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Scibert Scivocab Uncased Finetuned Ner
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Scibert Scivocab Uncased Finetuned Ner

Developed by HenryHXR
A named entity recognition model fine-tuned based on the SciBERT pre-trained model for scientific domains
Sequence Labeling
Transformers
#Scientific Literature NER#BERT Fine-tuning#Case Insensitive
Downloads 19
Release Time : 4/5/2022

Model Overview

This model is a fine-tuned version of SciBERT specifically for Named Entity Recognition (NER) tasks, suitable for entity recognition in scientific literature

Model Features

Scientific Domain Optimization
Trained on scientific vocabulary for better understanding of terms in scientific literature
Named Entity Recognition
Fine-tuned specifically for entity recognition tasks in scientific literature
BERT Architecture
Utilizes powerful Transformer architecture to capture deep semantic relationships in text

Model Capabilities

Scientific text entity recognition
Named entity classification
Scientific literature information extraction

Use Cases

Academic Research
Scientific Literature Metadata Extraction
Automatically extract key entities such as methods, materials, results, etc. from research papers
Knowledge Graph Construction
Identify entities in scientific literature for building domain knowledge graphs
Information Retrieval
Academic Search Engine Optimization
Enhance entity recognition capabilities of academic search engines
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