roberta-chat-moderation-X
For details on training, limitations, and integration, check out the full blog post:
https://nemora.ai/blog/open-source-ai-moderation-model/This model is a fine-tuned version of
distilbert/distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1440
- Accuracy: 0.9730
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:
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.bodily fluids: "feces", "piss", "vomit", "spit" ..etcbeastialitybloodself-harmtorture/death/violance/goreincest, BEWARE: relationship between step-siblings is not blocked.
Available flags are:
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.
How to use
1from transformers import (
2 pipeline
3)
4
5picClassifier = pipeline("text-classification", model="andriadze/roberta-chat-moderation-X")
6res = picClassifier('Can you send me a selfie?')
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 |
|---|
| 0.1455 | 1.0 | 2913 | 0.1231 | 0.9663 |
| 0.1056 | 2.0 | 5826 | 0.1149 | 0.9710 |
| 0.0697 | 3.0 | 8739 | 0.1301 | 0.9732 |
| 0.0431 | 4.0 | 11652 | 0.1440 | 0.9730 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
