Merge branch 'main' of https://huggingface.co/hackathon-pln-es/unam_tesis_BETO_finnetuning into main

README.md

@@ -1,20 +1,21 @@
 ---
 annotations_creators: 
+- inoid
 - MajorIsaiah
 - Ximyer
 - clavel
-- inoid
-language_creators: [found]
-languages: [es]
-multilinguality: [monolingual]
-pretty_name: ''
-size_categories:
-- n=200
-source_datasets: [unam_tesis]
-task_categories: [text-classification]
-task_ids: [language-modeling ]
-license: apache-2.0
+tags: 
+- "transformers"
+- "text-classification"
+languages: "es"
+license: "apache-2.0"
+datasets: "unam_tesis"
+metrics: "accuracy"
+widget: 
+- text: "Introducción al análisis de riesgos competitivos bajo el enfoque de la función de incidencia acumulada (FIA) y su aplicación con R"
+- text: "Asociación del polimorfismo rs1256031 del receptor beta de estrógenos en pacientes con diabetes tipo 2"
 ---
+
 # Unam_tesis_beto_finnetuning: Unam's thesis classification with BETO 
 
 This model is created from the finetuning of the pre-model
@@ -25,7 +26,7 @@ possible careers at the UNAM.
 
 ## Training Dataset 
 
-1000 documents (Thesis introduction, Author´s first name, Author´s last name, Thesis title, Year, Career )
+1000 documents (Thesis introduction, Author´s first name, Author´s last name, Thesis title, Year, Career)
 
 |     Careers |  Size         | 
 |--------------|----------------------|
@@ -35,10 +36,10 @@ possible careers at the UNAM.
 |  Psicología|   200    | 
 |  Química Farmacéutico Biológica|   200    | 
 
-
 ## 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 '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. 
+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. 
+
 ```python
 
  tokenizer = AutoTokenizer.from_pretrained('hiiamsid/BETO_es_binary_classification', use_fast=False)
@@ -53,10 +54,9 @@ For further details on how to use unam_tesis_beto_finnetuning you can visit the
 
 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)
+[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:
 
@@ -69,7 +69,6 @@ To cite this resource in a publication please use the following:
 }
 ```
 
-
 ## Team members
 - Isaac Isaías López López ([MajorIsaiah](https://huggingface.co/MajorIsaiah))
 - Dionis López Ramos ([inoid](https://huggingface.co/inoid))