Model Card: yt-strategic-intent-transformer
Model Details
- Model Name:
yt-strategic-intent-transformer - Task: Text Classification
- Framework: Hugging Face Transformers
- Library: PyTorch
- License: Apache 2.0
This model predicts strategic audience intent behind YouTube-related content. It answers the question: “What does the audience want?”
Intended Use
- Audience Insight: Identify strategic motivations behind content engagement.
- Content Strategy: Help creators and brands align delivery with audience intent.
- Market Research: Track evolving audience desires across categories.
- Business Intelligence: Map content to strategic drivers for decision-making.
Labels: Strategic Intent
- Build
- Learn
- Career
- Evaluate
- Awareness
- Decide
- Inspiration
- Predict
Training Data
- Source: yt-strategic-intent-9k
- Size: ~10,000 rows.
- Language: English (
en). - Preprocessing: Tokenization with DistilBERT tokenizer, balanced sampling across categories.
Evaluation
- Metrics: Accuracy, F1-score,
- Validation Strategy: Stratified train/validation split.
Example Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="mr-checker/yt-strategic-intent-distilbert")
text = "How To Create AI UGC Ads (Full Tutorial)"
output = classifier(text)
print(output)
Limitations
- Supports only English-language inputs.
- Predictions depend on text quality (titles, transcripts, metadata).
- Does not yet incorporate multimodal signals (video thumbnails, audio tone).
- may misbehave sometimes on real data due to data quality