A pretrained if a text contains a metaphor classifier that sorts text into one of 2 categories. Use the if a text contains a metaphor 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 Metaphor",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a text contains a metaphor 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 assist authors and editors in analyzing manuscripts by identifying metaphors within the text. By highlighting metaphorical language, writers can refine their style and enhance the imagery in their storytelling, ultimately improving reader engagement.
Marketers can leverage this text classification to evaluate advertising copy and promotional materials. By identifying and analyzing metaphors used, they can ensure that the language resonates with their target audience, making campaigns more effective.
Educators can use this function in literature classes to help students identify and understand metaphors in poetry and prose. It can serve as an interactive tool that encourages deeper analysis and discussion about figurative language and its impact on meaning.
Businesses engaging in sentiment analysis can employ this metaphor identification to enrich understanding of consumer emotions. By recognizing metaphorical language, companies can capture nuanced sentiments that may be overlooked in straightforward text analysis.
Social media platforms can use this tool to analyze user-generated content for the presence of metaphors. By gauging how metaphors are used in discussions about brands or topics, companies can gain insights into public perception and cultural relevance.
Therapists can utilize this function in therapy sessions to identify metaphors in their clients’ narratives, which may reveal underlying emotions or thought patterns. This identification can promote deeper conversations and assist in therapeutic progress.
Linguists researching figurative language can utilize this tool to identify metaphors across different languages and cultures. This can lead to insights into cultural perceptions and values, shedding light on how metaphorical language shapes communication patterns globally.
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 metaphor 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.