mr-checker/yt-strategic-intent-distilbert

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