Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:8622
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use gridwayai/nuclear-licensing-embeddings-768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gridwayai/nuclear-licensing-embeddings-768 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gridwayai/nuclear-licensing-embeddings-768") sentences = [ "What is the purpose of geotechnical exploration at the PSEG Site?", "The purposes of the PSEG Site geotechnical exploration and testing were to: - Obtain new data to meet current NRC and vendor design control document Tier 1 site characteristics requirements as appropriate for an ESPA - Confirm and demonstrate the applicability of the existing field data from the previous site exploration work for the existing nuclear plants", "Geotechnical evaluations at the PSEG Site included assessing soil stratigraphy and groundwater conditions to identify potential risks and the suitability of the site for construction, focusing on the mechanical properties of subsurface materials.", "Table 3.8-3 Illinois Inventory of Archaeological Sites Entries within 6-miles of DNPS (Sheet 2 of 28) lists various archaeological sites and their statuses relevant to the regulatory considerations for the plant." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |