A pretrained if a text contains an adjective classifier that sorts text into one of 2 categories. Use the if a text contains an adjective 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 Adjective",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a text contains an adjective 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 use case utilizes the adjective identifier to enhance sentiment analysis by pinpointing emotion-laden words in customer feedback. By identifying adjectives, businesses can gauge consumer sentiment more effectively, allowing for targeted improvements in products or services based on positive or negative sentiments.
E-commerce platforms can leverage this function to summarize customer reviews by highlighting the most frequent adjectives used. This helps potential buyers quickly understand the quality and features of products based on common descriptors, thus aiding in decision-making.
Content creators can use the adjective identifier to improve the descriptive quality of their articles or blogs. By analyzing adjectives in popular texts, writers can enhance their language and make their content more engaging and visually appealing to the audience.
Marketers can analyze the language of successful campaigns to identify compelling adjectives that resonate with their target audience. By understanding which descriptors drive engagement and conversion, businesses can craft more effective promotional materials.
Customer support teams can utilize this technology to analyze incoming support tickets for triggering adjectives indicating urgency or dissatisfaction. By flagging such communications, teams can prioritize their responses and improve overall customer satisfaction.
Businesses can employ this function to analyze competitor communications, reviews, and advertisements for frequently used adjectives. This data can offer insights into competitors’ positioning and help companies refine their messaging and branding strategies.
Machine learning teams can use the adjective identifier to automatically tag training datasets where adjectives are pivotal in understanding context. This can significantly enhance the efficiency of training models by focusing on relevant linguistic features for natural language processing tasks.
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 adjective 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.