Sentence Similarity
sentence-transformers
Safetensors
qwen2
feature-extraction
Generated from Trainer
dataset_size:693000
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use FINGU-AI/Fingu-M-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FINGU-AI/Fingu-M-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FINGU-AI/Fingu-M-v2", trust_remote_code=True) sentences = [ "Paracrystalline materials are defined as having short and medium range ordering in their lattice (similar to the liquid crystal phases) but lacking crystal-like long-range ordering at least in one direction.", "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: Paracrystalline", "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: Øystein Dahle", "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: Makis Belevonis" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f351777f7453cd49816cd895f9fe2e49d6c220d69f6ea91f7529781b2316590d
- Size of remote file:
- 6.19 GB
- SHA256:
- ad4c6ab7fce986ed76182f793b8df42d46f680b7dc1460eff9a4a979de9aeb0a
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