Feature Extraction
sentence-transformers
Safetensors
Transformers
Japanese
llama
sentence-similarity
text-embeddings-inference
Instructions to use sbintuitions/sarashina-embedding-v2-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sbintuitions/sarashina-embedding-v2-1b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sbintuitions/sarashina-embedding-v2-1b") 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] - Transformers
How to use sbintuitions/sarashina-embedding-v2-1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sbintuitions/sarashina-embedding-v2-1b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina-embedding-v2-1b") model = AutoModel.from_pretrained("sbintuitions/sarashina-embedding-v2-1b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB)による評価 (*)
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|Model|Avg.|Retrieval|STS|
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|Sarashina-Embedding-v2-1B (This model)|**76.38**|**76.48**|**84.22**|77.14|**86.28**|52.56|
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|[cl-nagoya/ruri-v3-310m](https://huggingface.co/cl-nagoya/ruri-v3-310m)|75.85|76.03|81.59|**77.65**|85.84|50.52|
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## [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB)による評価 (*)
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|Model|Avg.|Retrieval|STS|Classification|Reranking|Clustering|
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|Sarashina-Embedding-v2-1B (This model)|**76.38**|**76.48**|**84.22**|77.14|**86.28**|52.56|
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|[cl-nagoya/ruri-v3-310m](https://huggingface.co/cl-nagoya/ruri-v3-310m)|75.85|76.03|81.59|**77.65**|85.84|50.52|
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