A pretrained a resume's average job tenure classifier that sorts text into one of 10 categories — the average job tenure for different positions in a resume.. Use the a resume's average job tenure 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 31 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": "1 Year",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 a resume's average job tenure 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 help recruiters quickly assess the stability of a candidate's job history by identifying average job tenures. By filtering out candidates with extremely short tenures, recruiters can focus on more stable candidates who are likely to be a better fit for long-term roles.
Organizations can analyze the average job tenure of candidates to develop targeted acquisition strategies. For instance, if the data indicates shorter tenures in specific industries, companies can adjust their offerings or culture to attract more stable talent.
HR teams can utilize this function to derive insights into employee retention and stability trends across departments. By analyzing average job tenures, they can identify patterns that may indicate potential retention challenges and take proactive measures.
Employers can use average job tenure data to determine salary competitiveness. A candidate with longer tenures may command a higher salary, and understanding these trends can assist companies in balancing budgets while remaining attractive to talent.
Managers can leverage average job tenure data to refine performance metrics and career development programs. If employees typically stay for shorter periods, it may indicate the need for focused mentorship or clearer progression pathways to enhance engagement.
Companies can apply this function to improve their hiring predictability models. By assessing the average job tenure of new hires historically, organizations can better forecast employment durations and adjust hiring plans accordingly.
This function can be instrumental in designing employee engagement initiatives. By understanding the average job tenure, HR can tailor programs aimed at enhancing job satisfaction and retention, specifically targeting groups likely to leave earlier.
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 a resume's average job tenure 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.