output

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.6405

Model description

This model helps in Sexism Categorization in Tweets Many facets of a woman’s life may be the focus of sexist attitudes including domestic and parenting roles, career opportunities, sexual image, and life expectations, to name a few. Automatically detecting which of these facets of women are being more frequently attacked in social networks will facilitate the development of policies to fight against sexism. According to this, each sexist tweet must be categorized in one or more of the following categories

IDEOLOGICAL AND INEQUALITY: The text discredits the feminist movement, rejects inequality between men and women, or presents men as victims of gender-based oppression.

“Mi hermana y mi madre se burlan de mí por defender todo el tiempo los derechos de todos y me acaban de decir feminazi, la completaron”. “I think the whole equality thing is getting out of hand. We are different, thats how were made!”. STEREOTYPING AND DOMINANCE: The text expresses false ideas about women that suggest they are more suitable to fulfill certain roles (mother, wife, family caregiver, faithful, tender, loving, submissive, etc.), or inappropriate for certain tasks (driving, hardwork, etc), or claims that men are somehow superior to women.

“@Paula2R @faber_acuria A las mujeres hay que amarlas…solo eso… Nunca las entenderás.”. “Most women no longer have the desire or the knowledge to develop a high quality character, even if they wanted to.”. OBJECTIFICATION: The text presents women as objects apart from their dignity and personal aspects, or assumes or describes certain physical qualities that women must have in order to fulfill traditional gender roles (compliance with beauty standards, hypersexualization of female attributes, women’s bodies at the disposal of men, etc.).

““Pareces una puta con ese pantalón” - Mi hermano de 13 cuando me vio con un pantalón de cuero”. “Don’t get married than blame all woman for your poor investment. You should of got a hooker but instead you choose to go get a wedding ring.”. SEXUAL VIOLENCE: Sexual suggestions, requests for sexual favors or harassment of a sexual nature (rape or sexual assault) are made.

“#MeToo Estas 4 no han conseguido su objetivo.El juez estima que se abrieron de patas https://t.co/GSHiiwqY6Aánta lagartona hay en este \metoo"!👇🏻👇🏻🤔🤔🤔 https://t.co/8t5VmFIUFn"” “fuck that cunt, I would with my fist”. MISOGYNY AND NON-SEXUAL VIOLENCE: The text expressses hatred and violence towards women.

“Las mujeres de hoy en dia te enseñar a querer… estar soltero” “Some woman are so toxic they don’t even know they are draining everyone around them in poison. If you lack self awareness you won’t even notice how toxic you really are”.

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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: 1

Training results

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu121
Tokenizers 0.19.1

Abhi964
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sexism-identification-in-tweets