Fine-tuned distilbert-base-uncased that classifies YouTube comments from fitness influencer videos into 5 categories: fitness, nutrition, motivational, challenge, product.
Quick Start
python
1from transformers import pipeline
23classifier = pipeline(4'text-classification',5 model='Krat6s/fitness-comment-classifier'6)78classifier("This protein shake changed my life, amazing with oat milk")9# [{'label': 'nutrition', 'score': 0.956}]1011classifier("Day 7 of the squat challenge complete 🔥")12# [{'label': 'challenge', 'score': 0.508}]
Model Description
Base model:distilbert-base-uncased (66M parameters)
Task: Multi-class text classification (5 classes)
Domain: YouTube comments from fitness influencer channels
Language: English (non-English comments present in dataset but not handled)
Dataset
Self-scraped YouTube comments collected via the YouTube Data API v3 for MSc dissertation research on fitness influencer sentiment and thematic analysis.
Total dataset size: 92,223 comments
YouTubers: 94 fitness influencer channels
Top channels: Noel Deyzel, Browney, Jeff Nippard, Renaissance Periodization, ATHLEAN-X
Trained two versions to measure the effect of data volume:
Model
Training Data
Accuracy
F1
DistilBERT
5,000 rows
53.6%
53.8%
DistilBERT
20,000 rows
60.4%
60.4%
4x more data produced +6.8pp accuracy improvement with linear increase in training time (81s → 327s).
Inference Examples
Comment
Predicted
Confidence
"This protein shake recipe changed my life, tastes amazing with oat milk"
nutrition
95.6%
"I've been doing this workout for 30 days and I can see abs forming!"
fitness
93.1%
"Never give up on your dreams, the grind is worth it"
motivational
79.8%
"Is this pre-workout worth buying? I've heard mixed reviews"
product
81.4%
"Day 7 of the squat challenge complete 🔥"
challenge
50.8%
Limitations
Challenge/motivational confusion is the largest source of error (136 challenge comments predicted as motivational in the 20K test set). Both classes share workout encouragement language. Without video context, the boundary is inherently ambiguous — this is a label ambiguity issue, not a data quantity issue. More training data did not reduce this confusion proportionally.
Product/nutrition overlap is the second largest error pattern (64 nutrition comments predicted as product). Supplement and protein content sits on the boundary between these two classes.
Non-English comments default to incorrect predictions. Approximately 15% of the full dataset contains non-English comments (Russian, German, Spanish, Korean). VADER assigned these neutral scores in the original dissertation — DistilBERT similarly has no signal for them.
Product class underrepresentation — product has roughly half the examples of other classes in the full dataset. Despite this, product achieved competitive F1 (0.56) due to distinctive brand/review vocabulary.
Next Steps
Fine-tune roberta-base (125M parameters) on 20K rows — model size comparison
YouTuber-stratified train/test split — test generalisation to unseen creators
Sentiment classification using human-labelled subset replacing VADER baseline
Citation
If you use this model or dataset, please credit:
Dataset: Self-scraped YouTube comments from 94 fitness influencer channels
Collected via YouTube Data API v3 for MSc dissertation research
HuggingFace: Krat6s/fitness-youtube-comments
