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Dmeta Embedding Zh
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Dmeta Embedding Zh
Developed by DMetaSoul
Dmeta-embedding is a model for sentence similarity calculation and feature extraction, supporting various text-related tasks.
Downloads 2,774
Release Time : 1/25/2024
Model Overview
This model is primarily used for tasks such as sentence similarity calculation, feature extraction, and text retrieval, demonstrating excellent performance on multiple Chinese datasets.
Model Features
Multi-task Support
Supports various tasks including sentence similarity calculation, text classification, clustering, retrieval, and re-ranking.
High Performance
Demonstrates outstanding performance on multiple Chinese datasets, particularly excelling in medical QA retrieval tasks.
Multiple Similarity Metrics
Supports various similarity measurement methods such as cosine similarity, Euclidean distance, and Manhattan distance.
Model Capabilities
Sentence similarity calculation
Feature extraction
Text classification
Clustering
Text retrieval
Re-ranking
Use Cases
Medical QA
Medical QA Retrieval
Used in medical QA retrieval systems to help users quickly find relevant answers.
Achieved an average precision of 88.48% on the CMedQA dataset.
Text Classification
Product Review Classification
Classifies product reviews on e-commerce platforms.
Achieved an accuracy of 44.93% on the AmazonReviews Chinese dataset.
Sentence Similarity
Sentence Pair Matching
Determines whether two sentences express the same or similar meaning.
Achieved a Pearson correlation coefficient of 65.61% for cosine similarity on the AFQMC dataset.
Transformers
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