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 tokenizer.json from malma0/e5-gesn: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/malma0/e5-gesn/resolve/main/tokenizer.json
- Command line
-
hf download hf://malma0/e5-gesn/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/malma0/e5-gesn/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- b8e241e404a10d5495cc933310aabbd4de2845f68b22897c23bfc5198dde2af1
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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