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license: apache-2.0
tags:
- retrieval
- movie-recommendation
- sentence-transformers
- semantic-search
- feature-extraction
- loss:MultipleNegativesRankingLoss
library_name: sentence-transformers
model-index:
- name: fine-tuned movie retriever
results:
- task:
type: retrieval
name: Information Retrieval
metrics:
- name: Recall@1
type: recall
value: 0.456
- name: Recall@3
type: recall
value: 0.693
- name: Recall@5
type: recall
value: 0.758
- name: Recall@10
type: recall
value: 0.836
metrics:
- recall
base_model:
- BAAI/bge-base-en-v1.5
---
# 🎬 Fine-Tuned Movie Retriever (Rich Semantic & Metadata Queries + Smart Negatives)
[](https://huggingface.co/your-username/my-st-model)
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 | 0.456 | 0.214 |
| Recall@3 | 0.693 | 0.361 |
| Recall@5 | 0.758 | 0.422 |
| Recall@10 | 0.836 | 0.500 |
| MRR | 0.595 | 0.315 |
**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-bge-base-en-v1.5")
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.
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