sentence-transformers
Safetensors
bert
retrieval
movie-recommendation
semantic-search
Eval Results (legacy)
Instructions to use JJTsao/fine-tuned_movie_retriever-all-minilm-l6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JJTsao/fine-tuned_movie_retriever-all-minilm-l6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JJTsao/fine-tuned_movie_retriever-all-minilm-l6-v2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -59,7 +59,7 @@ This is a custom fine-tuned sentence-transformer model designed for movie and TV
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| Recall@3 | 0.657 | 0.258 |
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| Recall@5 | 0.720 | 0.309 |
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| Recall@10 | 0.795 | 0.382 |
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**Evaluation setup**:
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- Dataset: 3,600 held-out metadata and vibe-style natural queries
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| Recall@3 | 0.657 | 0.258 |
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| Recall@5 | 0.720 | 0.309 |
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| Recall@10 | 0.795 | 0.382 |
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| MRR | 0.563 | 0.230 |
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**Evaluation setup**:
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- Dataset: 3,600 held-out metadata and vibe-style natural queries
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