Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
feature-extraction
text-embeddings-inference
Instructions to use Eklavya73/duplicate_sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Eklavya73/duplicate_sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Eklavya73/duplicate_sbert") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Eklavya73/duplicate_sbert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Eklavya73/duplicate_sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Verified signature# Duplicate Ticket Detection Model (SBERT) This repository contains a Sentence-BERT (SBERT) model fine-tuned for detecting duplicate or semantically similar support tickets. The model generates dense vector embeddings for ticket descriptions, enabling efficient similarity comparison using cosine distance. ## ๐ Purpose The model is designed to identify duplicate or closely related tickets in IT service management systems. It helps reduce redundancy, improves resolution time, and enhances overall ticket handling efficiency. ## โ๏ธ Model Details * **Base Model**: microsoft/mpnet-base * **Architecture**: Transformer (MPNet) + Mean Pooling + Normalization * **Embedding Dimension**: 768 * **Max Sequence Length**: 384 * **Output**: Dense semantic embeddings for similarity comparison ## ๐ Repository Structure * `modules.json` โ Defines model pipeline (Transformer โ Pooling โ Normalize) * `0_Transformer/` โ Core transformer model, weights, and tokenizer files * `1_Pooling/` โ Mean pooliโฆf7eebff