Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2542
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use HydroEmbed/HydroEmbed-MCQTF-MiniLM-MNRL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HydroEmbed/HydroEmbed-MCQTF-MiniLM-MNRL with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HydroEmbed/HydroEmbed-MCQTF-MiniLM-MNRL") sentences = [ "How does climate change influence the hydrological cycle and streamflow patterns in river basins?", "Answer: B) The protection of the water environment is essential.", "Answer: B) Changes in climatic forces and land use/land cover (LULC) changes characterized by re-vegetation", "Answer: B) Climate change can lead to increased temperatures and altered precipitation patterns, affecting streamflow variability." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 368 Bytes
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