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DistilBERT base FR sexism detection

This model is a fine-tuned version of distilbert-base-multilingual-cased on the lidiapierre/fr_sexism_labelled dataset. It is intended to be used as a classification model for identifying sexist language in French (0 - not sexist; 1 - sexist).

It achieves the following results on the evaluation set:

  • Loss: 0.3751
  • Accuracy: 0.9123
  • F1: 0.9206

Classification examples:

Prediction Text
sexist Tu pourrais sourire plus
not sexist Tout le monde ร  table

Model description

Transformer-based language model for binary classification.

Risks & limitations

This model is susceptible of displaying bias inherited from its pretrained model: predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3.0

Training results

Epoch Step Validation Loss Accuracy F1
1.0 128 0.5027 0.8509 0.8759
2.0 256 0.2606 0.9298 0.9365
3.0 384 0.3751 0.9123 0.9206

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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Finetuned from

Space using lidiapierre/distilbert-base-multi-fr-sexism 1