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--- |
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annotations_creators: |
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- inoid |
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- MajorIsaiah |
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- Ximyer |
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- clavel |
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tags: |
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- "transformers" |
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- "text-classification" |
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languages: "es" |
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license: "apache-2.0" |
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datasets: "unam_tesis" |
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metrics: "accuracy" |
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widget: |
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- text: "Introducción al análisis de riesgos competitivos bajo el enfoque de la función de incidencia acumulada (FIA) y su aplicación con R" |
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- text: "Asociación del polimorfismo rs1256031 del receptor beta de estrógenos en pacientes con diabetes tipo 2" |
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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 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. |
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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-es/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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## Citation |
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To cite this resource in a publication please use the following: |
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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; Clavel Quintero, Yisel; López Ramos, Dionis & 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)) |
