A pretrained identification number classifier that sorts text into one of 10 categories — what identification number it belongs to. Use the identification number 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 16 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": "Alphanumeric Format",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 identification number 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 to automatically identify and validate identification numbers within legal documents, ensuring that the documents meet required compliance standards. By efficiently flagging any discrepancies, organizations can streamline their verification process and minimize the risk of fraud.
Financial institutions could integrate this function into their onboarding systems to quickly validate customer identification numbers during account opening. This would enhance the user experience by reducing waiting times and automatically alerting staff to any potential issues that need to be addressed.
In medical settings, this identification number identifier can help confirm patient identities against their health records. By ensuring accurate matching of identification numbers, healthcare providers can improve the quality of care and reduce the potential for errors in patient data management.
Online retailers can utilize this function to check user-submitted identification numbers during transactions. By identifying and flagging incorrect or suspicious identification numbers, businesses can significantly reduce the incidence of fraud and chargebacks.
Insurance companies could leverage this function to monitor claims and policies for any unusual patterns in identification numbers. Early detection of anomalies can help in preventing identity theft and protecting policyholders.
Public sector agencies could employ this function to verify identification numbers when processing applications for various services, such as permits or licenses. This process would improve efficiency by ensuring that only valid requests are processed, reducing backlog and enhancing service delivery.
Companies can integrate the identification number validation functionality into their data management systems to ensure the accuracy and integrity of customer databases. By routinely checking for incorrect identification numbers, organizations can maintain high-quality data, which is crucial for targeted marketing and analytics efforts.
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 identification number 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.