Sentence Similarity
sentence-transformers
Japanese
feature-extraction
dense
Generated from Trainer
dataset_size:15098874
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-ja with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-ja") 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
Download modules.json from RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-ja: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-ja/resolve/main/modules.json
- Command line
-
hf download hf://RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-ja/modules.json
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curl -L -o modules.json https://huggingface.co/RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-ja/resolve/main/modules.json
97 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "SSE_quantize.SSEQ" | |
| } | |
| ] |