metadata

annotations_creators:
  - inoid
  - MajorIsaiah
  - Ximyer
  - clavel
tags:
  - transformers
  - text-classification
languages: es
license: apache-2.0
datasets: unam_tesis
metrics: accuracy
widget:
  - text: >-
      Introducción al análisis de riesgos competitivos bajo el enfoque de la
      función de incidencia acumulada (FIA) y su aplicación con R
  - text: >-
      Asociación del polimorfismo rs1256031 del receptor beta de estrógenos en
      pacientes con diabetes tipo 2

Unam_tesis_beto_finnetuning: Unam's thesis classification with BETO

This model is created from the finetuning of the pre-model for Spanish [BETO] (https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased), using PyTorch framework, and trained with a set of theses of the National Autonomous University of Mexico (UNAM) (https://tesiunam.dgb.unam.mx/F?func=find-b-0&local_base=TES01). The model classifies a text into for five (Psicología, Derecho, Química Farmacéutico Biológica, Actuaría, Economía) possible careers at the UNAM.

Training Dataset

1000 documents (Thesis introduction, Author´s first name, Author´s last name, Thesis title, Year, Career)

Careers	Size
Actuaría	200
Derecho	200
Economía	200
Psicología	200
Química Farmacéutico Biológica	200

Example of use

For further details on how to use unam_tesis_BETO_finnetuning you can visit the Huggingface Transformers library, starting with the Quickstart section. Unam_tesis models can be accessed simply as 'hackathon-pln-e/unam_tesis_BETO_finnetuning' by using the Transformers library. An example of how to download and use the models on this page can be found in this colab notebook.


 tokenizer = AutoTokenizer.from_pretrained('hiiamsid/BETO_es_binary_classification', use_fast=False)
 model = AutoModelForSequenceClassification.from_pretrained(
                   'hackathon-pln-e/unam_tesis_BETO_finnetuning', num_labels=5, output_attentions=False,
                  output_hidden_states=False)
 pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
 
 classificationResult = pipe("Análisis de las condiciones del aprendizaje desde casa en los alumnos de preescolar y primaria del municipio de Nicolás Romero")

To cite this resource in a publication please use the following:

Citation

[UNAM's Tesis with BETO finetuning classify] (https://huggingface.co/hackathon-pln-es/unam_tesis_BETO_finnetuning)

To cite this resource in a publication please use the following:

@inproceedings{SpanishNLPHackaton2022,
  title={UNAM's Theses with BETO fine-tuning classify },
  author={López López, Isaac Isaías and López Ramos, Dionis and Clavel Quintero, Yisel and López López, Ximena Yeraldin },
  booktitle={Somos NLP Hackaton 2022},
  year={2022}
}

Team members

Isaac Isaías López López (MajorIsaiah)
Dionis López Ramos (inoid)
Yisel Clavel Quintero (clavel)
Ximena Yeraldin López López (Ximyer)