A pretrained if a text contains a modal verb classifier that sorts text into one of 2 categories. Use the if a text contains a modal verb 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 Modal Verb",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a text contains a modal verb 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.
Law firms can utilize a modal verb identifier to parse large volumes of legal texts, contracts, and agreements. Identifying modal verbs can help pinpoint obligations, permissions, and prohibitions within legal documents, enhancing contract review processes and risk assessments.
Businesses can analyze customer feedback and reviews to extract insights about customer sentiment. The presence of modal verbs can indicate uncertainty or possibility, providing deeper context to customer attitudes and helping shape product and service improvements.
Online platforms can use a modal verb identifier to enhance moderation of user-generated content. By detecting modal verbs, systems can flag potentially harmful or uncertain content that may require further manual review, ensuring community safety and compliance with guidelines.
Educational tools can apply modal verb detection to support students and researchers in their writing. By highlighting the use of modal verbs, tools can guide users in conveying appropriate levels of certainty or speculation within their research papers or essays.
Chatbots can be designed to better understand and generate human-like responses by identifying modal verbs in user queries. This capability can help chatbots assess user intent and provide responses that appropriately reflect uncertainty or suggest possibilities, enhancing user experience.
Financial analysts can employ a modal verb identifier to analyze market reports, news articles, and social media posts. The presence of modal verbs can signal trends in market sentiment, indicating where analysts should focus their attention for investment opportunities or risks.
Language learning platforms can implement modal verb recognition to provide tailored feedback to learners. By identifying modal verb usage, the application can help students understand nuances in expressing obligation, permission, or ability, improving their grasp of language fluency.
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 modal verb 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.