bert-base-uncased-ag_news Open-Source Text Classification Model - Free Deployment, with a Classification Accuracy of 93.75%

Bert Base Uncased Ag News

Developed by fabriceyhc

A text classification model fine-tuned on the AG News dataset based on the BERT base model, achieving an accuracy of 93.75%

Text Classification

Transformers

Open Source License:Apache-2.0 #News Classification #High Accuracy #BERT Fine-tuning

Downloads 125

Release Time : 3/2/2022

Model Overview

This model is a text classification model fine-tuned for the AG News classification task based on bert-base-uncased. It is mainly used for news text classification tasks and can categorize news into different classes.

Model Features

High Accuracy

Achieves a classification accuracy of 93.75% on the AG News test set

BERT-based

Uses bert-base-uncased as the base model, providing strong text understanding capabilities

Specialized for News Classification

Optimized specifically for news text classification tasks

Model Capabilities

News Text Classification

English Text Understanding

Multi-class Classification

Use Cases

News Classification

Automatic News Classification

Automatically categorizes news articles into predefined classes

Accuracy 93.75%

Content Filtering

Filters and recommends content based on news categories

🚀 bert-base-uncased-ag_news

This model is a fine - tuned version of [bert - base - uncased](https://huggingface.co/bert - base - uncased) on the ag_news dataset. It can achieve high accuracy in text classification tasks, providing reliable performance for news categorization.

🚀 Quick Start

This model is a fine - tuned version of [bert - base - uncased](https://huggingface.co/bert - base - uncased) on the ag_news dataset. It achieves the following results on the evaluation set:

Loss: 0.3284
Accuracy: 0.9375

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 8
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 7425
training_steps: 74250

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5773	0.13	2000	0.3627	0.8875
0.3101	0.27	4000	0.2938	0.9208
0.3076	0.4	6000	0.3114	0.9092
0.3114	0.54	8000	0.4545	0.9008
0.3154	0.67	10000	0.3875	0.9083
0.3095	0.81	12000	0.3390	0.9142
0.2948	0.94	14000	0.3341	0.9133
0.2557	1.08	16000	0.4573	0.9092
0.258	1.21	18000	0.3356	0.9217
0.2455	1.35	20000	0.3348	0.9283
0.2361	1.48	22000	0.3218	0.93
0.254	1.62	24000	0.3814	0.9033
0.2528	1.75	26000	0.3628	0.9158
0.2282	1.89	28000	0.3302	0.9308
0.224	2.02	30000	0.3967	0.9225
0.174	2.15	32000	0.3669	0.9333
0.1848	2.29	34000	0.3435	0.9283
0.19	2.42	36000	0.3552	0.93
0.1865	2.56	38000	0.3996	0.9258
0.1877	2.69	40000	0.3749	0.9258
0.1951	2.83	42000	0.3963	0.9258
0.1702	2.96	44000	0.3655	0.9317
0.1488	3.1	46000	0.3942	0.9292
0.1231	3.23	48000	0.3998	0.9267
0.1319	3.37	50000	0.4292	0.9242
0.1334	3.5	52000	0.4904	0.9192

Framework versions

Transformers 4.10.2
Pytorch 1.7.1
Datasets 1.6.1
Tokenizers 0.10.3

📄 License

This model is released under the Apache 2.0 license.

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