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Description

This model is a fine-tuned version of BETO (spanish bert) that has been trained on the Datathon Against Racism dataset (2022)

We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022) We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:

| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 | |--- |--- |--- |--- |--- | | raw-label | raw-label-epoch-1 | raw-label-epoch-2 | raw-label-epoch-3 | raw-label-epoch-4 | | m-vote-strict | m-vote-strict-epoch-1 | m-vote-strict-epoch-2 | m-vote-strict-epoch-3 | m-vote-strict-epoch-4 | | m-vote-nonstrict | m-vote-nonstrict-epoch-1 | m-vote-nonstrict-epoch-2 | m-vote-nonstrict-epoch-3 | m-vote-nonstrict-epoch-4 | | regression-w-m-vote | regression-w-m-vote-epoch-1 | regression-w-m-vote-epoch-2 | regression-w-m-vote-epoch-3 | regression-w-m-vote-epoch-4 | | w-m-vote-strict | w-m-vote-strict-epoch-1 | w-m-vote-strict-epoch-2 | w-m-vote-strict-epoch-3 | w-m-vote-strict-epoch-4 | | w-m-vote-nonstrict | w-m-vote-nonstrict-epoch-1 | w-m-vote-nonstrict-epoch-2 | w-m-vote-nonstrict-epoch-3 | w-m-vote-nonstrict-epoch-4 |

This model is m-vote-strict-epoch-4

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
  
model_name = 'm-vote-strict-epoch-4'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)

pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)

texts = [
    'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
    'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9965864419937134}, {'label': 'racist', 'score': 0.6058831214904785}]

For more details, see https://github.com/preyero/neatclass22

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