A pretrained if a text contains a proper noun classifier that sorts text into one of 2 categories. Use the if a text contains a proper noun 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 Proper Noun",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a text contains a proper noun 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 utilized to identify proper nouns in legal documents, such as names of individuals, organizations, and locations. By extracting these entities, law firms can efficiently organize case-related information and ensure compliance with legal standards.
Marketing teams can leverage this identifier to track mentions of their brand names or competitors in social media and online content. This allows for timely responses to brand sentiment and competitive analysis, enhancing brand reputation management.
E-commerce platforms can use this function to identify proper nouns in customer reviews and feedback. By recognizing specific products and brands mentioned, businesses can tailor recommendations and improve customer satisfaction based on user-generated content.
News aggregation services can apply this text classification function to filter articles by key figures, entities, or events. This enables them to perform sentiment analysis on specific proper nouns, helping users stay informed about trending topics and public opinion.
Automated chatbots can utilize proper noun identification to enhance customer interactions by recognizing the names of products or services customers mention. This contextual understanding allows for more accurate responses, improving customer experience in support scenarios.
Researchers can use this identifier to streamline the process of analyzing academic papers by extracting proper nouns such as author names, institutions, and research topics. This facilitates citation tracking and helps researchers locate relevant literature more efficiently.
SEO specialists can employ this function to analyze web content for proper nouns related to their target keywords. By understanding how often proper nouns are used and in what context, they can optimize content for better search rankings and organic traffic.
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 a text contains a proper noun 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.