A pretrained if phone number is in a text message classifier that sorts text into one of 2 categories. Use the if phone number is in a text message 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 a text message 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.
The True text classification function can be utilized by email and messaging platforms to identify spam messages containing phone numbers. By flagging or filtering such messages, it helps minimize spam and protects users from potential scams.
Businesses can implement this function in customer support systems to scan incoming messages for phone numbers. This allows for automated responses or ticket generation, facilitating quicker follow-ups and improving customer experience.
Sales teams can use this identifier to sift through text messages from potential leads. By identifying messages that contain phone numbers, they can prioritize leads for follow-up, focusing on high-value prospects that provide direct contact information.
Organizations can leverage this function to monitor communications for compliance with regulations such as GDPR. By flagging messages that contain phone numbers, they can ensure that personal data is being handled and stored in accordance with relevant laws.
Financial institutions can implement the phone number identifier in their transaction monitoring systems to detect fraudulent activities. Messages with phone numbers that appear suspicious can trigger alerts or further investigation to protect customers.
Marketing teams can analyze the reception of promotional messages by detecting and tracking phone numbers within user replies. Understanding customer responses that include phone numbers helps refine future campaigns and improve engagement strategies.
Companies can enhance customer profiles by using this functionality to identify phone numbers in communication. By adding this data to existing profiles, businesses can better personalize marketing efforts and improve customer relationship management.
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 a text message 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.