Update README.md

README.md

@@ -20,19 +20,64 @@ inference:
 ---
 
 # Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
+
+## Table of contents
+<details>
+<summary>Click to expand</summary>
+
+- [Model description](#model-description)
+- [Intended uses and limitations](#intended-use)
+- [How to use](#how-to-use)
+- [Limitations and bias](#limitations-and-bias)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Additional information](#additional-information)
+  - [Author](#author)
+  - [Contact information](#contact-information)
+  - [Copyright](#copyright)
+  - [Licensing information](#licensing-information)
+  - [Funding](#funding)
+  - [Citing information](#citing-information)
+  - [Disclaimer](#disclaimer)
+  
+## Model description  
 RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large 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.
 
 Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-large-bne
 
-## Dataset
+## Intended uses and limitations
+
+## How to use
+
+## Limitations and bias
+At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
+
+## Training
 The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).
 
-## Evaluation and results
+## Evaluation 
 F1 Score: 0.8998
 
 For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish).
 
-## Citing 
+## Additional information
+
+### Author
+Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
+
+### Contact information
+For further information, send an email to <[email protected]>
+
+### Copyright
+Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
+
+### Licensing information
+[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.
+
+## Citing information
 If you use this model, please cite our [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405):
 ```
 @article{,
@@ -50,21 +95,8 @@ Intelligence (SEDIA) within the framework of the Plan-TL.},
    url = {https://upcommons.upc.edu/handle/2117/367156#.YyMTB4X9A-0.mendeley},
    year = {2022},
 }
-
 ```
 
-## Copyright
-
-Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
-
-## Licensing information
-
-[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.
-
 ## Disclaimer
 
 The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.