A pretrained city skyline ratings classifier that sorts text into one of 10 categories — the skyline rating of various cities.. Use the city skyline ratings 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 23 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.
Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "The text you want to classify"}'
import requests
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer YOUR_ACCESS_TOKEN"},
json={"data": "The text you want to classify"},
)
print(response.json())
const response = await fetch("https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_ACCESS_TOKEN",
"Content-Type": "application/json",
},
body: JSON.stringify({ data: "The text you want to classify" }),
});
console.log(await response.json());
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{"data": "The text you want to classify"}');
$headers = array();
$headers[] = 'Authorization: Bearer YOUR_ACCESS_TOKEN';
$headers[] = 'Content-Type: application/json';
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
$result = curl_exec($ch);
curl_close($ch);
echo $result;
Example response
{
"labelName": "1 Out Of 10",
"labelId": "label_...",
"confidence": 0.92
}
Trained on a Nyckel-curated dataset covering 10 city skyline ratings 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.
This function can be utilized by city planners and developers to gauge public sentiment about various urban skyline designs. By classifying text feedback from citizens regarding new skyscraper projects, planners can assess the potential acceptance or opposition to proposed changes.
Real estate agents can leverage this function to analyze online reviews and social media posts related to properties near city skylines. Understanding public sentiment can help agents better market properties either as desirable living spaces or areas needing improvement.
City tourism boards can use skyline ratings to craft targeted marketing campaigns that highlight scenic viewpoints. By analyzing community sentiment about various city skylines, they can promote specific locations that garner the most positive feedback.
Government bodies can utilize skyline ratings to determine priority areas for infrastructure investments, such as parks or public spaces with skyline views. Positive ratings can indicate regions that could benefit from enhancements to attract more visitors and residents.
Local governments can use this function to assess the effectiveness of community engagement programs focused on urban aesthetics. By analyzing text data from surveys and feedback forms, they can refine initiatives to better align with public opinions about skyline appearances.
Companies within the hospitality sector, like hotels and restaurants, can use skyline ratings to shape their brand image. By understanding how their establishments contribute to or detract from the skyline perception, they can enhance visual appeal and customer experience.
Environmental agencies can employ sentiment analysis on text related to skyline changes and their ecological effects, such as light pollution and green space reduction. This data can inform policy decisions and community education efforts to minimize negative impacts while promoting skyline-friendly developments.
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 city skyline ratings 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.