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/FinguMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FINGU-AI/FinguMv3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FINGU-AI/FinguMv3", 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:
- b648d3a942574857548668a66d4011ec4fa4bbf46e501d2edb81c73e01ef015d
- Size of remote file:
- 6.19 GB
- SHA256:
- 6e6b9b073e7a6423f9839842837728552088566f99d11150e0c91b87923b6aa5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.