Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
token-pruning
text-embeddings-inference
Instructions to use jangedoo/multilingual-e5-small-ne-pruned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jangedoo/multilingual-e5-small-ne-pruned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jangedoo/multilingual-e5-small-ne-pruned") 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: 90 Bytes
41f939d | 1 2 3 4 5 | {
"embedding_dimension": 384,
"pooling_mode": "mean",
"include_prompt": true
} |