Update README.md

README.md

@@ -40,7 +40,18 @@ used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/ex
 
 ## Training corpora and preprocessing
 
-The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus (Clinical cases misc.):
+The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus (Clinical cases misc.). To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus (Clinical cases misc.) uncleaned. Essentially, the cleaning operations used are:
+
+- data parsing in different formats
+  - sentence splitting
+  - language detection
+  - filtering of ill-formed sentences 
+  - deduplication of repetitive contents
+  - keep the original document boundaries
+
+Then, the biomedical corpora are concatenated and further global deduplication among the biomedical corpora have been applied.
+Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus resulting in a medium-size biomedical-clinical corpus for Spanish composed of about 963M tokens. The table below shows some basic statistics of the individual cleaned corpora:
+
     
 | Name                                                                                    | No. tokens  | Description                                                                                                                                                                                                                                          |
 |-----------------------------------------------------------------------------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -48,23 +59,13 @@ The training corpus is composed of several biomedical corpora in Spanish, collec
 | Clinical cases misc.                                                                    | 102,855,267 | A miscellany of medical content, essentially clinical cases. Note that a clinical case report is different from a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document.                                                                                                                                                                                 |
 | [Scielo](https://github.com/PlanTL-SANIDAD/SciELO-Spain-Crawler)                        | 60,007,289  | Publications written in Spanish crawled from the Spanish SciELO server in 2017.                                                                                                                                       |
 | [BARR2_background](https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2) | 24,516,442  | Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines.                                                                                       |
-| Wikipedia_life_sciences                                                                 | 13,890,501  | Wikipedia articles belonging to the Life Sciences category crawled on 04/01/2021                                                                                                                                                                      |
+| Wikipedia_life_sciences                                                                 | 13,890,501  | Wikipedia articles crawled 04/01/2021 with the [Wikipedia API python library](https://pypi.org/project/Wikipedia-API/) starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content.                                                                                                                                                                    |
 | Patents                                                                                 | 13,463,387  | Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P".                                                        |
 | [EMEA](http://opus.nlpl.eu/download.php?f=EMEA/v3/moses/en-es.txt.zip)                  | 5,377,448   | Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency.                                                                                                                            |
 | [mespen_Medline](https://zenodo.org/record/3562536#.YTt1fH2xXbR)                        | 4,166,077   | Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature.  The collection of parallel resources are aggregated from the MedlinePlus source. |
 | PubMed                                                                                  | 1,858,966   | Open-access articles from the PubMed repository crawled in 2017.                                                                                                                                              |
 
-To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus (Clinical cases misc.) uncleaned. Essentially, the cleaning operations used are:
-
-- data parsing in different formats
-  - sentence splitting
-  - language detection
-  - filtering of ill-formed sentences 
-  - deduplication of repetitive contents
-  - keep the original document boundaries
 
-Then, the biomedical corpora are concatenated and further global deduplication among the corpora have been applied.
-Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus resulting in a medium-size biomedical-clinical corpus for Spanish composed of about 963M tokens.
 
 ## Evaluation and results