The bert-base-uncased-finetuned-sdg-Mar23 open-source classification model achieves an accuracy of 91.13% for specific tasks!

Home

Bert Base Uncased Finetuned Sdg Mar23

Developed by jonas

A classification model fine-tuned based on the bert-base-uncased model, achieving 91.13% accuracy on specific tasks

Text Classification

Transformers

Open Source License:Apache-2.0 #Text Classification #High Accuracy #Multi-label Recognition

Downloads 357

Release Time : 3/20/2023

Model Overview

This model is a classification model fine-tuned based on the BERT base version, mainly used for text classification tasks, supporting classification into 16 categories

Model Features

High Accuracy

Achieves a classification accuracy of 91.13% on the evaluation set

BERT Fine-tuning

Utilizes pre-trained BERT model for domain adaptation fine-tuning

Multi-category Classification

Supports text classification into 16 different categories

Model Capabilities

Text Classification

Natural Language Understanding

Sequence Labeling

Use Cases

Text Classification

Document Classification

Classify document content

Accuracy 91.13%

Training Loss	Epoch	Step	Validation Loss	Acc
0.4165	1.0	1098	0.3656	0.8908
0.2062	2.0	2196	0.3234	0.9113

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Bert Base Uncased Finetuned Sdg Mar23

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 bert-base-uncased-finetuned-sdg-Mar23

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Labelling

🔧 Technical Details

Training procedure

Training hyperparameters

Training results

Framework versions

📄 License