A pretrained if phone number is in an email classifier that sorts text into one of 2 categories. Use the if phone 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": "Contains Phone Number",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if phone 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 function can help email filtering systems identify potential spam or phishing attempts by checking for unexpected phone numbers in emails. When a message includes a phone number, it can trigger additional scrutiny or be marked for further review, improving overall email security.
Businesses can use this identifier in their customer outreach efforts to verify if a phone number is provided in the emails from leads. If phone numbers are found, sales teams can prioritize follow-ups based on the completeness of the lead information, enhancing the effectiveness of their conversion strategies.
Automated systems can utilize this function to streamline responses to customer inquiries found in emails that contain phone numbers. This enables quicker routing to the appropriate support channels, enhancing customer service efficiency and responsiveness.
Organizations can implement this function to ensure compliance with data protection regulations by flagging emails that contain personal phone numbers. Monitoring such occurrences can help maintain privacy standards and reduce the risk of unauthorized sharing of sensitive information.
Marketing teams can analyze outgoing email campaigns using this identifier to track how often contact numbers are included. By assessing this data, they can adjust content strategies and improve customer engagement through targeted calls-to-action.
Financial and e-commerce platforms can employ this function to detect suspicious emails that contain phone numbers, which may be associated with fraudulent behavior. By identifying these instances, businesses can initiate further investigation, safeguarding customer transactions.
Customer Relationship Management (CRM) systems can leverage this identifier to enrich existing customer profiles when they receive emails with phone numbers. Automating the update process improves data accuracy and enables personalized communications with clients.
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 phone 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.