| --- |
| language: |
| - en |
| license: apache-2.0 |
| library_name: transformers |
| pipeline_tag: text-classification |
| base_model: distilbert/distilbert-base-uncased |
| tags: |
| - distilbert |
| - netflix |
| - text-classification |
| - movie |
| - tv-show |
| --- |
| |
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| # Netflix Movie-TV Show Classifier |
|
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| ## Model Description |
|
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| This project uses a fine-tuned DistilBERT model to classify Netflix content as either a Movie or a TV Show. |
|
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| ## Base Model |
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| distilbert-base-uncased |
|
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| ## Dataset |
|
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| Netflix Titles Dataset (`netflix_titles.csv`) |
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| The dataset contains information about Netflix movies and TV shows, including: |
|
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| - Title |
| - Description |
| - Genre |
| - Director |
| - Cast |
| - Country |
| - Release Year |
| - Rating |
| - Type |
|
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| ## Model Input |
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| The model uses: |
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| Title + Description + Genre |
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| Example: |
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| "The Lost Kingdom. A young warrior travels across an ancient kingdom to rescue his family. Action, Adventure." |
|
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| ## Output Classes |
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| - Movie |
| - TV Show |
|
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| ## Fine-Tuning |
|
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| The pre-trained DistilBERT model was fine-tuned using the Netflix dataset for binary text classification. |
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| Training settings: |
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| - Epochs: 3 |
| - Learning Rate: 2e-5 |
| - Batch Size: 16 |
| - Maximum Sequence Length: 256 |
| - Optimizer: AdamW |
|
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| ## Prediction Example |
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| Input: |
|
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| Title: The Lost Kingdom |
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| Description: A young warrior travels across an ancient kingdom to rescue his family and defeat a powerful enemy. |
|
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| Genre: Action, Adventure |
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| Output: |
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| Prediction: Movie |
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| ## Evaluation |
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| The model is evaluated using: |
|
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| - Accuracy |
| - Precision |
| - Recall |
| - F1-score |
|
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| ## Technology Used |
|
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| - Python |
| - PyTorch |
| - Hugging Face Transformers |
| - DistilBERT |
| - Pandas |
| - Scikit-learn |
| - Google Colab |
|
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| ## Purpose |
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| The purpose of this project is to demonstrate how a pre-trained transformer model can be fine-tuned on a custom Netflix dataset for text classification. |
