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Scinertopic

Developed by RJuro
A scientific term recognition model based on SciBERT, supporting NER-enhanced topic modeling
Downloads 71
Release Time : 11/5/2022

Model Overview

This model is a named entity recognition model fine-tuned from allenai/scibert_scivocab_cased, specifically designed to identify terms in scientific literature and supports BERTopic-style topic modeling.

Model Features

Scientific term recognition
Designed specifically for scientific literature, capable of identifying 6 types of scientific terms including tasks, methods, and evaluation metrics
Topic modeling enhancement
Combined with BERTopic to achieve NER-enhanced topic modeling, improving scientific literature analysis
Efficient training
Fine-tuned from the pre-trained SciBERT model, requiring only 10 training epochs to achieve good performance

Model Capabilities

Scientific text entity recognition
Topic modeling
Academic literature analysis

Use Cases

Academic research
Machine translation research analysis
Analyzing methods, metrics, and other terms in Transformer-related papers
Can identify evaluation metrics like BLEU and model architecture information
Text-to-image generation research analysis
Identifying key terms in papers from the text-to-image generation field
Can extract modeling methods, datasets, and other information
Literature management
Automatic academic literature tagging
Automatically adding term labels to scientific literature
Improves literature retrieval and organization efficiency
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