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--- |
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license: apache-2.0 |
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tags: |
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- text-classification |
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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: misogynistic-statements-classification-model |
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results: [] |
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widget: |
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- text: Las mujeres deben ser madres antes que nada |
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example_title: Machista |
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- text: >- |
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Las mujeres tienen el mismo potencial y habilidades para los negocios que |
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los hombres |
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example_title: No machista |
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datasets: |
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- glombardo/misogynistic-statements-classification |
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language: |
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- es |
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output_data: |
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- format: class |
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- class_labels: ["Sexist", "Non-sexist"] |
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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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# Misogynistic statements classification model |
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**Model that classifies text as sexist or non-sexist.** |
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the [misogynistic-statements-classification dataset](https://huggingface.co/datasets/glombardo/misogynistic-statements-classification). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2493 |
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- Accuracy: 0.9524 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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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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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
