Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
xlm-roberta
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Nextcloud-AI/multilingual-e5-large-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Nextcloud-AI/multilingual-e5-large-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nextcloud-AI/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] - Transformers
How to use Nextcloud-AI/multilingual-e5-large-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Nextcloud-AI/multilingual-e5-large-instruct")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Nextcloud-AI/multilingual-e5-large-instruct") model = AutoModelForMultimodalLM.from_pretrained("Nextcloud-AI/multilingual-e5-large-instruct") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28ef0f157b2f587c224a23f503dd29207cfc284d17712b50c68296b306b8ec27
- Size of remote file:
- 17.1 MB
- SHA256:
- f59925fcb90c92b894cb93e51bb9b4a6105c5c249fe54ce1c704420ac39b81af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.