A pretrained citation style classifier that sorts text into one of 10 categories — the appropriate citation style for the given reference.. Use the citation style 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 26 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": "Acs",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 citation style 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 academic institutions to automate the process of identifying and categorizing citation styles in research papers. By processing submissions, the system can ensure adherence to specific formatting guidelines, facilitating smoother peer reviews and publication processes.
Publishers and online content platforms can integrate this function to tag documents with appropriate citation styles. This helps maintain consistency across their repositories and enhances the user experience by allowing easy filtering of documents based on citation formats.
Educational technology companies can implement this function in their plagiarism detection software. By correctly identifying the citation styles used, the tool can provide more accurate feedback and suggestions for proper citation practices to enhance academic integrity.
Developers of reference management tools can utilize this function to improve the automatic detection of citation styles in user-generated content. This enables seamless integration of various citation formats, making it easier for users to manage their bibliographies efficiently.
Tools designed for collaborative writing can integrate this function to help teams identify and standardize citation styles in real time. This ensures that all contributors are following the same guidelines, reducing confusion and enhancing the quality of the final document.
Law firms can benefit from this function by streamlining the review of legal documents that require specific citation formats. By automatically identifying the citation styles used, legal professionals can ensure compliance with court requirements and improve document accuracy.
Educational technology providers can use this function in assessment systems to evaluate students' understanding of citation practices. By identifying the citation styles used in student submissions, teachers can provide targeted feedback and enhance teaching methods around proper citation practices.
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 citation style 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.