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- added bert_embeddings.pkl, sentence_transformer_embeddings.pkl and user input examples

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jan-kordas 2025-01-02 23:17:25 +01:00
parent 3d4e031cf4
commit 12aa28d0b0
4 changed files with 13 additions and 3 deletions
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data/bert_embeddings.pkl
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data/input_examples Normal file
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A heartwarming tale of friendship and adventure: Follow the journey of a young boy and his loyal dog as they navigate through the challenges of growing up in a small town. Together, they discover the true meaning of loyalty and courage.
A gripping sci-fi thriller: In a dystopian future, a group of rebels fights against a tyrannical regime that controls the world through advanced technology. As they uncover dark secrets, they must decide whether to risk everything for freedom.
A romantic comedy with a twist: When a successful career woman accidentally swaps lives with a struggling artist, they both learn valuable lessons about love, ambition, and what truly matters in life. Sparks fly as they navigate their new realities.
An epic fantasy adventure: In a magical realm, a young sorcerer embarks on a quest to save his kingdom from an ancient evil. Along the way, he forms unlikely alliances and discovers his true destiny.
A chilling horror story: A group of friends decides to spend the weekend in an abandoned mansion, only to find themselves haunted by the spirits of its tragic past. As they uncover the house's dark history, they must confront their own fears to survive.

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data/sentence_transformer_embeddings.pkl
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src/model/generate_embeddings.py
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@ -55,8 +55,9 @@ data_path = "../../data/preprocessed_data.csv"
bert_embeddings_path = "../../data/bert_embeddings.pkl"
sentence_transformer_embeddings_path = "../../data/sentence_transformer_embeddings.pkl"
print("Generating embeddings with BERT...")
#generate_and_save_embeddings(data_path, bert_embeddings_path, model_name='bert-base-uncased')
print("Generating embeddings with lightweight model all-MiniLM-L6-v2 ...")
lightweight_generate_and_save_embeddings(data_path, sentence_transformer_embeddings_path, model_name='all-MiniLM-L6-v2')
print("Generating embeddings with BERT...")
generate_and_save_embeddings(data_path, bert_embeddings_path, model_name='bert-base-uncased')