Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
xlm-roberta
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-large-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-large-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/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 intfloat/multilingual-e5-large-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="intfloat/multilingual-e5-large-instruct")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("intfloat/multilingual-e5-large-instruct") model = AutoModel.from_pretrained("intfloat/multilingual-e5-large-instruct") - Inference
- Notebooks
- Google Colab
- Kaggle
How can I download the model and use it in my local environment?
#6
by yuneun92 - opened
I've downloaded and saved this model, and tried to use the saved model. Although the file direction is right and the model was saved well, there rised value error. It says the model path is not found. is it wrong to activate model just by
SentenceTransformer('model path') ?
You have to include everything from the Files and versions tab, including the folder "1_Pooling".