README.md

---
library_name: transformers
tags:
- bert
- youtube
- classification
license: apache-2.0
language:
- en
---

# 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](https://huggingface.co/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]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- This model can be directly used to classify YouTube video titles and descriptions into predefined categories: Education, Technology, Motivation, Entertainment, and Gaming.

Example use cases:

Automatically tagging videos in content moderation systems

Enabling smart filtering and recommendations

Analyzing category distribution of YouTube channels -->

### Direct Use
<!-- python

from transformers import BertTokenizer, BertForSequenceClassification

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

inputs = tokenizer("This video is about personal productivity hacks", return_tensors="pt")
outputs = model(**inputs)
predicted = outputs.logits.argmax(dim=1).item()```

-->

### 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]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** <!Base model: bert-base-uncased Epochs: 4 Batch size: 16 Learning rate: 2e-5 Optimizer: AdamW -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## 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

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### 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]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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