Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
xlm-roberta
mteb
Sentence Transformers
sentence-similarity
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-large") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Maximum Chunk Size for RAG
#27
by mox - opened
What would be the maximum Chunk Size that I can use with this embedding model, if I want to split up my documents into chunks for RAG?
It would be 512 tokens.
Hi, I have a follow up question. What is the expected behaviour when the passed text is longer than 512 tokens? I assume it gets cut off at 512.
Should we account for the "passage:" prefix when chunking the documents?
i.e. should f"passage: {doc.page_content}" be 512 tokens long or doc.page_content itself?
And with this being the max_len for a chunk, is there an optimal_len we should aim for?