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
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |