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
Download 1_Pooling/config.json from NASA-AIML/MIKA_Custom_IR: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/NASA-AIML/MIKA_Custom_IR/resolve/refs%2Fpr%2F1/1_Pooling/config.json
- Command line
-
hf download hf://NASA-AIML/MIKA_Custom_IR@refs/pr/1/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/NASA-AIML/MIKA_Custom_IR/resolve/refs%2Fpr%2F1/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |