Sentence Similarity
sentence-transformers
ONNX
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:10134
loss:CachedMultipleNegativesRankingLoss
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use malma0/e5-gesn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use malma0/e5-gesn with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("malma0/e5-gesn") sentences = [ "query: грунтовка стен квартиры", "passage: Отделочные работы. Окраска фасадов с люлек по подготовленной поверхности: силикатная. Ед. изм.: 100 м2", "passage: Отделочные работы. Покрытие поверхностей грунтовкой глубокого проникновения: за 1 раз стен. Ед. изм.: 100 м2", "passage: Отделочные работы. Покрытие поверхностей грунтовкой глубокого проникновения: за 2 раза стен. Ед. изм.: 100 м2" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from malma0/e5-gesn: direct link, hf CLI and curl.
- Browser
- Download file 90 Bytes
-
https://huggingface.co/malma0/e5-gesn/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://malma0/e5-gesn/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/malma0/e5-gesn/resolve/main/1_Pooling/config.json
90 Bytes
| { | |
| "embedding_dimension": 768, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |