Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
dense
Generated from Trainer
dataset_size:3763
loss:MultipleNegativesRankingLoss
Instructions to use Eklavya73/sbert_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
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] - Notebooks
- Google Colab
- Kaggle
Commit History
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Verified signature# Intelligent Ticket Auto-Routing System (SBERT Fine-Tuned Model) This repository contains a fine-tuned Sentence-BERT (SBERT) model based on `sentence-transformers/all-mpnet-base-v2`, optimized for semantic understanding of IT support tickets. The model is designed to power an automated ticket routing system by generating high-quality sentence embeddings for tasks such as multi-label classification, duplicate detection, and semantic similarity matching. ## 🔍 Key Capabilities * **Semantic Ticket Understanding**: Converts ticket descriptions into dense vector embeddings. * **Duplicate Detection**: Identifies semantically similar or duplicate tickets. * **Multi-Label Routing Support**: Enables downstream classification models to assign tickets to appropriate departments or categories. * **High Recall Similarity Matching**: Improves retrieval-based workflows using cosine similarity. ## ⚙️ Model Details * **Base Model**: sentence-transformers/all-mpnet-base-v2 * **Architecture**: Tran…2dcef2a -
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