Feature Extraction
sentence-transformers
Safetensors
Japanese
modernbert
sparse-encoder
splade
sparse
information-retrieval
japanese
learned-sparse-retrieval
text-embeddings-inference
Instructions to use mahiyama/splade-ja-310m-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mahiyama/splade-ja-310m-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mahiyama/splade-ja-310m-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
- Xet hash:
- 9840bddf88a787ad04c024762e4133f6493afd93ef669e5309a9b154c7cb038a
- Size of remote file:
- 1.26 GB
- SHA256:
- 1427959c09890164d20a741fe28b1dd5e3c2f2d05df1b7fea8ec9873bdd9b96f
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