Update README.md

README.md

@@ -7,15 +7,21 @@ language:
 license: apache-2.0
 
 tags:
+
 - "national library of spain"
+
 - "spanish"
+
 - "bne"
+
 - "roberta-large-bne"
 
 datasets:
+
 - "bne"
 
 metrics:
+
 - "ppl"
 
 widget:
@@ -25,7 +31,7 @@ widget:
 - text: "Hay base legal dentro del marco <mask> actual."
 
 ---
-# RoBERTa large trained with data from National Library of Spain (BNE)
+# RoBERTa large trained with data from the National Library of Spain (BNE)
 
 ## Table of Contents
 <details>
@@ -58,13 +64,15 @@ widget:
 - **Data:** BNE
 
 ## 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.
+The **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.
 
 ## Intended uses and limitations
+The **roberta-large-bne** model is ready-to-use only for masked language modeling to perform the Fill Mask task (try the inference API or read the next section).
+However, it is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification, or Named Entity Recognition.
 You can use the raw model for fill mask or fine-tune it to a downstream task.
 
 ## How to use
-You can use this model directly with a pipeline for fill mask. Since the generation relies on some randomness, we set a seed for reproducibility:
+Here is how to use this model:
 
 ```python
 >>> from transformers import pipeline
@@ -115,7 +123,7 @@ At the time of submission, no measures have been taken to estimate the bias and
 
 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.
+To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among others, sentence splitting, language detection, filtering of bad-formed sentences, and deduplication of repetitive contents. During the process, document boundaries are kept. This resulted in 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting in 570GB of text.
 
 Some of the statistics of the corpus:
 
@@ -124,20 +132,14 @@ Some of the statistics of the corpus:
 | BNE     |         201,080,084 |  135,733,450,668 |     570GB |
 
 ### Training procedure
-The configuration of the **RoBERTa-large-bne** model is as follows:
-
- - RoBERTa-l: 24-layer, 1024-hidden, 16-heads, 355M parameters.
-
-The pretraining objective used for this architecture is masked language modeling without next sentence prediction.
-
-The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. 
+The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens.
 
-The RoBERTa-large-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa base. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
+The **roberta-large-bne** pre-training consists of a masked language model training, that follows the approach employed for the RoBERTa large. The training lasted a total of 96 hours with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
 
 ## Evaluation
 
 When fine-tuned on downstream tasks, this model achieves the following results:
-| Dataset      | Metric   | [**RoBERTa-l**](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne)   |
+| Dataset      | Metric   | [**RoBERTa-large**](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne)   |
 |--------------|----------|------------|
 | MLDoc        | F1       |     0.9702 |
 | CoNLL-NERC   | F1       |     0.8823 |
@@ -160,13 +162,13 @@ Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
 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)
+Copyright by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) (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.
+This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx) within the framework of the Plan-TL.
 
 ### Citation information