A pretrained if password is in an email classifier that sorts text into one of 2 categories. Use the if password is in an email 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": "No Password In Email",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if password is in an email 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 implement this function to automatically scan incoming and outgoing emails for the presence of passwords. By identifying emails that contain passwords, security teams can take immediate action to mitigate potential data breaches and ensure compliance with IT security policies.
This text classification can be used in phishing detection systems to identify suspicious emails that may be attempting to solicit sensitive information. By flagging emails that contain passwords, companies can alert users and reduce the risk of falling victim to phishing attacks.
Businesses can use this function as part of their data leak prevention strategies to prevent employees from inadvertently sharing passwords via email. By monitoring for passwords in emails, organizations can enforce policies that protect critical information and maintain data integrity.
Implement this capability in user authentication systems to enhance password security. When users receive emails containing their passwords, alerts can be generated to inform them of the potentially insecure situation and encourage immediate password changes.
Companies in regulated industries, such as finance and healthcare, can leverage this function to ensure compliance with regulations regarding the handling of sensitive information. By detecting and flagging emails that contain passwords, organizations can demonstrate that they are taking proactive steps to protect sensitive data.
This feature can enhance customer support systems by identifying emails that include user passwords in support requests. Automated responses can be triggered to advise customers against sharing sensitive information via email and guide them to safer communication channels.
Organizations can analyze the results from this text classification to identify patterns in employees’ email behavior. This data can then be used to tailor training programs on secure password handling practices and reduce the risk of sensitive information being compromised.
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 an email 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.