A pretrained is gluten-free classifier that sorts text into one of 2 categories. Use the is gluten-free 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 2 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": "Contains Gluten",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 is gluten-free 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.
Businesses in the food industry can use the 'is gluten-free' identifier to ensure that their product labeling adheres to regulatory standards. This classification helps manufacturers accurately convey gluten-free status, avoiding potential legal issues and enhancing consumer trust.
Online retailers can implement this text classification function to allow customers to filter products based on gluten-free status. This enhances user experience by quickly directing customers to suitable products, ultimately increasing sales in the gluten-free segment.
Health and wellness apps can integrate the gluten-free identifier to provide users with nutritional insights. This feature can help individuals with gluten sensitivities or celiac disease manage their diets more effectively by identifying safe food choices.
Cooking websites and apps can use the classification to suggest gluten-free alternatives in recipes. By identifying gluten-free ingredients, users can easily modify traditional recipes to meet dietary needs, increasing the platform's usability and appeal.
Restaurants and food service companies can employ this identifier to streamline food safety checks. By ensuring that gluten-free items are accurately identified and prepared separately, they can minimize the risk of cross-contamination for sensitive diners.
Brands can leverage the 'is gluten-free' classification for targeted marketing campaigns. By identifying and promoting gluten-free products to conscious consumers, businesses can effectively reach and engage a niche market segment.
Food distributors can utilize this classification in inventory management systems to track gluten-free products. This can optimize stock levels, reduce waste, and improve order fulfillment accuracy for gluten-sensitive consumers.
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 is gluten-free 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.