A pretrained if a text contains a quotation mark classifier that sorts text into one of 2 categories. Use the if a text contains a quotation mark 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 Quotation Mark",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a text contains a quotation mark 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.
In online forums and social media platforms, identifying quotes can help moderators quickly assess user-generated content. By flagging text that contains quotation marks, moderators can efficiently review discussions for policy violations or misinformation that is often encased in quoted material.
Businesses can utilize text classification to analyze customer feedback and reviews that include specific quotes. By isolating mentions in quotation marks, data scientists can better understand sentiments and themes in customer opinions that may directly impact brand perception.
In qualitative research, quotes from participants are crucial pieces of data. By identifying text with quotation marks, researchers can efficiently compile and analyze participant insights, enabling them to derive actionable strategies or improve product offerings based on direct customer feedback.
Legal professionals can employ this classification function to scan through large volumes of documents for quoted statements, such as depositions or witness accounts. This helps streamline the review process by efficiently pinpointing critical evidence and testimonies that may be relevant to ongoing cases.
In academic writing and literature reviews, researchers often quote existing studies. This text classification allows scholars to easily identify and extract relevant citations from various documents, ensuring accurate referencing and proper integration of scholarly discourse.
Brands can analyze social media conversations for direct quotes that customers mention in their posts. By identifying these quotes, companies can engage with their audience more effectively, tailoring responses based on actual sentiments expressed in the conversations.
Chatbots can improve user experience by recognizing when users provide feedback or ask questions that include direct quotes. By identifying these key phrases, chatbots can offer more contextual and relevant responses, ultimately leading to more satisfying user interactions.
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 quotation mark 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.