# Generate embedding outputs = model(**inputs) plot_embedding = outputs.last_hidden_state[:, 0, :] # Take CLS token embedding
# Example plot summary plot_summary = "A modern retelling of the classic Seven Samurai story, set in India." the glorious seven 2019 dual audio hindi mkv upd
from transformers import BertTokenizer, BertModel import torch content features like plot summary embeddings
# Load pre-trained model and tokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') the glorious seven 2019 dual audio hindi mkv upd
# Preprocess text inputs = tokenizer(plot_summary, return_tensors="pt")
# Further processing or use in your application print(plot_embedding.shape) The deep feature for "The Glorious Seven 2019" could involve a combination of metadata, content features like plot summary embeddings, genre vectors, and sentiment analysis outputs. The exact features and their representation depend on the application and requirements. This approach enables a rich, multi-faceted representation of the movie that can be used in various contexts.
# Generate embedding outputs = model(**inputs) plot_embedding = outputs.last_hidden_state[:, 0, :] # Take CLS token embedding
# Example plot summary plot_summary = "A modern retelling of the classic Seven Samurai story, set in India."
from transformers import BertTokenizer, BertModel import torch
# Load pre-trained model and tokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased')
# Preprocess text inputs = tokenizer(plot_summary, return_tensors="pt")
# Further processing or use in your application print(plot_embedding.shape) The deep feature for "The Glorious Seven 2019" could involve a combination of metadata, content features like plot summary embeddings, genre vectors, and sentiment analysis outputs. The exact features and their representation depend on the application and requirements. This approach enables a rich, multi-faceted representation of the movie that can be used in various contexts.