_misogBETO is a domain adaptation of a Spanish BERT language model, specifically adapted to the misogyny domain.
It was adapted using a guided lexical masking strategy during masked language model (MLM) pretraining. Instead of randomly masking tokens, we prioritized masking words appearing in a misogyny-specific lexicon. The base corpus used for domain adaptation was the Spanish Hate Speech Superset.
For training the model we used a batch size of 8, with a learning rate of 2e-5. We trained the model for four epochs using a NVIDIA GeForce RTX 5090 GPU.
Usage
from transformers import pipeline
pipe = pipeline("fill-mask", model="PeterPanecillo/_misogBETO")
text = pipe("Las [MASK] son adictivas.")
print(text)
Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("PeterPanecillo/_misogBETO")
model = AutoModelForMaskedLM.from_pretrained("PeterPanecillo/_misogBETO")