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
How to fine-tune for 2 additional languages
#47
by CCRss - opened
Hello, great model. I tested on English, but on my language (Kazakh it's not working well). What would be the best way to fine-tune it? I have prepared dataset 500k rows of wiki data (1 row 512 max token chunks) of (anchors, positive, negative) on 3 languages (ru, kk, en) and want to fine-tune this model, but I'm not sure what would be the right way to do it. For example:
anchor (en) | positive (en) | negative (en)
or
anchor (en) | positive (kk) | negative (kk)
or
anchor (kk) | positive (en) | negative (en)
or combined all cases as a 1 dataset.
Don't have intuition how exactly do it, please if someone can help that will really help us ๐.