Sentence Similarity
Transformers
PyTorch
Safetensors
sentence-transformers
English
Russian
xlm-roberta
feature-extraction
mteb
retrieval
retriever
pruned
e5
Eval Results (legacy)
text-embeddings-inference
Instructions to use d0rj/e5-large-en-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0rj/e5-large-en-ru with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("d0rj/e5-large-en-ru") model = AutoModel.from_pretrained("d0rj/e5-large-en-ru") - sentence-transformers
How to use d0rj/e5-large-en-ru with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("d0rj/e5-large-en-ru") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8fded435f40e9682d6aa40e433ca111eebd6ad40c0fdbc5870194aae4146522
- Size of remote file:
- 1.46 GB
- SHA256:
- a900d8829b407aaadc83b6315504ba1acdfde420b5e2288c706a0215c6b11ddb
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