Instructions to use dronenlp/DroNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dronenlp/DroNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dronenlp/DroNER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dronenlp/DroNER") model = AutoModelForTokenClassification.from_pretrained("dronenlp/DroNER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: gpl
language:
- en
metrics:
- accuracy
pipeline_tag: token-classification
widget:
- text: >-
Battery temperature is below 15 degrees Celsius. Warm up the battery
temperature to above 25 degree Celsius to ensure a safe flight.
example_title: Example 1
- text: >-
Aircraft is returning to the Home Point. Minimum RTH Altitude is 30m. You
can reset the RTH Altitude in Remote Controller Settings after cancelling
RTH if necessary.
example_title: Example 2