Model Card for Model ID

This is a fine-tuned BERT model that classifies YouTube channels content into categories such as Education, Technology, Finance, and more.

Model Details

Model Description

This is a fine-tuned BERT-based classification model designed to categorize YouTube video metadata—specifically titles, descriptions into one of categories**:

Education
Technology
Motivation
Entertainment
Gaming

The model is based on the bert-base-uncased architecture from the Hugging Face Transformers library and was fine-tuned using a labeled dataset of YouTube content. It is optimized for short text classification, making it ideal for content analytics, recommendation systems, and media monitoring tools focused on YouTube.

Highlights

🧠 Model type: BERT (Transformer-based)
🔠 Input: Raw text (title + optional description)
🎯 Task: Multi-class classification
🏷️ Classes: 20 categories Such as Gaming,Technology,Finance etc
📦 Pretrained Base: bert-base-uncased
💡 Use Case: YouTube video categorization, content recommendation, channel analysis

Let me know if you also want a short version or something more technical for the model-index or metadata fields.

Developed by: Jayesh Mehta
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: BERT-based sequence classification model
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model [optional]: [More Information Needed]

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Uses

Direct Use

Downstream Use [optional]

This model can be integrated into larger systems, such as:

Content management systems

YouTube channel analytics tools

Personalized recommendation engines

Out-of-Scope Use

The model is not suitable for long-form text or transcript-level classification.

Should not be used to classify non-YouTube content or languages other than English.

Avoid using it in sensitive decision-making scenarios (e.g., legal, medical).

Bias, Risks, and Limitations

Like most models trained on public or scraped data:

The model may carry biases from the underlying data (e.g., overrepresentation of certain video types).

It may misclassify mixed-genre or ambiguous titles (e.g., “Top 10 Gaming Laptops for Students”).

It is sensitive to text length and clarity—very short or vague titles may reduce accuracy.

Recommendations

Use the model as an assistive tool, not a final decision-maker.

Evaluate its performance on your specific data before deploying.

Consider adding user feedback or manual review in production systems.

How to Get Started with the Model

from transformers import BertTokenizer, BertForSequenceClassification

model = BertForSequenceClassification.from_pretrained("JaySenpai/bert-model") tokenizer = BertTokenizer.from_pretrained("JaySenpai/bert-model")

text = "10 Tips to Grow Your YouTube Channel" inputs = tokenizer(text, return_tensors="pt") outputs = model(**inputs) prediction = outputs.logits.argmax(dim=1).item()

labels = {0: "Education", 1: "Comedy and Humour", 2: "Gaming", 3: "Technology", 4: "Motivation"} print("Predicted label:", labels[prediction])

Training Details

Training Data

Training Data The model was fine-tuned using a labeled dataset of YouTube titles and descriptions, mapped to categories:

Education
Travel
Cooking
Gaming
Music
Health and Fitness
Finance
Technology
Vlogging
Beauty & Fashion
Digital Marketing
Movies/Series Reviews
Comedy and Humour
Podcast
Youtube or Instagram Grow Tips
Online Income
ASMR
Business and Marketing
News
Motivation

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training regime:

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Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was evaluated on a held-out validation set of manually labeled YouTube titles and descriptions.

Factors

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Metrics

Accuracy: ~97%

F1-score (macro): ~0.95

Results

The model performed well on clear-cut categories like "Gaming" and "Technology" but showed confusion between "Motivation" and "Education" in edge cases.

Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Model Card Contact

Author: Jayesh Mehta(JaySenpai) Hugging Face: @JaySenpai