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 à 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
