A pretrained if password is in a text message classifier that sorts text into one of 2 categories. Use the if password 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 Password",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if password 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.
This function can be used in financial institutions to monitor text messages for potential phishing attempts. By identifying messages containing passwords, banks can alert customers and take preventive measures against unauthorized transactions.
Human Resource departments can utilize this identifier to ensure employees do not share sensitive credentials through company communication channels. This helps to maintain compliance with data protection regulations and ensures the security of employee information.
Customer support chatbots can incorporate this function to flag messages that contain passwords. By doing so, they can automatically prompt users to refrain from sharing sensitive information, thus enhancing security in customer interactions.
Social media platforms can deploy this identifier to monitor user messages for shared passwords. By doing this, they can proactively prevent security breaches and protect users from account compromise.
E-commerce platforms can integrate this function to scan communications for shared passwords related to user accounts. By detecting such occurrences, these platforms can educate customers on secure practices and prevent account takeovers.
Schools and universities can use this text classification to monitor student communications for shared passwords. Ensuring students do not exchange sensitive login details can help maintain the integrity of educational resources and personal data.
Organizations can implement this feature in their data loss prevention (DLP) strategies to identify potential breaches through messaging platforms. By flagging any communications that contain passwords, they can take immediate action to protect sensitive information and mitigate risks.
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 password 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.