readme: ---
  language: es
  ---
  
  # es-seq2seq-gender (decoder)
  
  This is a seq2seq model (decoder half) to "flip" gender in Spanish sentences.
  The model can augment your existing Spanish data, or generate counterfactuals
  to test a model's decisions (would changing the gender of the subject or speaker change output?).
  
  Intended Examples:
  
  - el profesor viejo => la profesora vieja (article, noun, adjective all flip)
  - una actriz => un actor (irregular noun)
  - el lingüista => la lingüista (irregular noun)
  - la biblioteca => la biblioteca (no person, no flip)
  
  People's names are unchanged in this version, but you can use packages
  such as https://pypi.org/project/gender-guesser/
  
  ## Sample code
  
  https://colab.research.google.com/drive/1Ta_YkXx93FyxqEu_zJ-W23PjPumMNHe5
  
  ```
  import torch
  from transformers import AutoTokenizer, EncoderDecoderModel
  
  model = EncoderDecoderModel.from_encoder_decoder_pretrained("monsoon-nlp/es-seq2seq-gender-encoder", "monsoon-nlp/es-seq2seq-gender-decoder")
  tokenizer = AutoTokenizer.from_pretrained('monsoon-nlp/es-seq2seq-gender-decoder') # all are same as BETO uncased original
  
  input_ids = torch.tensor(tokenizer.encode("la profesora vieja")).unsqueeze(0)
  generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)
  tokenizer.decode(generated.tolist()[0])
  > '[PAD] el profesor viejo profesor viejo profesor...'
  ```
  
  ## Training
  
  I originally developed 
  <a href="https://github.com/MonsoonNLP/el-la">a gender flip Python script</a>
  with 
  <a href="https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased">BETO</a>,
  the Spanish-language BERT from Universidad de Chile,
  and spaCy to parse dependencies in sentences.
  
  More about this project: https://medium.com/ai-in-plain-english/gender-bias-in-spanish-bert-1f4d76780617
  
  The seq2seq model is trained on gender-flipped text from that script run on the
  <a href="https://huggingface.co/datasets/muchocine">muchocine dataset</a>, 
  and the first 6,853 lines from the
  <a href="https://oscar-corpus.com/">OSCAR corpus</a>
  (Spanish ded-duped).
  
  The encoder and decoder started with weights and vocabulary from BETO (uncased).
  
  ## Non-binary gender
  
  This model is useful to generate male and female text samples, but falls
  short of capturing gender diversity in the world and in the Spanish
  language. Some communities prefer the plural -@s to represent
  -os and -as, or -e and -es for gender-neutral or mixed-gender plural,
  or use fewer gendered professional nouns (la juez and not jueza). This is not yet
  embraced by the Royal Spanish Academy 
  and is not represented in the corpora and tokenizers used to build this project.
  
  This seq2seq project and script could, in the future, help generate more text samples 
  and prepare NLP models to understand us all better.
  
  #### Sources
  
  - https://www.nytimes.com/2020/04/15/world/americas/argentina-gender-language.html
  - https://www.washingtonpost.com/dc-md-va/2019/12/05/teens-argentina-are-leading-charge-gender-neutral-language/?arc404=true
  - https://www.theguardian.com/world/2020/jan/19/gender-neutral-language-battle-spain
  - https://es.wikipedia.org/wiki/Lenguaje_no_sexista
  - https://remezcla.com/culture/argentine-company-re-imagines-little-prince-gender-neutral-language/
  