A pretrained if date of birth is in text classifier that sorts text into one of 2 categories. Use the if date of birth 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": "Contains Date Of Birth",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if date of birth 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.
This function can be used by online platforms to verify the age of users by scanning submitted documents or registration forms for a date of birth. Ensuring that users meet age requirements is crucial for compliance in industries such as gambling, alcohol sales, and adult content.
Businesses can leverage this function to identify customer segments based on age derived from the date of birth found in their profiles or interactions. Tailored marketing initiatives can then be developed to appeal to particular age groups, enhancing engagement and conversion rates.
Financial institutions can utilize this identifier to cross-verify the date of birth presented in applications against other submitted information. This preventive measure aids in detecting and mitigating identity theft and fraudulent account openings.
Healthcare providers can implement this function to extract and validate patients’ dates of birth from hand-written notes, referrals, or electronic communications. Accurate age information ensures appropriate treatment plans and age-related health monitoring.
Event organizers can incorporate this text classification to automatically check participants’ ages during registration processes. This helps ensure compliance with age-restricted events, making the registration process more streamlined and reducing manual checks.
In the insurance sector, this identifier can help underwriters automatically pull the date of birth from applicants’ documents for underwriting assessments. By doing so, insurers can more quickly evaluate risk and determine premiums based on age-related factors.
Businesses can use this function during the onboarding phase to verify the dates of birth of new employees as part of identity verification. This ensures compliance with labor laws and internal policies pertaining to age restrictions in the workplace.
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 date of birth 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.