Sentence Similarity
sentence-transformers
PyTorch
multilingual
DistilBert
Universal Sentence Encoder
sentence-embeddings
Instructions to use sadakmed/distiluse-base-multilingual-cased-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sadakmed/distiluse-base-multilingual-cased-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sadakmed/distiluse-base-multilingual-cased-v1") 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] - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:Config file config.json cannot be fetched (too big)
Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)
Knowledge distilled version of multilingual Universal Sentence Encoder. Supports 15 languages: Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Portuguese, Russian, Spanish, Turkish.
This Model is saved from 'distiluse-base-multilingual-cased-v1' in sentence-transformers, to be used directly from transformers
Note that ST has additional two layers(Pooling, Linear), that cannot be saved in any predefined model in HG.
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