Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
sbert
embeddings
multilingual
en
uk
ru
text-embeddings-inference
Instructions to use uaritm/multilingual_en_uk_ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use uaritm/multilingual_en_uk_ru with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("uaritm/multilingual_en_uk_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] - Transformers
How to use uaritm/multilingual_en_uk_ru with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("uaritm/multilingual_en_uk_ru") model = AutoModel.from_pretrained("uaritm/multilingual_en_uk_ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Citing & Authors
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```
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@misc{
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title={sentence-transformers: Semantic similarity of medical texts},
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author={Vitaliy Ostashko},
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year={2023},
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url={https://
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}
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```
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## Citing & Authors
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```
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@misc{UARITM,
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title={sentence-transformers: Semantic similarity of medical texts ukr, kor, eng},
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author={Vitaliy Ostashko},
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year={2023},
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url={https://ai.esemi.org},
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}
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```
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