
# 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))
{"license": "apache-2.0", "tags": ["transformers", "text-classification"], "datasets": "unam_tesis", "metrics": "accuracy", "annotations_creators": ["inoid", "MajorIsaiah", "Ximyer", "clavel"], "languages": "es", "widget": [{"text": "Introducci\u00f3n al an\u00e1lisis de riesgos competitivos bajo el enfoque de la funci\u00f3n de incidencia acumulada (FIA) y su aplicaci\u00f3n con R"}, {"text": "Asociaci\u00f3n del polimorfismo rs1256031 del receptor beta de estr\u00f3genos en pacientes con diabetes tipo 2"}]}
transformers pytorch text-classification dataset:unam_tesis license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #text-classification #dataset-unam_tesis #license-apache-2.0 #endpoints_compatible #has_space #region-us 

Unam\_tesis\_beto\_finnetuning: Unam's thesis classification with BETO
======================================================================


This model is created from the finetuning of the pre-model
for Spanish BETO, using PyTorch framework,
and trained with a set of theses of the National Autonomous University of Mexico (UNAM).
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)



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.


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


[UNAM's Tesis with BETO finetuning classify] (URL


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


Team members
------------


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



TAGS
#transformers #pytorch #text-classification #dataset-unam_tesis #license-apache-2.0 #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #text-classification #dataset-unam_tesis #license-apache-2.0 #endpoints_compatible #has_space #region-us 
