How to use bbmb/deep-learning-for-embedding-model-ssilwal-qpham6_army_doc with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bbmb/deep-learning-for-embedding-model-ssilwal-qpham6_army_doc") sentences = [ "Defense \n11 January 2024 ATP 3-21.8 5-57\n Reaction to enemy fires (for example, artillery and/or aviation) and CBRN\nattacks.\n Reports to higher, monitoring stockage levels, and cross leveling or resupply.\n CASEVAC and MEDEVAC procedures.\n Criteria to commitment the reserve.\nFigure 5-13. Main battle area (platoon engagements), example \nFOLLOW THROUGH \n5-167. During the planning for the defensive operation, the platoon leader must discern \nfrom the company OPORD what the potential follow-on missions are and begin to plan\nhow to achieve them. During this planning , the leader determines the possible timeline\nand location for defeat in detail , consolidate, reorganize, and transition which best\nf\nacilitates future operations and provides adequate protection.", "What are some methods for distributing fires effectively in a platoon?", "What should I consider when selecting battle positions for my unit?", "What key factors are involved in planning for a defense scenario?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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