This is a custom fine-tuned sentence-transformer model designed for movie and TV recommendation systems. Optimized for high-quality vector retrieval in a movie and TV show recommendation RAG pipeline. Fine-tuning was done using ~32K synthetic natural language queries across metadata and vibe-based prompts:

Enriched vibe-style natural language queries (e.g., Emotionally powerful space exploration film with themes of love and sacrifice.)
Metadata-based natural language queries (e.g., Any crime movies from the 1990s directed by Quentin Tarantino about heist?)
Smarter negative sampling (genre contrast, theme mismatch, star-topic confusion)
A dataset of over 32,000 triplets (query, positive doc, negative doc)

🧠 Training Details

Base model: BAAI/bge-base-en-v1.5
Loss function: MultipleNegativesRankingLoss
Epochs: 4
Optimized for: top-k semantic retrieval in RAG systems

📈 Evaluation: Fine-tuned vs Base Model

Metric	Fine-Tuned Model Score	Base Model Score
Recall@1	0.459	0.159
Recall@3	0.695	0.289
Recall@5	0.765	0.342
Recall@10	0.836	0.425
MRR	0.598	0.251

Evaluation setup:

Dataset: 3,598 held-out metadata and vibe-style natural queries
Method: Top-k ranking using cosine similarity between query and positive documents
Goal: Assess top-k retrieval quality in recommendation-like settings

📦 Usage

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("jjtsao/fine-tuned_movie_retriever-all-mpnet-base-v2")
query_embedding = model.encode("mind-bending sci-fi thrillers from the 2000s about identity")

🔍 Ideal Use Cases

RAG-style movie recommendation apps
Semantic filtering of large movie catalogs
Query-document reranking pipelines

📜 License

Apache 2.0 — open for personal and commercial use.

JJTsao/fine-tuned_movie_retriever-all-mpnet-base-v2

🎬 Fine-Tuned Movie Retriever (Rich Semantic & Metadata Queries + Smart Negatives)

🧠 Training Details

📈 Evaluation: Fine-tuned vs Base Model

📦 Usage

🔍 Ideal Use Cases

📜 License

JJTsao/
fine-tuned_movie_retriever-all-mpnet-base-v2