Sentence Similarity
Transformers
PyTorch
Safetensors
sentence-transformers
English
Russian
xlm-roberta
feature-extraction
mteb
retrieval
retriever
pruned
e5
text-embeddings-inference
Instructions to use d0rj/e5-base-en-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0rj/e5-base-en-ru with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("d0rj/e5-base-en-ru") model = AutoModel.from_pretrained("d0rj/e5-base-en-ru") - sentence-transformers
How to use d0rj/e5-base-en-ru with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("d0rj/e5-base-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:
- ce060099fd02768560419c518432aa2244ef57fb24359eb0110701bb2c2b18aa
- Size of remote file:
- 529 MB
- SHA256:
- 95d81c27bdb8a225fa946c4baf0f834b9bb1c215cf8780e28f343939afa44f21
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