Update README.md

README.md

@@ -29,19 +29,20 @@ widget:
 
 - [Overview](#overview)
 - [Model Description](#model-description)
-- [How to Use](#how-to-use)
 - [Intended Uses and Limitations](#intended-uses-and-limitations)
+- [How to Use](#how-to-use)
+- [Limitations and bias](#limitations-and-bias)
 - [Training](#training)
   - [Training Data](#training-data)
   - [Training Procedure](#training-procedure)
 - [Additional Information](#additional-information)
-   - [Authors](#authors)
-   - [Citation Information](#citation-information)
-   - [Contact Information](#contact-information)
-   - [Funding](#funding)
-   - [Licensing Information](#licensing-information)
-   - [Copyright](#copyright)
-   - [Disclaimer](#disclaimer)
+  - [Contact Information](#contact-information)
+  - [Copyright](#copyright)
+  - [Licensing Information](#licensing-information)
+  - [Funding](#funding)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+  - [Disclaimer](#disclaimer)
 
 </details>
 
@@ -54,6 +55,11 @@ widget:
 ## Model Description
 **GPT2-large-bne** is a transformer-based model for the Spanish language. It is based on the [GPT-2](http://www.persagen.com/files/misc/radford2019language.pdf) model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the  [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
 
+
+## Intended Uses and Limitations
+
+You can use the raw model for text generation or fine-tune it to a downstream task.
+
 ## How to Use
 
 Here is how to use this model:
@@ -87,9 +93,7 @@ Here is how to use this model to get the features of a given text in PyTorch:
 torch.Size([1, 14, 1280])
 ```
 
-## Intended Uses and Limitations
-
-You can use the raw model for text generation or fine-tune it to a downstream task.
+## Limitations and bias
 
 The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
 unfiltered content from the internet, which is far from neutral. Here's an example of how the model can have biased predictions:
@@ -141,9 +145,22 @@ The training lasted a total of 10 days with 32 computing nodes each one with 4 N
  
 ## Additional Information
 
-### Authors 
+### Contact Information
+
+For further information, send an email to <plantl-gob-es@bsc.es>
+
+### Copyright
+
+Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
+
+### Licensing Information
+
+This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Funding
+
+This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
 
-The Text Mining Unit from Barcelona Supercomputing Center.
 
 ### Citation Information
 If you use this model, please cite our [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405):
@@ -166,21 +183,10 @@ Intelligence (SEDIA) within the framework of the Plan-TL.},
 
 ```
 
-### Contact Information
+### Contributions
 
-For further information, send an email to <plantl-gob-es@bsc.es>
-
-### Funding
+[N/A]
 
-This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
-
-### Licensing Information
-
-This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
-
-### Copyright
-
-Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
 
 ### Disclaimer