A pretrained spice levels classifier that sorts text into one of 9 categories — the optimal spice level for your dish. Use the spice levels 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 9 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 Hot",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 9 spice levels 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.
Restaurants could utilize the 'spice levels' identifier to customize digital menus based on customer preferences. By analyzing previous orders and feedback, establishments can recommend dishes that align with an individual's spice tolerance, enhancing the dining experience.
Delivery platforms can implement the spice levels identifier to provide tailored recommendations for customers. By allowing users to filter or sort dishes based on their desired spice level, the service can improve customer satisfaction and reduce order complaints related to spice levels.
Cooking apps can use the spice levels identifier to help users find recipes that match their palate. By allowing users to specify their preferred spice level, the app can filter recipes and suggest suitable alternatives, promoting better cooking experiences.
Restaurants can analyze customer reviews with the spice levels identifier to understand preferences and potential issues with their dishes. This insight can guide chefs on how to modify or develop menu items to better suit their customer base.
Nutrition apps could incorporate the spice levels identifier to help users adjust their diets according to spice tolerance. This can be particularly beneficial for individuals with health conditions that necessitate specific dietary requirements regarding spice and irritants.
Catering companies can leverage the spice levels identifier to design custom menus for events. By collecting information on the spice preferences of guests, they can ensure that the food served accommodates varying tastes, promoting a more enjoyable event experience.
Cooking schools and culinary training programs can use the spice levels identifier in their curricula to teach students about flavor profiles. By focusing on how to balance spices according to different levels, students can learn to create dishes that cater to diverse customer preferences.
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 spice levels 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.