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---
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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 at the 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 for five (Psychology, Law, Química Farmaceutico Biológica, Actuaría, Economy)
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possible careers at the University of Mexico.
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List of races from a text.
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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 '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.
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```python
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tokenizer = AutoTokenizer.from_pretrained('inoid/unam_tesis_beto_finnetuning', use_fast=False)
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model = AutoModelForSequenceClassification.from_pretrained(
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'inoid/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("El objetivo de esta tesis es elaborar un estudio de las condiciones asociadas al aprendizaje desde casa")
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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 Tesis with BETO finetunning classify },
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author={Cañete, Isahías and López, Dionis and Clavel, Yisell 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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``` |
