Update README.md

README.md

@@ -1,76 +1,76 @@
----
-annotations_creators: 
-- inoid
-- MajorIsaiah
-- Ximyer
-- clavel
-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
-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 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)
- model = AutoModelForSequenceClassification.from_pretrained(
-                   'hackathon-pln-e/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")
-
-```
-
-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)
-
-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 and López Ramos, Dionis and Clavel Quintero, Yisel and 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))
+---
+annotations_creators: 
+- inoid
+- MajorIsaiah
+- Ximyer
+- clavel
+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
+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 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)
+ model = AutoModelForSequenceClassification.from_pretrained(
+                   'hackathon-pln-e/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")
+
+```
+
+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)
+
+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))