Upload becasv2.py

becasv2.py

@@ -0,0 +1,158 @@
+from datasets.utils import version
+"""TODO(squad_es): Add a description here."""
+
+
+import json
+
+import datasets
+
+
+# TODO(squad_es): BibTeX citation
+_CITATION = """\
+@article{2016arXiv160605250R,
+       author = {Casimiro Pio , Carrino and  Marta R. , Costa-jussa and  Jose A. R. , Fonollosa},
+        title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
+Question Answering}",
+      journal = {arXiv e-prints},
+         year = 2019,
+          eid = {arXiv:1912.05200v1},
+        pages = {arXiv:1912.05200v1},
+archivePrefix = {arXiv},
+       eprint = {1912.05200v2},
+}
+"""
+
+# TODO(squad_es_v1):
+_DESCRIPTION = """\
+automatic translation of the Stanford Question Answering Dataset (SQuAD) v2 into Spanish
+"""
+
+_URL = "https://raw.githubusercontent.com/EvelynQuevedo/becas/main/"
+print(_URL)
+_URLS_V1 = {
+    "train": _URL + "datos/data.json"
+
+    #"dev": _URL + "SQuAD-es-v1.1/dev-v1.1-es.json",
+}
+print(_URLS_V1)
+#_URLS_V2 = {
+ #   "train": _URL + "SQuAD-es-v2.0/train-v2.0-es.json",
+    #"dev": _URL + "SQuAD-es-v2.0/dev-v2.0-es.json",
+#}
+
+
+class SquadEsConfig(datasets.BuilderConfig):
+    """BuilderConfig for SQUADEsV2."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SQUADEsV2.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquadEsConfig, self).__init__(**kwargs)
+
+
+class SquadEs(datasets.GeneratorBasedBuilder):
+    """TODO(squad_es): Short description of my dataset."""
+
+    # TODO(squad_es): Set up version.
+    VERSION = datasets.Version("0.1.0")
+    BUILDER_CONFIGS = [
+        SquadEsConfig(
+            name="v1.1.0",
+            version=datasets.Version("1.1.0", ""),
+            description="Plain text Spanish squad version 1",
+        ),
+        #SquadEsConfig(
+         #   name="v2.0.0",
+          #  version=datasets.Version("2.0.0", ""),
+           # description="Plain text Spanish squad version 2",
+        #),
+    ]
+
+    def _info(self):
+        # TODO(squad_es): Specifies the datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                
+                    # These are the features of your dataset like images, labels ...
+                    "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"),
+                        }
+                    ),
+                }
+            ),
+            # 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.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/EvelynQuevedo/becas",
+        
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(squad_es): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+
+        # download and extract URLs
+        if self.config.name == "v1.1.0":
+            dl_dir = dl_manager.download_and_extract(_URLS_V1)
+            print(dl_dir)
+       # elif self.config.name == "v2.0.0":
+        #    dl_dir = dl_manager.download_and_extract(_URLS_V2)
+        else:
+            raise Exception("version does not match any existing one")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["train"]},
+            ),
+           # datasets.SplitGenerator(
+            #    name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+             #   gen_kwargs={"filepath": dl_dir["dev"]},
+            #),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(squad_es): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for example in data["data"]:
+                title = example.get("title", "").strip()
+                for paragraph in example["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"]]
+
+                        yield id_, {
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            
+                            },
+                           
+                        }