A pretrained if there are footnotes classifier that sorts an image into one of 2 categories. Use the if there are footnotes API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo 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": "https://example.com/photo.jpg"}'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": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "With Footnotes",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if there are footnotes categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file 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.
Automate the review process of legal and financial documents by identifying the presence of footnotes. This functionality helps professionals quickly assess whether a document requires further scrutiny, thereby increasing efficiency and reducing the time spent on manual checks.
Assist researchers in evaluating academic papers by identifying footnotes that may contain critical references and citations. This tool streamlines the literature review process, enabling scholars to focus on the most relevant sources with minimal effort.
Evaluate the quality of written content, such as articles or reports, by checking for footnotes. By determining whether a piece of content includes supporting references or disclaimers, businesses can ensure they uphold high standards of credibility and accuracy.
Enhance the e-discovery process in legal cases by identifying documents with footnotes that may imply additional context or hidden facts. This capability accelerates the identification of relevant materials and helps attorneys build stronger cases based on thorough evidence.
Support publishers in preparing manuscripts for publication by identifying and managing footnotes. By ensuring that all footnotes are present and properly formatted, the tool reduces errors in the final publication and improves overall production quality.
Facilitate compliance audits in regulated industries by identifying documents with footnotes that provide necessary regulations or legal disclaimers. This information assists compliance officers in ensuring that all required disclosures are present, minimizing the risk of penalties.
Aid marketing teams in evaluating promotional content by identifying footnotes that contain important disclaimers or legal information. This identification helps ensure that all marketing materials comply with advertising standards and legal requirements, thereby protecting the company’s reputation.
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 images 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 there are footnotes 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.