---
license: apache-2.0
---
# 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 for five (Psicología, Derecho, Química Farmacéutico Biológica, Actuaría, Economía) 
possible careers at the University of Mexico.
List of careers from a text.

## 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. 
```python

 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("El objetivo de esta tesis es elaborar un estudio de las condiciones asociadas al aprendizaje desde casa")

```

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](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))