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
Checkpoint of the first stage
#28
by YuTian8328 - opened
Thank you so much for the releasing of an amazing model.
I'm working with a specialised domain in Finnish, and I believe that the checkpoint from the first stage could be immensely beneficial for my project. Could I possibly acquire the checkpoint from the first stage?
Can you fine-tune starting with this checkpoint? I do not think we'll release the first-stage checkpoint, sorry for the inconvenience.