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yt-strategic-intent-distilbert – AI Model by mr-checker | AlphaNeural AI

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mr-checker/
yt-strategic-intent-distilbert

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text-classification

intent-detection

mr-checker/yt-strategic-intent-9k

distilbert/distilbert-base-uncased

text-embeddings-inference

endpoints_compatible

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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.

image

Example Usage

python

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