Instructions to use Remasalmania/multilingual-e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use Remasalmania/multilingual-e5 with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("Remasalmania/multilingual-e5") - sentence-transformers
How to use Remasalmania/multilingual-e5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Remasalmania/multilingual-e5") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 322 Bytes
0596fe7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"model_type": "model2vec",
"architectures": [
"StaticModel"
],
"tokenizer_name": "intfloat/multilingual-e5-small",
"apply_pca": 64,
"sif_coefficient": 0.0001,
"hidden_dim": 64,
"seq_length": 1000000,
"normalize": true,
"pooling": "mean",
"embedding_dtype": "float16"
} |