Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
xlm-roberta
mteb
Sentence Transformers
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-base") 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
Semantic similarity
#29 opened 12 months ago
by
ZijieAsus
Adding ONNX file of this model
#28 opened 12 months ago
by
ra1f
Request for Fine-Tuning Documentation for intfloat/multilingual-e5-base
3
#25 opened over 1 year ago
by
epchannel
Unrelated docs gets higher score even if that does not have matching docs in the indexed db.
1
#21 opened about 2 years ago
by
HimSinghvi
[AUTOMATED] Model Memory Requirements
#15 opened over 2 years ago
by
model-sizer-bot
Semantic Search
5
#14 opened over 2 years ago
by
dilolo
vocab.txt
4
#7 opened almost 3 years ago
by
jzhang86
details on dataset
2
#4 opened almost 3 years ago
by
carlesoctav
how train in my domain
6
#2 opened almost 3 years ago
by
chaochaoli