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@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+train.json filter=lfs diff=lfs merge=lfs -text

SQAC.py

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+# Loading script for the SQAC dataset.
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+bibtex
+@article{DBLP:journals/corr/abs-2107-07253,
+  author    = {Asier Guti{\'{e}}rrez{-}Fandi{\~{n}}o and
+               Jordi Armengol{-}Estap{\'{e}} and
+               Marc P{\`{a}}mies and
+               Joan Llop{-}Palao and
+               Joaqu{\'{\i}}n Silveira{-}Ocampo and
+               Casimiro Pio Carrino and
+               Aitor Gonzalez{-}Agirre and
+               Carme Armentano{-}Oller and
+               Carlos Rodr{\'{\i}}guez Penagos and
+               Marta Villegas},
+  title     = {Spanish Language Models},
+  journal   = {CoRR},
+  volume    = {abs/2107.07253},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2107.07253},
+  archivePrefix = {arXiv},
+  eprint    = {2107.07253},
+  timestamp = {Wed, 21 Jul 2021 15:55:35 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2107-07253.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+            """
+
+_DESCRIPTION = """
+This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment.
+
+The sources of the contexts are:
+
+* Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode). 
+
+* News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/). 
+
+* Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence] (https://creativecommons.org/licenses/by/4.0/legalcode). 
+
+This dataset can be used to build extractive-QA.
+               """
+
+_HOMEPAGE = """"""
+
+_URL = "https://huggingface.co/datasets/PlanTL-GOB-ES/SQAC/resolve/main/"
+_TRAINING_FILE = "train.json"
+_DEV_FILE = "dev.json"
+_TEST_FILE = "test.json"
+
+
+class SQACConfig(datasets.BuilderConfig):
+    """ Builder config for the SQAC dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SQAC.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SQACConfig, self).__init__(**kwargs)
+
+
+class SQAC(datasets.GeneratorBasedBuilder):
+    """SQAC Dataset."""
+
+    BUILDER_CONFIGS = [
+        SQACConfig(
+            name="SQAC",
+            #version=datasets.Version("1.0.1"),
+            description="SQAC dataset",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.features.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "answer_start": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            sqac_data = json.load(f)
+            for article in sqac_data["data"]:
+                title = article.get("title", "").strip()
+                for paragraph in article["paragraphs"]:
+                    context = paragraph["context"].strip()
+                    for qa in paragraph["qas"]:
+                        question = qa["question"].strip()
+                        id_ = qa["id"]
+
+                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                        answers = [answer["text"].strip() for answer in qa["answers"]]
+
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield id_, {
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }

train.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d5c76176646e2ae7bdcd8b5ec6f18349102a9363aa25ad7d0e48262d7480d43
+size 11042089