Create BioNLP2021.py

BioNLP2021.py

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+import os
+
+import datasets
+import pandas as pd
+
+_CITATION = None
+
+_DESCRIPTION = """\
+MEDIQA @ NAACL-BioNLP 2021 -- Task 2: Multi-answer summarization
+https://sites.google.com/view/mediqa2021
+Biomedical Summarization Data
+The MEDIQA-AnS Dataset could be used for training.
+"""
+_HOMEPAGE = "https://github.com/abachaa/MEDIQA2021/tree/main/Task2"
+_LICENSE = None
+_DATA_URL = "https://huggingface.co/datasets/nbtpj/BioNLP2021/resolve/main/{split_name}.csv"
+           
+_SPLIT = ['train_mul', 'train_sig', 'validate', 'test']
+
+
+class BioNLP2021(datasets.BuilderConfig):
+    """BuilderConfig for GLUE."""
+
+    def __init__(self, data_url, **kwargs):
+        """BuilderConfig for BioNLP2021MAS
+        Args:
+          data_url: `string`, url to the dataset (word or raw level)
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(BioNLP2021, self).__init__(
+            version=datasets.Version(
+                "1.0.0",
+            ),
+            **kwargs,
+        )
+        self.data_url = data_url
+
+
+class Loader(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.1.0")
+    BUILDER_CONFIGS = BioNLP2021(name='BioNLP2021', data_url=_DATA_URL)
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "key": datasets.Value("string"),
+                    "summ_abs": datasets.Value("string"),
+                    "summ_ext": datasets.Value("string"),
+                    # These are the features of your dataset like images, labels ...
+                    # key,question,sum_abs,sum_ext,rating,section,article,text
+
+                }
+            ),
+            # 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=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        rs = []
+        for split in _SPLIT:
+          file= dl_manager.download_and_extract(_DATA_URL.format(split_name=split))
+          rs.append(
+              datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_file": file, "split": split },
+            ))
+        
+
+        return rs
+
+
+    def _generate_examples(self, data_file, split):
+        """Yields examples."""
+        with open(data_file, encoding="utf-8") as f:
+            f = pd.read_csv(f).to_dict('records')
+            for idx, row in enumerate(f):
+                yield idx, row