A pretrained room size classifier that sorts text into one of 5 categories — what room size it is. Use the room size 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": "Extra Large",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 5 room size 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.
The 'room size' identifier can enhance online property listing accuracy by automatically categorizing and tagging rooms based on size. This can help potential buyers filter search results effectively, leading to a more efficient purchasing process.
Interior designers can use the 'room size' identifier to assess and provide recommendations based on the dimensions of each room. This can streamline the design process and ensure that proposed layouts and furniture selections fit appropriately in the designated spaces.
Furniture retailers can leverage the 'room size' identifier to suggest products that are ideal for specific room dimensions. This targeted approach can improve the customer shopping experience by presenting options that match their space requirements.
Contractors can utilize the 'room size' identifier to assist homeowners in identifying how much space they have to work with during renovations. This helps ensure that design plans are practical and suitable for the existing room sizes.
Appraisers can enhance property valuations by integrating the 'room size' identifier to provide a more accurate assessment of a property's value based on its layout and room dimensions. This data can lead to more informed decision-making for buyers and sellers alike.
Real estate marketers can use the 'room size' identifier to segment their audience based on preferences for room dimensions. This allows for focused marketing campaigns that highlight properties that meet specific size criteria, attracting interested buyers more effectively.
Smart home technology platforms can implement the 'room size' identifier to optimize device placements and functionalities within different rooms. By understanding room dimensions, systems can enhance energy efficiency and user experiences through tailored automation settings.
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 room size 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.