---
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
1. **Input:** Provide a movie title as input.
2. **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:

```python
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())