Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
qwen2
medical
clinicaltrials
cancer
feature-extraction
Generated from Trainer
dataset_size:1395384
loss:OnlineContrastiveLoss
loss:MultipleNegativesRankingLoss
custom_code
text-embeddings-inference
Instructions to use jim-bo/TrialSpace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jim-bo/TrialSpace with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jim-bo/TrialSpace", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K