Clean script

catalan_textual_corpus.py

@@ -14,27 +14,22 @@
 # limitations under the License.
 """Catalan Textual Corpus."""
 
-
-import csv
-import json
 import os
 
 import datasets
 
 
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
+@misc{armengolestape2021multilingual,
+      title={Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan},
+      author={Jordi Armengol{-}Estap{\'{e}} and Casimiro Pio Carrino and Carlos Rodriguez-Penagos and Ona de Gibert Bonet and Carme Armentano{-}Oller and Aitor Gonzalez{-}Agirre and Maite Melero and Marta Villegas},
+      year={2021},
+      eprint={2107.07903},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
 }
 """
 
-# TODO: Add description of the dataset here
-# You can copy an official description
 _DESCRIPTION = """\
 The Catalan Textual Corpus is a 1760-million-token web corpus of Catalan built from several sources: existing corpus such as DOGC, CaWac (non-dedup version), Oscar (unshuffled version), Open Subtitles, Catalan Wikipedia; and three brand new crawlings: the Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains; the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government; and the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the Catalan News Agency.
 
@@ -54,31 +49,9 @@ class CatalanTextualCorpus(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.0.0")
 
     def _info(self):
-        # # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        # if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-        #     features = datasets.Features(
-        #         {
-        #             "sentence": datasets.Value("string"),
-        #             "option1": datasets.Value("string"),
-        #             "answer": datasets.Value("string")
-        #             # These are the features of your dataset like images, labels ...
-        #         }
-        #     )
-        # else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-        #     features = datasets.Features(
-        #         {
-        #             "sentence": datasets.Value("string"),
-        #             "option2": datasets.Value("string"),
-        #             "second_domain_answer": datasets.Value("string")
-        #             # These are the features of your dataset like images, labels ...
-        #         }
-        #     )
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            features=None,
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
+            features=datasets.Features({"text": datasets.Value("string")}),
             supervised_keys=None,
             homepage=_HOMEPAGE,
             license=_LICENSE,
@@ -90,28 +63,10 @@ class CatalanTextualCorpus(datasets.GeneratorBasedBuilder):
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": os.path.join(data_dir, "corpus", "catalan_textual_corpus.txt"),
-                    # "split": "train",
                 },
             ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.TEST,
-            #     # These kwargs will be passed to _generate_examples
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "test.jsonl"),
-            #         "split": "test"
-            #     },
-            # ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.VALIDATION,
-            #     # These kwargs will be passed to _generate_examples
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
-            #         "split": "dev",
-            #     },
-            # ),
         ]
 
     def _generate_examples(self, filepath):