A pretrained if date of birth is in a message classifier that sorts text into one of 2 categories. Use the if date of birth is in a 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 Date Of Birth",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if date of birth is in a 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.
This function can be employed by online platforms to verify age restrictions for content or services. By parsing user messages, it can identify if a user meets the minimum age requirement, thereby ensuring compliance with legal regulations.
Businesses can use date of birth identification to segment audiences and tailor marketing messages. By knowing the age of potential customers, companies can create targeted promotions for age-specific products or services.
This feature can enhance customer support chatbots by allowing them to personalize conversations based on the user's age. It can improve customer experience by providing relevant information or services based on their age group.
Retailers can utilize this function to dynamically send birthday promotions to customers via email or messaging platforms. By identifying users' dates of birth, businesses can enhance customer engagement with personalized discounts or offers on their birthday.
Financial institutions can integrate this identifier to validate user identity during account recovery processes. By confirming a user's date of birth in correspondence, it can serve as an additional layer of security against unauthorized access.
Event organizers can leverage this function to send age-appropriate event invitations. For example, they can distinguish between adult and children's events based on the identified dates of birth, leading to a more tailored guest experience.
Healthcare providers can apply this capability to ensure that patients receive appropriate health information and services based on their age. By recognizing patients' ages in messages, providers can offer targeted health advice, screenings, or treatments relevant to specific age groups.
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 a 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.