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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |