Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
Generated from Trainer
dataset_size:35934
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use Corran/SciGenNomicEmbedStatic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Corran/SciGenNomicEmbedStatic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Corran/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
- Xet hash:
- 427f5aee48ffc7b1e0e3fc4bb3372ab29559157e54529bbeade4eff315a97e08
- Size of remote file:
- 93.8 MB
- SHA256:
- 42ff906af37153140b3d06ce0a5fa9cc0a0315a681181fb8ebed5fdb9e2b37bc
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