Update README.md

README.md

@@ -1,20 +1,22 @@
+--- 
+annotations_creators: "MajorIsaiah, Ximyer, clavel, inoid,"
+language_creators: found
+languages: 
+  - es
+license: 
+  - apache-2.0
+multilinguality: 
+  - monolingual
+size_categories: 
+  - n=200
+source_datasets: 
+  - unam_tesis
+task_categories: 
+  - text-classification
+task_ids: 
+  - language-modeling
 ---
-annotations_creators: 
-- 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
----
+
 # Unam_tesis_beto_finnetuning: Unam's thesis classification with BETO 
 
 This model is created from the finetuning of the pre-model
@@ -25,7 +27,7 @@ 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         | 
 |--------------|----------------------|
@@ -39,6 +41,7 @@ possible careers at the UNAM.
 ## 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)
@@ -53,13 +56,13 @@ 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:
 
+
 ```
 @inproceedings{SpanishNLPHackaton2022,
   title={UNAM's Theses with BETO fine-tuning classify },