readme: ---
  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 Hugging Face Transformers library, starting with the Quickstart section. The UNAM tesis model 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 model can be found next. 
  
  ```python
  
   tokenizer = AutoTokenizer.from_pretrained('hiiamsid/BETO_es_binary_classification', use_fast=False)
   model = AutoModelForSequenceClassification.from_pretrained(
                     'hackathon-pln-es/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")
  
  ```
  
  
  ## Citation
  
  To cite this resource in a publication please use the following:
  
  [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; Clavel Quintero, Yisel; López Ramos, Dionis & López López, Ximena Yeraldin},
    booktitle={Somos NLP Hackaton 2022},
    year={2022}
  }
  ```
  
  ## Team members
  - Isaac Isaías López López ([MajorIsaiah](https://huggingface.co/MajorIsaiah))
  - Dionis López Ramos ([inoid](https://huggingface.co/inoid))
  - Yisel Clavel Quintero ([clavel](https://huggingface.co/clavel))
  - Ximena Yeraldin López López ([Ximyer](https://huggingface.co/Ximyer))