A pretrained if a text contains an adverb classifier that sorts text into one of 2 categories. Use the if a text contains an adverb 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_Adverb",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a text contains an adverb 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.
By identifying adverbs in customer reviews, businesses can better gauge the tone and intensity of sentiments expressed. This can help in refining sentiment analysis models, providing insights into customer feelings towards products or services.
Media platforms can use adverb detection to flag potentially harmful or inflammatory content. By focusing on the use of adverbs, moderators can identify extreme language that may indicate problematic posts, enhancing community safety.
Marketing teams can analyze advertising copy for adverb usage to optimize language for effectiveness. Understanding which adverbs impact customer engagement can lead to improved conversion rates in campaigns.
Educational platforms can integrate adverb identification within grammar checking software to enhance learning. This feature can help users recognize incorrect or awkward adverb usage, promoting better writing skills.
In customer service chatbots, identifying adverbs can enhance the understanding of user emotions. This can lead to more empathetic and context-aware responses, improving overall customer satisfaction.
In legal or technical document analysis, detecting adverbs can aid in identifying the primary focus of arguments or points made. This function can contribute to more accurate automatic summarization processes, saving time for users.
Researchers in linguistics can leverage adverb identification to study language patterns and usage in various texts. This can lead to deeper insights into language evolution and adverbial trends across different communication domains.
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 an adverb 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.