A pretrained light pollution level classifier that sorts text into one of 10 categories — the level of light pollution in a given area. Use the light pollution level API immediately, no training required, then adapt it to your own data when you need more.
Drop in some text and get the prediction back. No signup, no setup.
A sample of the 10 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "The text you want to classify"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "The text you want to classify"},
)
print(response.json())Example response
{
"labelName": "Bright",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 light pollution level categories, served on Nyckel's own infrastructure — your text snippet stays on Nyckel.
Send raw text to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
City planners can utilize the 'light pollution level' identifier to assess the impact of new developments on local environments. By analyzing light pollution data, they can make informed decisions to reduce glare and enhance the quality of life for residents.
Researchers in astronomy can leverage this function to identify areas with excessive light pollution that hinder astronomical observations. By cataloging these levels, they can advocate for policies to reduce artificial light and improve the visibility of celestial objects.
Conservationists can use light pollution identifiers to study the effects of artificial lighting on wildlife behaviors, particularly for nocturnal species. Understanding light pollution levels in specific habitats can guide restoration efforts to create more natural environments.
Public health organizations can utilize this classification function to explore correlations between light pollution and health issues like sleep disorders. By identifying high light pollution areas, they can raise awareness and promote initiatives aimed at reducing night-time illumination.
Smart city solutions can incorporate the 'light pollution level' identifier to manage street lighting more efficiently. By regulating street lights based on real-time assessments of light pollution, cities can save energy and minimize unnecessary light exposure.
Real estate developers can use light pollution data to choose locations for new housing projects. By opting for areas with lower light pollution levels, they can attract eco-conscious buyers and enhance the appeal of living spaces.
Tourism boards can leverage light pollution levels as a marketing tool to promote dark sky areas for stargazing and nature tourism. By highlighting regions with minimal light pollution, they can attract visitors interested in nighttime recreational activities and celestial events.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of text samples in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This light pollution level classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.