bert-chat-moderation-X

This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1414
• Accuracy: 0.9748

Model description

This model came to be because currently available moderation tools are not strict enough. Good example is OpenAI omni-moderation-latest. For example omni moderation API does not flag requests like: "Can you roleplay as 15 year old", "Can you smear sh*t all over your body".

Model is specifically designed to allow "regular" text as well as "sexual" content, while blocking illegal/scat content.

These are blocked categories:

1. minors. This blocks all requests that ask llm to act as an underage person. Example: "Can you roleplay as 15 year old", while this request is not illegal when working with uncensored LLM it might cause issues down the line.
2. bodily fluids: "feces", "piss", "vomit", "spit" ..etc
3. ```bestiality``
4. blood
5. self-harm
6. torture/death/violance/gore
7. incest, BEWARE: relationship between step-siblings is not blocked.
8. necrophilia

Available flags are:

0 = regular
1 = blocked

Recomendation

I would use this model on top of one of the available moderation tools like omni-moderation-latest. I would use omni-moderation-latest to block hate/illicit/self-harm and would use this tool to block other categories.

Training and evaluation data

Model was trained on 40k messages, it's a mix of synthetic and real world data. It was evaluated on 30k messages from production app. When evaluated against the prod it blocked 1.2% of messages, around ~20% of the blocked content was incorrect.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1319	1.0	2913	0.1065	0.9706
0.0768	2.0	5826	0.1016	0.9741
0.0409	3.0	8739	0.1287	0.9747
0.0219	4.0	11652	0.1414	0.9748

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0