---
pipeline_tag: token-classification
tags:
- drone-forensics
- event-recognition
license: mit
language:
- en
base_model:
- albert/albert-base-v2
library_name: transformers
---

# ADFLER-albert-base-v2

This is an [albert-base-v2](https://huggingface.co/albert/albert-base-v2) model fine-tuned on a collection of drone flight log messages: It performs log event recognition by assigning NER tag to each token within the input message using the BIOES tagging scheme.

For more detailed information about the model, please refer to the Albert's model card.

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## Intended Use
![Description of Image](./concept.png)
- Use to split log records into sentences as well as detecting if the sentence is an event message or not.
- This model is trained diverse drone log messages from various models acquired from [Air Data](https://app.airdata.com/wiki/Notifications/)
## Usage (Transformers)

Using this model becomes easy when you have [transformers](https://www.SBERT.net) installed:

```
pip install -U transformers
```

Then you can use the model like this:

```python
from transformers import pipeline
model = pipeline('ner', model='swardiantara/ADFLER-albert-base-v2')

model("Unknown Error, Cannot Takeoff. Contact DJI support.")
```

## Citing & Authors
```bibtex
@misc{albert_ner_model,
  author={Silalahi, Swardiantara and Ahmad, Tohari and Studiawan, Hudan},
  title = {ALBERT Model for Drone Flight Log Event Recognition},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub}
}
```
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