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-small-en-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0rj/e5-small-en-ru with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("d0rj/e5-small-en-ru") model = AutoModel.from_pretrained("d0rj/e5-small-en-ru") - sentence-transformers
How to use d0rj/e5-small-en-ru with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("d0rj/e5-small-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
fix word_embedding_dimension parameter
Browse filesHello! It seems that the embedding dimension here should be 384 as in the original e5-small

- 1_Pooling/config.json +1 -1
1_Pooling/config.json
CHANGED
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@@ -1,5 +1,5 @@
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{
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-
"word_embedding_dimension":
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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{
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+
"word_embedding_dimension": 384,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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