Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
Generated from Trainer
dataset_size:35934
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use ITOCJ/SciGenNomicEmbedStatic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ITOCJ/SciGenNomicEmbedStatic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ITOCJ/SciGenNomicEmbedStatic") sentences = [ "Stating purpose of the current research with reference to gaps or issues in the literature", "During the 15-year study, 10% of the osseointegrated implants in the edentulous jaw showed signs of peri-implantitis, leading to their failure.", "This paper provides an in-depth exploration of the qualitative case study methodology, addressing the lack of comprehensive guidance for novice researchers in this area.", "As a novice researcher in management science, I have been drawn to the qualitative case study methodology due to its ability to provide rich, in-depth insights into complex real-world situations." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 134 Bytes
8ae4f89 | 1 2 3 4 5 6 7 8 | [
{
"idx": 0,
"name": "0",
"path": "0_StaticEmbedding",
"type": "sentence_transformers.models.StaticEmbedding"
}
] |