A pretrained cleanliness rating classifier that sorts text into one of 5 categories — the cleanliness rating of various environments. Use the cleanliness rating 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 5 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": "1 Star",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 5 cleanliness rating 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.
Hotels and restaurants can use the cleanliness rating identifier to automatically assess reviews and feedback from guests. This will help them quickly identify areas needing improvement and enhance overall customer satisfaction by maintaining high cleanliness standards.
Food industry regulators can utilize the cleanliness rating identifier to analyze reports from various establishments. By identifying compliance issues through this function, they can foster safer food environments for consumers and ensure higher hygienic standards in food service operations.
Property management companies can leverage this tool to monitor cleanliness ratings of their rental properties through tenant feedback. By addressing cleanliness issues proactively, they can improve tenant retention and enhance the property’s marketability.
Tourism boards can assess cleanliness ratings derived from visitor reviews to promote destinations better. Positive cleanliness indicators can be highlighted in marketing materials to attract more tourists, enhancing the region’s reputation and driving economic growth.
Retailers can analyze customer feedback related to store cleanliness to identify patterns that may affect shopping experiences. This function can prompt targeted clean-up efforts and ultimately lead to increased customer satisfaction and sales.
Event coordinators can use the cleanliness rating identifier to evaluate potential venues based on historical cleanliness feedback from past events. Choosing a venue with high cleanliness ratings can enhance the overall experience for attendees and reflect positively on the event organizers.
Public health organizations can implement the cleanliness rating identifier to track cleanliness trends across various public spaces, such as parks or restrooms. This information is vital for ensuring community health and can prompt timely interventions when needed.
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 cleanliness rating 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.