Feature Extraction
Transformers.js
ONNX
sentence-transformers
multilingual
xlm-roberta
sentence-similarity
mteb
e5
text-embeddings-inference
Instructions to use lmo3/multilingual-e5-large-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use lmo3/multilingual-e5-large-instruct with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'lmo3/multilingual-e5-large-instruct'); - sentence-transformers
How to use lmo3/multilingual-e5-large-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lmo3/multilingual-e5-large-instruct") 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
- Xet hash:
- 9d381a2eb210bb19a6769ea4ce1c69be1b451479a0ee64cb6e563a73f672d417
- Size of remote file:
- 2.24 GB
- SHA256:
- 4c13ca44e40a7ee86b461398cdee003d656e39f3fa87e1b3152fbada3e61b506
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