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
Fine tuning on text domain
#3
by wilfoderek - opened
Hi guys!
Please, share the receip to fine tuning in my own corpus this amazin model.
Thank you in advance!
Hi,
What codebase can I refer to if I want to implement the pre-training stage in your e5 paper?
Is there a tutorial on how to fine tune this model on a Greek sentence similarity dataset?
I am not aware of any, but you can adapt existing codebase for your specific needs.