A pretrained if password is in text classifier that sorts text into one of 2 categories. Use the if password is in text 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": "Password Not Present",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if password is in text 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.
Organizations can utilize the 'if password is in text' identifier to ensure that sensitive information, such as passwords, is not inadvertently transmitted in emails or documents. By monitoring communication channels, businesses can maintain compliance with data protection regulations and mitigate the risk of data breaches.
The function can be integrated into user input forms on websites and applications to validate that users do not enter passwords or other sensitive data into inappropriate fields. This proactive measure helps prevent accidental exposure of user credentials and enhances overall security.
Implementing this text classification function can aid in the identification of potential phishing attempts in email communications. By flagging messages that contain password-related information, companies can protect employees from social engineering scams aimed at stealing credentials.
Customer support systems can incorporate this identifier to filter out messages that contain passwords shared by users during support interactions. This enables automated responses to warn users against sharing sensitive information and ensures that customer support representatives handle such data correctly.
Online platforms with chat functionalities can deploy this function to monitor group discussions and direct messages for instances where users share passwords. By automatically detecting these occurrences, platforms can intervene to enforce community guidelines and promote safe behaviors among users.
Organizations can implement the text classification function to monitor internal communications for unauthorized sharing of passwords. By detecting such behavior, companies can take action to prevent potential insider threats and protect sensitive data from being misused within the organization.
The identifier can be used to analyze training materials or internal communications to ensure that employees are not inadvertently providing guidance on sharing passwords. This insight can inform training programs to raise awareness about secure password management practices and reinforce security culture within the company.
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 password is in text 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.