Instructions to use Muennighoff/SGPT-1.3B-weightedmean-nli-bitfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Muennighoff/SGPT-1.3B-weightedmean-nli-bitfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Muennighoff/SGPT-1.3B-weightedmean-nli-bitfit") 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
File size: 299 Bytes
21ac01b | 1 2 3 | epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
-1,-1,0.8329852176094534,0.8386309954374512,0.8291196910761947,0.828296436242254,0.8302104318397378,0.8293978465982256,0.7205795699601987,0.7008266718943091
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