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| library_name: transformers |
| tags: |
| - bert |
| - youtube |
| - classification |
| license: apache-2.0 |
| language: |
| - en |
| --- |
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| # 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. |
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| ## Model Details |
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| ### Model Description |
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| This is a fine-tuned BERT-based classification model designed to categorize **YouTube video metadata**—specifically titles, descriptions into one of categories**: |
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| * **Education** |
| * **Technology** |
| * **Motivation** |
| * **Entertainment** |
| * **Gaming** |
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| 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. |
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| ### Highlights |
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| * 🧠 **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 |
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| Let me know if you also want a short version or something more technical for the `model-index` or metadata fields. |
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| - **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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| ### Model Sources [optional] |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** [More Information Needed] |
| - **Paper [optional]:** [More Information Needed] |
| - **Demo [optional]:** [More Information Needed] |
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| ## Uses |
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| <!-- This model can be directly used to classify YouTube video titles and descriptions into predefined categories: Education, Technology, Motivation, Entertainment, and Gaming. |
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| Example use cases: |
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| Automatically tagging videos in content moderation systems |
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| Enabling smart filtering and recommendations |
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| Analyzing category distribution of YouTube channels --> |
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| ### Direct Use |
| <!-- python |
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| from transformers import BertTokenizer, BertForSequenceClassification |
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| model = BertForSequenceClassification.from_pretrained("JaySenpai/bert-youtube-model") |
| tokenizer = BertTokenizer.from_pretrained("JaySenpai/bert-youtube-model") |
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| inputs = tokenizer("This video is about personal productivity hacks", return_tensors="pt") |
| outputs = model(**inputs) |
| predicted = outputs.logits.argmax(dim=1).item()``` |
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| --> |
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| ### Downstream Use [optional] |
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| This model can be integrated into larger systems, such as: |
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| Content management systems |
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| YouTube channel analytics tools |
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| Personalized recommendation engines |
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| ### Out-of-Scope Use |
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| The model is not suitable for long-form text or transcript-level classification. |
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| Should not be used to classify non-YouTube content or languages other than English. |
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| Avoid using it in sensitive decision-making scenarios (e.g., legal, medical). |
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| ## Bias, Risks, and Limitations |
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| Like most models trained on public or scraped data: |
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| The model may carry biases from the underlying data (e.g., overrepresentation of certain video types). |
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| It may misclassify mixed-genre or ambiguous titles (e.g., “Top 10 Gaming Laptops for Students”). |
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| It is sensitive to text length and clarity—very short or vague titles may reduce accuracy. |
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| ### Recommendations |
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| Use the model as an assistive tool, not a final decision-maker. |
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| Evaluate its performance on your specific data before deploying. |
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| Consider adding user feedback or manual review in production systems. |
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| ## How to Get Started with the Model |
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| from transformers import BertTokenizer, BertForSequenceClassification |
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| model = BertForSequenceClassification.from_pretrained("JaySenpai/bert-model") |
| tokenizer = BertTokenizer.from_pretrained("JaySenpai/bert-model") |
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| text = "10 Tips to Grow Your YouTube Channel" |
| inputs = tokenizer(text, return_tensors="pt") |
| outputs = model(**inputs) |
| prediction = outputs.logits.argmax(dim=1).item() |
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| labels = {0: "Education", 1: "Comedy and Humour", 2: "Gaming", 3: "Technology", 4: "Motivation"} |
| print("Predicted label:", labels[prediction]) |
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| ## Training Details |
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| ### Training Data |
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| Training Data |
| The model was fine-tuned using a labeled dataset of YouTube titles and descriptions, mapped to categories: |
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| 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 |
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| ### Training Procedure |
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| #### Preprocessing [optional] |
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| [More Information Needed] |
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| #### Training Hyperparameters |
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| - **Training regime:** <!Base model: bert-base-uncased Epochs: 4 Batch size: 16 Learning rate: 2e-5 Optimizer: AdamW --> |
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| #### Speeds, Sizes, Times [optional] |
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| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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| [More Information Needed] |
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| ## Evaluation |
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| ### Testing Data, Factors & Metrics |
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| #### Testing Data |
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| The model was evaluated on a held-out validation set of manually labeled YouTube titles and descriptions. |
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| #### Factors |
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| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| [More Information Needed] |
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| #### Metrics |
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| Accuracy: ~97% |
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| F1-score (macro): ~0.95 |
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| ### Results |
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| The model performed well on clear-cut categories like "Gaming" and "Technology" but showed confusion between "Motivation" and "Education" in edge cases. |
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| #### Summary |
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| ## Model Examination [optional] |
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| <!-- Relevant interpretability work for the model goes here --> |
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| [More Information Needed] |
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| ## Environmental Impact |
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| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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| 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). |
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| - **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] |
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| ## Technical Specifications [optional] |
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| ### Model Architecture and Objective |
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| [More Information Needed] |
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| ### Compute Infrastructure |
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| [More Information Needed] |
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| #### Hardware |
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| [More Information Needed] |
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| #### Software |
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| [More Information Needed] |
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| ## Citation [optional] |
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| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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| **BibTeX:** |
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| [More Information Needed] |
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| **APA:** |
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| [More Information Needed] |
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| ## Glossary [optional] |
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| <!-- 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 Needed] |
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| ## More Information [optional] |
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| [More Information Needed] |
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| ## Model Card Authors [optional] |
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| [More Information Needed] |
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| ## Model Card Contact |
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| Author: Jayesh Mehta(JaySenpai) |
| Hugging Face: @JaySenpai |
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