Update loading script v1.2

corpus-carolina.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 """Carolina Corpus"""
 
+from collections import defaultdict
 from lxml import etree
 import os
 import datasets
@@ -30,7 +31,7 @@ robust volume of texts of varied typology in contemporary Brazilian Portuguese
 
 
 _CITATION = r"""
-@misc{corpusCarolinaV1.1,
+@misc{corpusCarolinaV1.2,
     title={
         Carolina:
         The Open Corpus for Linguistics and Artificial Intelligence},
@@ -52,7 +53,7 @@ _CITATION = r"""
         Palma, Mayara Feliciano},
     howpublished={\url{https://sites.usp.br/corpuscarolina/corpus}},
     year={2022},
-    note={Version 1.1 (Ada)},
+    note={Version 1.2 (Ada)},
 }
 """
 
@@ -81,13 +82,13 @@ def _taxonomies():
         leg="legislative branch",
         pub="public domain works",
         soc="social media",
-        uni="university_domains",
+        uni="university domains",
         wik="wikis",
     )
 
 
-_VERSION = "1.1.0"
-_CORPUS_URL = "corpus/{taxonomy}/"
+_VERSION = "1.2.0"
+_CORPUS_URL = "corpus/{tax}/"
 _CHECKSUM_FNAME = _CORPUS_URL + "checksum.sha256"
 
 
@@ -140,6 +141,7 @@ class Carolina(datasets.GeneratorBasedBuilder):
 
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
+            supervised_keys=None,
             homepage=_HOMEPAGE,
             citation=_CITATION,
             features=features,
@@ -153,23 +155,21 @@ class Carolina(datasets.GeneratorBasedBuilder):
         else:
             taxonomies = [_taxonomies()[self.config.taxonomy]]
 
-        zip_urls = dict()
-        for taxonomy in taxonomies:
-            # download checksum file
-            checksum_path = _CHECKSUM_FNAME.format(taxonomy=taxonomy)
-            checksum_path = dl_manager.download(checksum_path)
-
-            tax_url = _CORPUS_URL.format(taxonomy=taxonomy)
-
-            # extract and build zip urls
-            with open(checksum_path, encoding="utf-8") as cfile:
+        # download checksum files
+        checksum_urls = {t: _CHECKSUM_FNAME.format(tax=t) for t in taxonomies}
+        checksum_paths = dl_manager.download(checksum_urls)
+        
+        # prepare xml file name and zip urls
+        zip_urls = dict()   # xml
+        for tax, cpath in checksum_paths.items():
+            tax_path = _CORPUS_URL.format(tax=tax)
+            with open(cpath, encoding="utf-8") as cfile:
                 for line in cfile:
-                    fname = line.split()[1]
-                    if fname.endswith(".xml.zip"):
-                        zip_url = tax_url + fname           # download url
-                        fname = os.path.split(fname)[1]     # removes subdirs
-                        fname = fname[:-4]                  # removes .zip
-                        zip_urls[fname] = zip_url           # xml -> zip url
+                    xml_tax_path = line.split()[1]                  # xml file inside taxonomy
+                    zip_fname = xml_tax_path + ".zip"               # zip file inside taxonomy
+                    xml_fname = xml_tax_path.split('/')[-1]         # xml file name only
+                    zip_fpath = os.path.join(tax_path, zip_fname)   # path inside corpus
+                    zip_urls[xml_fname] = zip_fpath                 # xml file -> zip path
 
         # extractions are made in cache folders and
         # the path returned is the folder path, not the
@@ -200,11 +200,17 @@ class Carolina(datasets.GeneratorBasedBuilder):
             # parse xml file
             for _, tei in etree.iterparse(path, **parser_params):
                 header = tei.find(f"{TEI_NS}teiHeader")
+                
+                meta = etree.tostring(
+                    header, encoding="utf-8").decode("utf-8")
+                text = ' '.join([e.text
+                    for e in tei.findall(f".//{TEI_NS}body/{TEI_NS}p")
+                    if e.text is not None
+                ])
 
                 example = {
-                    "meta": etree.tostring(
-                        header, encoding="utf-8").decode("utf-8"),
-                    "text": tei.find(f".//{TEI_NS}body/{TEI_NS}p").text
+                    "meta": meta,
+                    "text": text
                 }
                 yield _key, example
                 _key += 1