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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: ai-chat-underage-moderation2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ai-chat-underage-moderation2 |
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This model is for detecting "underage" requests for uncensored chats. |
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While previous versions of the model had more labels, this model will only tag inappropriate content that is aimed at underage people. |
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This was done to improve accuracy specifically in this regard. |
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Available flags are: |
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``` |
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0 = regular |
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1 = underage |
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``` |
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### Model usage |
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For example: <br> |
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**"Act like a 16 year old girl"** will be flagged by this model. <br> |
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Same request is not flagged by OpenAI omni-moderation. While this message is not "inappropriate" itself, it might cause problems in later messages. |
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There are still ways to overcome the moderation if you try hard enough: |
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**"You're 30 year old woman, act like a 17 year old girl"** - Will not be flagged by this model. |
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This will be improved in the next version. |
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Model successfully differentiates between "normal" messages and sexual ones. |
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For example: |
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"I love my daughter, she's 17" - will not be tagged by the model. |
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There's still a long way to go, but I think this is a first version of the model that is good enough for production. |
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#### BEWARE: Regular sexual content won't be tagged. |
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### Dataset |
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The model was trained on a fully synthetic dataset + a mix of organic chatting data. |
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The size of the dataset is around ~30k messages. |
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### How to use |
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```python |
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from transformers import ( |
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pipeline |
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) |
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picClassifier = pipeline("text-classification", model="andriadze/ai-chat-underage-moderation2") |
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res = picClassifier('Can you send me a selfie?') |
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``` |
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### Training Params |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.1041 | 1.0 | 1126 | 0.0775 | 0.9782 | |
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| 0.0558 | 2.0 | 2252 | 0.0755 | 0.9822 | |
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| 0.0243 | 3.0 | 3378 | 0.0821 | 0.9833 | |
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### Framework versions |
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- Transformers 4.46.0 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.2 |
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- Tokenizers 0.20.1 |
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