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