A pretrained if passport number is in an email classifier that sorts text into one of 2 categories. Use the if passport number 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": "Passport Number Absent",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if passport number 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.
This use case focuses on scanning email correspondence for the presence of passport numbers, enabling organizations to identify potential breaches of sensitive data. By flagging emails that contain these identifiers, businesses can initiate additional security protocols to protect customer information.
In the travel sector, companies can utilize this function to detect fraudulent activities involving fake or stolen passport numbers in reservations. By cross-referencing emails against known patterns, organizations can mitigate fraud risks and enhance their trustworthiness.
Organizations can employ this text classification function to ensure compliance with data protection regulations by identifying and masking passport numbers in emails. This helps in maintaining customer trust and avoiding hefty fines related to data mishandling.
Businesses can integrate this function into their customer service workflows to automatically route inquiries that contain passport numbers to specialized teams. This streamlines the resolution process for travel-related queries and enhances overall customer experience.
Security teams can utilize this function to identify emails containing passport numbers as part of a broader threat analysis framework. By monitoring such communications, they can proactively address potential risks associated with unauthorized access to sensitive travel documents.
Companies can incorporate this text classification to assist in the verification of documents submitted via email, ensuring that only legitimate passport numbers are processed. This minimizes the chances of error in handling critical customer information.
Businesses in sectors like travel can analyze email content for passport number mentions to strengthen targeted marketing efforts. With insights on traveler personas, they can tailor promotions and services to better meet the needs of potential customers, enhancing engagement and conversion rates.
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 passport number 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.