Update README.md

README.md

@@ -27,29 +27,35 @@ widget:
 <details>
 <summary>Click to expand</summary>
 
+- [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 corpora and preprocessing](#training-corpora-and-preprocessing)
-- [Tokenization and pre-training](#tokenization-and-pre-training)
-- [Citation Information](#citing)
-- [Licensing Information](#licensing-information)
-- [Copyright](#copyright)
-- [Funding](#funding)
-- [Disclaimer](#disclaimer)
+- [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)
+   - [Disclaimer](#disclaimer)
 
 </details>
 
-## Model Description
+## Overview
 
-**GPT2-base-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.
+- **Architecture:** gpt2-base-bne
+- **Language:** Spanish
+- **Task:** text-generation
+- **Data:** BNE
 
-## Intended Uses and Limitations
+## Model Description
 
-You can use the raw model for text generation or fine-tune it to a downstream task.
+**GPT2-base-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.
 
-### How to Use
+## How to Use
 
 Here is how to use this model:
 You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
@@ -82,7 +88,9 @@ Here is how to use this model to get the features of a given text in PyTorch:
 torch.Size([1, 14, 768])
 ```
 
-### Limitations and bias
+## Intended Uses and Limitations
+
+You can use the raw model for text generation or fine-tune it to a downstream task.
 
 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:
@@ -110,7 +118,10 @@ unfiltered content from the internet, which is far from neutral. Here's an examp
 
 ```
 
-## Training corpora and preprocessing 
+## Training
+
+### Training Data
+
 The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
 
 To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text.
@@ -121,10 +132,21 @@ Some of the statistics of the corpus:
 |---------|---------------------|------------------|-----------|
 | BNE     |         201,080,084 |  135,733,450,668 |     570GB |
 
-## Tokenization and pre-training 
-The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [GPT-2](http://www.persagen.com/files/misc/radford2019language.pdf) model with a vocabulary size of 50,262 tokens. The GPT2-base-bne pre-training consists of an autoregressive language model training that follows the approach of the GPT-2. The training lasted a total of 3 days with 16 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
+### Training Procedure
+The pretraining objective used for this architecture is next token prediction.
+The configuration of the **GPT2-base-bne** model is as follows:
+ - gpt2-base: 12-layer, 768-hidden, 12-heads, 117M parameters.
+The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model with a vocabulary size of 50,262 tokens. 
+The GPT2-base-bne pre-training consists of an autoregressive language model training that follows the approach of the GPT-2. 
+The training lasted a total of 3 days with 16 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
+
+## Additional Information
 
-## Citing 
+### Authors 
+
+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):
 ```
 @article{,
@@ -145,29 +167,37 @@ Intelligence (SEDIA) within the framework of the Plan-TL.},
 
 ```
 
-## Licensing information
+### Contact Information
 
-[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+For further information, send an email to <plantl-gob-es@bsc.es>
 
-## Copyright
+### Funding
 
-Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
+This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
 
-## Funding
+### Licensing information
 
-This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
+This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Copyright
 
-## Disclaimer
+Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
+
+### Disclaimer
+
+<details>
+<summary>Click to expand</summary>
 
 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.
 
-When third  parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence.
+When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
 
-In no event shall the owner of the models (SEDIA – State Secretariat for digitalization and artificial intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
+In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
 
 
 Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
 
 Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
 
-En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
+En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
+</details>