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 did you create (title, paragraph) pairs from c4?
#45
by itayair - opened
In addition, did you filter the C4 data?
To create contrastive pairs, please refer to the discussion at https://huggingface.co/intfloat/multilingual-e5-large/discussions/37#664b1fe87a1ed3e001471b2f
And yes, we filter the mC4 data using the consistency-based filtering approach in Text Embeddings by Weakly-Supervised Contrastive Pre-training
One more thing, what does the page_content you take? (The web pages might be much longer than 512)
In that case, texts will be truncated to fit the model's maximum support length.