Feature Extraction
sentence-transformers
Safetensors
bert
retrieval
movie-recommendation
semantic-search
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use JJTsao/fine-tuned_movie_retriever-bge-base-en-v1.5-fp16 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-bge-base-en-v1.5-fp16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JJTsao/fine-tuned_movie_retriever-bge-base-en-v1.5-fp16") 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
- Xet hash:
- f3bdd0ab68a49ba6efe0be7136b03cd763c9acbfdd143ff1f6550b01d674857d
- Size of remote file:
- 219 MB
- SHA256:
- 242d13255df431d300bec7f706613f6607075ea91c436cf2d148b874508ba3ae
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