Feature Extraction
sentence-transformers
Safetensors
bert
sentence-similarity
dense
Generated from Trainer
dataset_size:500
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use nmngpt0/bge-qa-lilsmall01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nmngpt0/bge-qa-lilsmall01 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nmngpt0/bge-qa-lilsmall01") sentences = [ "what made the civil war different from others", "The Eastern Theater was inconclusive in 1861–62.", "In reality, Headquarters AAF controlled the conduct of all aspects of the air war in every part of the world, determining air policy and issuing orders without transmitting them through the Chief of Staff.", "Skin of a different nature exists in amphibians , reptiles , and birds ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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