---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: test-trainer
  results: []
---


# DistilBERT base FR sexism detection

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co./distilbert-base-multilingual-cased) on the [lidiapierre/fr_sexism_labelled](https://huggingface.co./datasets/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 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
- 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