Reasoning Bert Ccnews
This is a fine-tuned BERT-based sentence transformer model for mapping sentences and paragraphs into a 768-dimensional vector space, supporting tasks such as semantic text similarity and semantic search.
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Release Time : 3/28/2025
Model Overview
This model is fine-tuned based on google-bert/bert-base-uncased, trained on the reason_unfiltered dataset, primarily used for natural language processing tasks such as semantic text similarity calculation and information retrieval.
Model Features
High-Quality Sentence Embeddings
Maps sentences and paragraphs into a 768-dimensional dense vector space, preserving semantic information.
Reasoning-Guided Ranking Loss Training
Trained using a reasoning-guided ranking loss function to optimize semantic similarity judgment.
Multi-Task Performance Optimization
Performs well on multiple information retrieval tasks, including datasets such as nfcorpus and trec-covid.
Model Capabilities
Semantic text similarity calculation
Semantic search
Paraphrase mining
Text classification
Clustering analysis
Use Cases
Information Retrieval
Document Similarity Search
Finding semantically similar documents in large-scale document collections
Achieved an accuracy@10 of 0.5975 on the nfcorpus dataset.
Question Answering System
Matching the semantic similarity between questions and candidate answers
Achieved an accuracy@1 of 0.7256 on the quora dataset.
News Analysis
News Content Similarity Analysis
Analyzing the semantic relevance between different news articles
Trained on the CCNews dataset, suitable for news text processing.
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