Token Classification
Transformers
PyTorch
Safetensors
English
albert
drone-forensics
event-recognition
Instructions to use swardiantara/ADFLER-albert-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swardiantara/ADFLER-albert-base-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="swardiantara/ADFLER-albert-base-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("swardiantara/ADFLER-albert-base-v2") model = AutoModelForTokenClassification.from_pretrained("swardiantara/ADFLER-albert-base-v2") - Notebooks
- Google Colab
- Kaggle
ADFLER-albert-base-v2
This is an 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.
Intended Use
- 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
Usage (Transformers)
Using this model becomes easy when you have transformers installed:
pip install -U transformers
Then you can use the model like this:
from transformers import pipeline
model = pipeline('ner', model='swardiantara/ADFLER-albert-base-v2')
model("Unknown Error, Cannot Takeoff. Contact DJI support.")
Citing & Authors
@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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Model tree for swardiantara/ADFLER-albert-base-v2
Base model
albert/albert-base-v2