Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use sarkii/MizoEmbed-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sarkii/MizoEmbed-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sarkii/MizoEmbed-1") 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
Update README.md
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by Lms18 - opened
README.md
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@@ -44,9 +44,9 @@ from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("Lms18/mizo_embed")
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# Run inference
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sentences = [
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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model = SentenceTransformer("Lms18/mizo_embed")
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# Run inference
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sentences = [
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'Nepal a ka zin chu ka hlawkpui hle mai. Nupui te pawh ka hmu tep e.',
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'Ka zinna ram Nepal ah Mount Everest a awm.',
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'Inkhelh hi ka thiam vaklo mahse ka inkhel lui tho thin.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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