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---
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annotations_creators:
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- MajorIsaiah
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- Ximyer
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- clavel
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- inoid
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language_creators: [found]
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languages: [es]
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multilinguality: [monolingual]
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pretty_name: ''
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size_categories:
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- n=200
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source_datasets: [unam_tesis]
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task_categories: [text-classification]
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task_ids: [language-modeling ]
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license: apache-2.0
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---
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# Unam_tesis_beto_finnetuning: Unam's thesis classification with BETO
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This model is created from the finetuning of the pre-model
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for Spanish [BETO] (https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased), using PyTorch framework,
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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).
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The model classifies a text into for five (Psicología, Derecho, Química Farmacéutico Biológica, Actuaría, Economía)
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possible careers at the UNAM.
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## Training Dataset
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1000 documents (Thesis introduction, Author´s first name, Author´s last name, Thesis title, Year, Career )
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| Careers | Size |
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|--------------|----------------------|
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| Actuaría | 200 |
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| Derecho| 200 |
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| Economía| 200 |
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| Psicología| 200 |
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| Química Farmacéutico Biológica| 200 |
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## Example of use
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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.
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```python
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tokenizer = AutoTokenizer.from_pretrained('hiiamsid/BETO_es_binary_classification', use_fast=False)
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model = AutoModelForSequenceClassification.from_pretrained(
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'hackathon-pln-e/unam_tesis_BETO_finnetuning', num_labels=5, output_attentions=False,
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output_hidden_states=False)
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
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classificationResult = pipe("Análisis de las condiciones del aprendizaje desde casa en los alumnos de preescolar y primaria del municipio de Nicolás Romero")
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```
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To cite this resource in a publication please use the following:
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## Citation
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[UNAM's Tesis with BETO finetuning classify ](https://huggingface.co/hackathon-pln-es/unam_tesis_BETO_finnetuning)
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To cite this resource in a publication please use the following:
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```
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@inproceedings{SpanishNLPHackaton2022,
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title={UNAM's Theses with BETO fine-tuning classify },
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author={López López, Isaac Isaías and López Ramos, Dionis and Clavel Quintero, Yisel and López López, Ximena Yeraldin },
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booktitle={Somos NLP Hackaton 2022},
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year={2022}
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}
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```
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## Team members
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- Isaac Isaías López López ([MajorIsaiah](https://huggingface.co/MajorIsaiah))
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- Dionis López Ramos ([inoid](https://huggingface.co/inoid))
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- Yisel Clavel Quintero ([clavel](https://huggingface.co/clavel))
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- Ximena Yeraldin López López ([Ximyer](https://huggingface.co/Ximyer)) |
