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
File size: 475 Bytes
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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"
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