ModerationGPT Open-source Text Moderation Model - Free Deployment, Accurately Complete Content Filtering and Review

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Moderationgpt

Developed by abhiai

A text moderation model fine-tuned based on t5-small, excelling in content filtering and moderation tasks

Large Language Model

Transformers

Open Source License:Apache-2.0 #Text Moderation #High-precision Rouge #Content Filtering

Downloads 22

Release Time : 5/6/2023

Model Overview

This model is a text generation model based on the T5 architecture, specifically optimized for content moderation tasks, capable of identifying and handling inappropriate content

Model Features

Efficient Content Moderation

Specially optimized for content moderation tasks, capable of efficiently identifying inappropriate content

High Accuracy

Achieves a Rouge1 score of 81.77 on the evaluation set, demonstrating excellent performance

Lightweight Model

Based on the T5-small architecture, balancing performance and efficiency

Model Capabilities

Text Moderation

Content Filtering

Sensitive Information Identification

Text Classification

Use Cases

Content Safety

Social Media Content Moderation

Automatically identifies and filters inappropriate content on social media

Rouge1 score 81.77%

User-Generated Content Moderation

Moderates user-generated content such as forums and comment sections

Rouge2 score 77.82%

🚀 ModerationGPT

ModerationGPT is a fine - tuned version of the [t5 - small](https://huggingface.co/t5 - small) model, achieving excellent performance on the evaluation set.

📄 License

This model is released under the Apache-2.0 license.

📚 Documentation

Model Overview

This model is a fine - tuned version of [t5 - small](https://huggingface.co/t5 - small) on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2177
Rouge1: 81.7742
Rouge2: 77.8168
Rougel: 81.7812
Rougelsum: 81.7593
Gen Len: 13.6851

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 128
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 20

Training Results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	335	1.0577	54.2885	42.6805	54.1114	54.2064	15.8069
1.4482	2.0	670	0.5655	71.3866	65.6295	71.3181	71.3413	13.3214
0.7111	3.0	1005	0.4278	73.535	68.2492	73.5214	73.5207	13.3682
0.7111	4.0	1340	0.3626	74.1375	69.0355	74.1302	74.1116	13.409
0.508	5.0	1675	0.3200	75.8217	69.4616	75.8126	75.7987	13.4467
0.4281	6.0	2010	0.2923	80.1152	75.0253	80.1019	80.0814	13.5971
0.4281	7.0	2345	0.2740	81.1188	76.4582	81.1144	81.0866	13.6413
0.3818	8.0	2680	0.2621	81.4117	76.9557	81.4064	81.3814	13.6591
0.3505	9.0	3015	0.2521	81.5234	77.2074	81.5176	81.4917	13.6683
0.3505	10.0	3350	0.2461	81.6226	77.3821	81.6243	81.5999	13.6729
0.3331	11.0	3685	0.2401	81.6549	77.4807	81.6601	81.6397	13.6769
0.3176	12.0	4020	0.2341	81.7157	77.6237	81.7192	81.6996	13.6807
0.3176	13.0	4355	0.2314	81.7288	77.6693	81.7346	81.7125	13.6814
0.3068	14.0	4690	0.2271	81.7534	77.7293	81.758	81.7446	13.6834
0.3011	15.0	5025	0.2239	81.7569	77.7588	81.7643	81.7426	13.6839
0.3011	16.0	5360	0.2209	81.761	77.7766	81.7681	81.7474	13.6855
0.2951	17.0	5695	0.2201	81.7483	77.7596	81.7516	81.7331	13.6847
0.2904	18.0	6030	0.2188	81.7582	77.7865	81.7605	81.7428	13.6842
0.2904	19.0	6365	0.2179	81.7842	77.8249	81.7899	81.7721	13.686
0.2893	20.0	6700	0.2177	81.7742	77.8168	81.7812	81.7593	13.6851

Framework Versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3

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