Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:4338
loss:CosineSimilarityLoss
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use HydroEmbed/HydroEmbed-OpenQA-MiniLM-DualLoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HydroEmbed/HydroEmbed-OpenQA-MiniLM-DualLoss with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HydroEmbed/HydroEmbed-OpenQA-MiniLM-DualLoss") sentences = [ "What are the main climatic factors influencing water level fluctuations in lakes, particularly in semi-arid regions?", "The main climatic factors influencing water level fluctuations in lakes in semi-arid regions include potential evapotranspiration, precipitation, temperature, and vapor pressure.", "Bias correction improves the accuracy of satellite precipitation data, enhancing its effectiveness in streamflow simulation.", "Climate change is associated with an increase in the frequency and intensity of extreme rainfall events, although regional variations can complicate the detection of consistent trends." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle