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distilroberta-nsfw-text-classifier

Text Classification
Transformers
Safetensors
English
roberta
Transformers
PyTorch
safety
innapropriate
distilroberta
Eval Results
Inference Endpoints
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metadata
widget:
  - text: A family hiking in the mountains
    example_title: Safe
  - text: A child playing with a puppy
    example_title: Safe
  - text: A couple kissing passionately in bed
    example_title: Nsfw
  - text: A woman naked
    example_title: Nsfw
  - text: A man killing people
    example_title: Nsfw
  - text: A mass shooting
    example_title: Nsfw
license: apache-2.0
language:
  - en
metrics:
  - f1
  - accuracy
  - precision
  - recall
pipeline_tag: text-classification
tags:
  - Transformers
  - ' PyTorch'
  - safety
  - innapropriate
  - distilroberta
datasets:
  - eliasalbouzidi/NSFW-Safe-Dataset
model-index:
  - name: NSFW-Safe-Dataset
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: NSFW-Safe-Dataset
          type: .
        metrics:
          - name: F1
            type: f1
            value: 0.975
          - name: Accuracy
            type: accuracy
            value: 0.981

Model Card

This model is designed to categorize text into two classes: "safe", or "nsfw" (not safe for work), which makes it suitable for content moderation and filtering applications.

The model was trained using a dataset containing 190,000 labeled text samples, distributed among the two classes of "safe" and "nsfw".

The model is based on the Distilroberta-base model.

In terms of performance, the model has achieved a score of 0.975 for F1 (40K exemples).

To improve the performance of the model, it is necessary to preprocess the input text. You can refer to the preprocess function in the app.py file in the following space: https://huggingface.co/spaces/eliasalbouzidi/distilbert-nsfw-text-classifier.

Model Description

The model can be used directly to classify text into one of the two classes. It takes in a string of text as input and outputs a probability distribution over the two classes. The class with the highest probability is selected as the predicted class.

  • Developed by: Centrale Supélec Students
  • Model type: 82M
  • Language(s) (NLP): English
  • License: apache-2.0

Uses

The model can be integrated into larger systems for content moderation or filtering.

Training Data

The training data for finetuning the text classification model consists of a large corpus of text labeled with one of the two classes: "safe" and "nsfw". The dataset contains a total of 190,000 examples, which are distributed as follows:

117,000 examples labeled as "safe"

63,000 examples labeled as "nsfw"

It was assembled by scraping data from the web and utilizing existing open-source datasets. A significant portion of the dataset consists of descriptions for images and scenes. The primary objective was to prevent diffusers from generating NSFW content but it can be used for other moderation purposes.

You can access the dataset : https://huggingface.co/datasets/eliasalbouzidi/NSFW-Safe-Dataset

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 600
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Fbeta 1.6 False positive rate False negative rate Precision Recall
0.3325 0.0998 586 0.1839 0.9479 0.9280 0.9132 0.0202 0.1052 0.9638 0.8948
0.1161 0.1997 1172 0.0958 0.9682 0.9576 0.9577 0.0256 0.0422 0.9574 0.9578
0.1006 0.2995 1758 0.0959 0.9716 0.9616 0.9554 0.0140 0.0524 0.9761 0.9476
0.0984 0.3994 2344 0.0776 0.9717 0.9628 0.9674 0.0293 0.0265 0.9523 0.9735
0.0897 0.4992 2930 0.0994 0.9676 0.9580 0.9696 0.0428 0.0151 0.9326 0.9849
0.0856 0.5991 3516 0.0889 0.9751 0.9670 0.9684 0.0219 0.0299 0.9638 0.9701
0.0779 0.6989 4102 0.0842 0.9762 0.9681 0.9652 0.0149 0.0385 0.9749 0.9615
0.0821 0.7988 4688 0.0702 0.9768 0.9693 0.9723 0.0228 0.0239 0.9626 0.9761
0.073 0.8986 5274 0.0773 0.9776 0.9703 0.9727 0.0213 0.0243 0.9650 0.9757
0.0775 0.9985 5860 0.0709 0.9774 0.9701 0.9721 0.0210 0.0252 0.9653 0.9748
0.0627 1.0983 6446 0.0646 0.9789 0.9720 0.9737 0.0193 0.0240 0.9681 0.9760
0.0648 1.1982 7032 0.0729 0.9787 0.9716 0.9708 0.0158 0.0303 0.9735 0.9697
0.0592 1.2980 7618 0.0733 0.9802 0.9735 0.9725 0.0144 0.0289 0.9760 0.9711
0.0585 1.3979 8204 0.0764 0.9790 0.9720 0.9723 0.0173 0.0273 0.9713 0.9727
0.0579 1.4977 8790 0.0691 0.9782 0.9712 0.9739 0.0214 0.0225 0.9649 0.9775
0.0584 1.5975 9376 0.0739 0.9797 0.9732 0.9755 0.0195 0.0215 0.9679 0.9785
0.0564 1.6974 9962 0.0749 0.9774 0.9703 0.9757 0.0257 0.0173 0.9583 0.9827
0.0582 1.7972 10548 0.0721 0.9804 0.9739 0.9742 0.0162 0.0253 0.9730 0.9747
0.0576 1.8971 11134 0.0746 0.9799 0.9732 0.9734 0.0163 0.0264 0.9729 0.9736
0.0546 1.9969 11720 0.0758 0.9804 0.9739 0.9736 0.0152 0.0269 0.9747 0.9731
0.0431 2.0968 12306 0.0755 0.9805 0.9741 0.9762 0.0186 0.0211 0.9694 0.9789
0.0464 2.1966 12892 0.0785 0.9802 0.9737 0.9735 0.0156 0.0266 0.9740 0.9734
0.0443 2.2965 13478 0.0763 0.9811 0.9748 0.9734 0.0132 0.0283 0.9779 0.9717
0.0426 2.3963 14064 0.0753 0.9812 0.9750 0.9760 0.0165 0.0227 0.9727 0.9773
0.0413 2.4962 14650 0.0750 0.9811 0.9748 0.9760 0.0168 0.0225 0.9722 0.9775
0.0442 2.5960 15236 0.0756 0.9813 0.9752 0.9766 0.0169 0.0216 0.9720 0.9784
0.043 2.6959 15822 0.0810 0.9814 0.9750 0.9729 0.0119 0.0299 0.98 0.9701
0.0433 2.7957 16408 0.0783 0.9814 0.9751 0.9733 0.0125 0.0289 0.9790 0.9711
0.0398 2.8956 16994 0.0736 0.9814 0.9752 0.9766 0.0169 0.0216 0.9721 0.9784
0.0431 2.9954 17580 0.0757 0.9816 0.9754 0.9757 0.0151 0.0240 0.9749 0.9760
We selected the checkpoint with the highest F-beta1.6 score.

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1

Out-of-Scope Use

It should not be used for any illegal activities.

Bias, Risks, and Limitations

The model may exhibit biases based on the training data used. It may not perform well on text that is written in languages other than English. It may also struggle with sarcasm, irony, or other forms of figurative language. The model may produce false positives or false negatives, which could lead to incorrect categorization of text.

Recommendations

Users should be aware of the limitations and biases of the model and use it accordingly. They should also be prepared to handle false positives and false negatives. It is recommended to fine-tune the model for specific downstream tasks and to evaluate its performance on relevant datasets.

Load model directly 

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("eliasalbouzidi/distilroberta-nsfw-text-classifier")

model = AutoModelForSequenceClassification.from_pretrained("eliasalbouzidi/distilroberta-nsfw-text-classifier")

Use a pipeline 

from transformers import pipeline

pipe = pipeline("text-classification", model="eliasalbouzidi/distilroberta-nsfw-text-classifier")

Contact

Please reach out to [email protected] if you have any questions or feedback.