NER to extract failure-relevant entities from incident and accident reports. 
The model was trained on manually annotated NASA LLIS reports and evaluated on SAFECOM reports. 

NER model training was for 4 epochs with:`BertForTokenClassification.from_pretrained` , `learning_rate=2e-5`, ` weight_decay=0.01,` 

The model was trained to identify the following long-tailed entities:

- CAU: failure cause
- MOD: failure mode
- EFF: failure effect
- CON: control process
- REC: recommendations

Performace:

|Entity | Precision |Recall | F-1 | Support|
|-------|-----------|-------|-----|--------|
|CAU | 0.31 | 0.19 | 0.23 | 1634 |
|CON | 0.49 | 0.34 | 0.40 | 3859 |
|EFF | 0.45 | 0.20 | 0.28 | 1959 |
|MOD | 0.19 | 0.52 | 0.28 | 594 |
|REC | 0.30 | 0.59 | 0.40 | 954 |
|Average | 0.41 | 0.32 | 0.33 | 9000 |

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


Citation:
S. R. Andrade and H. S. Walsh, "What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition," 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), Portsmouth, VA, USA, 2022, pp. 1-10, doi: 10.1109/DASC55683.2022.9925798.
https://ieeexplore.ieee.org/abstract/document/9925798


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