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