⏱️SlopMeter
Paste your LinkedIn bio. Find out how much it says about nothing.
A fine-tuned distilbert-base-uncased that labels text USEFUL or SLOP, plus a small rule-based script that insults you about it. Trained on about 6,000 examples: tech tutorials, real job descriptions, and a lot of corporate cringe.
Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="QuantaSparkLabs/BioSlopMeter")
text = "I am a passionate, results-driven visionary leveraging cutting-edge AI to drive transformative growth."
print(classifier(text))
# [{'label': 'SLOP', 'score': 0.9998}]
Examples
Input: "I am thrilled to announce that I have accepted a new position as Chief Synergy Officer! Let's leverage our collective potential to drive transformative growth."
Verdict: SLOP (98%) Buzzwords: leverage, synergy, transformative Roast: You sound like a walking press release. This is vague enough to describe a bakery or a spaceship.
Input: "Compress images before uploading. Use WebP format. Enable browser caching."
Verdict: USEFUL (8%) Roast: Actually useful. Weird. Take my upvote.
How it works
DistilBERT does the classification. A few regex checks count buzzwords, numbers/tools mentioned, and action verbs. If the buzzword score is high and the action score is low, you get a random roast from a list.
About the numbers
Validation loss is 0.000007. Yes, that's suspiciously low. The training data is easy to separate, so please don't treat this as a real benchmark. It will probably still roast your bio correctly, though.
⚠️Disclaimer
If your bio gets a 100% slop score, please don't send me a connection request. I won't accept it (I don't even have a profile).
Built in one evening because I wanted to roast LinkedIn bios without logging in. 🥀
Experimental Model-2026
- Downloads last month
- 1
Model tree for QuantaSparkLabs/BioSlopMeter
Base model
distilbert/distilbert-base-uncased