Sentence Similarity
sentence-transformers
PyTorch
Safetensors
mpnet
feature-extraction
text-embeddings-inference
Instructions to use NASA-AIML/MIKA_Custom_IR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NASA-AIML/MIKA_Custom_IR with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NASA-AIML/MIKA_Custom_IR") sentences = [ "what components are vulnerable to fatigue crack?", "One of the first-stage compressor blades had fractued due to fatigue cracking.", "Witnesses and the fire department personnel noted fuel leaking due to a cracked fuel line.", "During periods of low visibility and night conditions, the supporting sensors sometimes conflict." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 99dbdda
fixed example
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README.md
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- source_sentence: "what components are vulnerable to fatigue crack?"
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# Manager for Intelligent Knowledge Access (MIKA)
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widget:
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- source_sentence: "what components are vulnerable to fatigue crack?"
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sentences:
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- "One of the first-stage compressor blades had fractued due to fatigue cracking."
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- "Witnesses and the fire department personnel noted fuel leaking due to a cracked fuel line."
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- "During periods of low visibility and night conditions, the supporting sensors sometimes conflict."
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example_title: "Fatigue Crack Query"
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# Manager for Intelligent Knowledge Access (MIKA)
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