Feature Extraction
sentence-transformers
Safetensors
bert
sentence-similarity
biomedical
embeddings
life-sciences
scientific-text
SODA-VEC
EMBO
text-embeddings-inference
Instructions to use EMBO/negative_sampling_pmb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EMBO/negative_sampling_pmb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EMBO/negative_sampling_pmb") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - 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" | |
| } | |
| ] |