Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-large") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Fix SummEval scores (https://github.com/embeddings-benchmark/mteb/pull/99)
#4
by Muennighoff - opened
There was an issue with the SummEval benchmark which affected e5 - I reran e5 with the new code (https://github.com/embeddings-benchmark/mteb/pull/99) & these are the scores I got. Feel free to rerun on your own hardware though & report those scores instead.
Muennighoff changed pull request title from Fix SummEval scores (https://github.com/embeddings-benchmark/mteb/pull/990) to Fix SummEval scores (https://github.com/embeddings-benchmark/mteb/pull/99)
intfloat changed pull request status to merged