How to use Eklavya73/sbert_finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Eklavya73/sbert_finetuned") sentences = [ "request increase server capacity dear customer support ask upgrade server capacity optimize database improve scalability performance saas project management platform current setup limit increase user volume lead long response time reduced productivity ensure smooth user experience remain competitive extension infrastructure necessary ask urgent review request timely solution please inform u detail need process start", "customer service update please write request update integration enhance compatibility across multiple product within scalable saas project management platform aim improve user experience increase efficiency", "system failure data synchronization problem detect incident impact several product result system crash data synchronization error root cause suspect server overload lead integration failure effort resolve include restarting service clear cache review log problem persist scalability challenge likely underlying issue", " customer support inquire updating security protocol software integration within hospital system objective improve data protection compliance ensure confidentiality integrity patient information would like know solution available process implement update could please provide detail matter thank assistance look forward prompt response best regard thank support" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
