---
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 at the Autonomous University of Mexico  [UNAM](https://tesiunam.dgb.unam.mx/F?func=find-b-0&local_base=TES01). 
The model classifies for five (Psychology, Law, Química Farmaceutico Biológica, Actuaría, Economy) 
possible careers at the University of Mexico.
List of races from a text.


## 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 'inoid/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('inoid/unam_tesis_beto_finnetuning', use_fast=False)
 model = AutoModelForSequenceClassification.from_pretrained(
                   'inoid/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 Tesis with BETO finetunning classify },
  author={Cañete, Isahías and López, Dionis and Clavel, Yisell and López López, Ximena Yeraldin },
  booktitle={Somos NLP Hackaton 2022},
  year={2022}
}
```