This model is a fine-tuned version of roberta-base on the dirtycomputer/Toxic_Comment_Classification_Challenge dataset. It achieves the following results on the evaluation set:

Loss: 0.0511
Accuracy: 0.9812

Model description

Fine-tuned roberta-base model for detecting toxicity in comments. It categorizes a comment into different toxicity types, such as "toxic," "obscene," "insult," and "threat."

Intended uses & limitations

Intended Uses

Content Moderation: Automatically flagging or removing toxic comments on social media platforms, forums, and customer support.
Toxicity Detection: Classifying comments based on their toxicity level, such as harmful language or insults.

Limitations

False Negatives: May not always catch subtle toxic behavior.
Limited Language Support: Currently, the model is trained on English-only data.
Context Sensitivity: May struggle with ambiguous language or sarcasm.

Training and evaluation data

This model was fine-tuned using the dirtycomputer/Toxic_Comment_Classification_Challenge dataset, which contains labeled comments for toxicity classification.

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: Use adamw_torch with betas=(0.9, 0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1691	1.0	17952	0.1464	0.9617
0.0892	2.0	35904	0.1456	0.9617
0.0527	3.0	53856	0.0511	0.9812

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

HyperX-Sentience/RogueBERT-Toxicity-85K

toxicity-classification-model