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README.md
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- Inclusive Language
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- Text Neutralization
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- pytorch
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metrics:
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- sacrebleu
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model-index:
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- name:
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results:
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- task:
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type: Text2Text Generation
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name: Neutralization of texts in Spanish
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# args: {es} # Optional. Example: zh-CN
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metrics:
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- type: sacrebleu # Required. Example: wer
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value: 93.8347 # Required. Example: 20.90
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## Training and evaluation data
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The data used for the model training has been
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### Compiled sources
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- Inclusive Language
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- Text Neutralization
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- pytorch
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datasets:
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- hackathon-pln-es/neutral-es
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metrics:
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- sacrebleu
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model-index:
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- name: es_text_neutralizer
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results:
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- task:
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type: Text2Text Generation
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name: Neutralization of texts in Spanish
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dataset:
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type: hackathon-pln-es/neutral-es
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name: neutral-es
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metrics:
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- type: sacrebleu # Required. Example: wer
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value: 93.8347 # Required. Example: 20.90
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## Training and evaluation data
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One of the major challenges was to obtain a valuable dataset that would suit our purpose, therefore, the team opted to dedicate a considerable amount of time to build it from a scratch.
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The data used for the model training has been created form a compilation of sources, obtained from a series of guidelines and manuals issued by Spanish Ministry of Health, Social Services and Equality in the matter of the usage of non-sexist language, stipulated in this linked [document:](https://www.inmujeres.gob.es/servRecursos/formacion/GuiasLengNoSexista/docs/Guiaslenguajenosexista_.pdf):
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### Compiled sources
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