Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:204810
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use olegGerbylev/all-MiniLM-L6-v2-goods with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use olegGerbylev/all-MiniLM-L6-v2-goods with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("olegGerbylev/all-MiniLM-L6-v2-goods") sentences = [ "[OFFER] Распаянная коробка ОП 100x100x55мм, крышка, IP54, 8вх. TDM | 12.0 ШТ", "[ORDER] Коробка распределительная 100х100х50 ip54 | 12.0 шт", "[OFFER] Арматура А500С 14(11,7) МД ГОСТ Р 52544-2006 | 2.5 т", "[OFFER] Штамп самонаборный пласт. 3стр. Pr.C20/3-Set 38x14 (аналог 4911/DB)Colop | 1.0 шт" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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