A pretrained if username contains a profanity classifier that sorts text into one of 2 categories. Use the if username contains a profanity 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": "Clean",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if username contains a profanity 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 use case involves automatically identifying and flagging usernames that contain profanity during the account registration process. By filtering out inappropriate usernames, businesses can maintain a professional online environment and enhance the overall user experience.
For platforms that allow user-generated content, this functionality helps screen usernames before they post comments or articles. By preventing profane usernames, the platform promotes a respectful community and minimizes the risk of offensive content.
In online gaming, this classification can be used to monitor player usernames to ensure they adhere to community guidelines. Filtering out profanity helps create a more enjoyable gaming experience and protects players, particularly younger ones, from toxic interactions.
Social media platforms can utilize this function to review user usernames during account creation and profile updates. By prohibiting profane usernames, they can foster a safer environment for users and reduce the likelihood of harassment or bullying.
For e-commerce businesses, this function can help prevent inappropriate usernames during user registration for shopping accounts. This decreases the chance of alienating potential customers and supports a positive brand image in a competitive market.
Online forums and discussion boards can implement this identifier to screen usernames in order to maintain a respectful discourse. By filtering out profane usernames, forums can protect users from offensive language and cultivate a welcoming atmosphere for all members.
Educational websites and e-learning platforms can use this classification to ensure all usernames comply with appropriate standards. By monitoring and filtering out profane usernames, these platforms can make learning spaces more inclusive and family-friendly, enhancing the experience for students of all ages.
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 if username contains a profanity 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.