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--- |
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tags: |
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- recommender |
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- movie |
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- imdb |
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language: eng |
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datasets: imdb |
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license: apache-2.0 |
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library_name: your_library_name |
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pipeline_tag: text-classification |
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--- |
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## Model Card |
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### Model Description |
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This model is a movie recommender system trained on IMDB movie data. It provides movie recommendations based on cosine similarity of text features extracted from movie titles and other attributes. |
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### Intended Use |
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- **Recommendation:** The model is designed to recommend movies based on a given movie title. It provides a list of similar movies from the IMDB dataset. |
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### How to Use |
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1. **Input:** Provide a movie title as input. |
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2. **Output:** The model returns a list of recommended movies based on similarity. |
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### Model Details |
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- **Training Data:** The model was trained on a dataset of IMDB movies including movie titles, genres, and other attributes. |
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- **Features:** The model uses text features extracted from movie titles and additional metadata such as genres and certificates. |
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### Example |
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To get recommendations, you can use the following code snippet: |
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```python |
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import requests |
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model_name = 'Gaurav2k/IMDB_Recommender' |
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api_url = f'https://api-inference.huggingface.co/models/{model_name}' |
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headers = { |
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'Authorization': f'Bearer your_token' |
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} |
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data = { |
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'inputs': 'The Godfather' |
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} |
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response = requests.post(api_url, headers=headers, json=data) |
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print(response.json()) |
