| _misogBETO is a domain adaptation of a [Spanish BERT](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) 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](https://github.com/fmplaza/hate-speech-spanish-lexicons/blob/master/misogyny_lexicon.txt). | |
| The base corpus used for domain adaptation was the [Spanish Hate Speech Superset](https://huggingface.co/datasets/manueltonneau/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 | |
| ```python | |
| 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") | |
| ``` |