add data and ldkp3k.py

ldkp3k.py

@@ -0,0 +1,119 @@
+import csv
+import json
+import os
+
+import datasets
+from typing import List, Any
+
+# _SPLIT = ['train', 'test', 'valid']
+_CITATION = """\
+TBA
+"""
+
+
+_DESCRIPTION = """\
+This new dataset is designed to solve kp NLP task and is crafted with a lot of care.
+"""
+
+
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+
+_URLS = {
+    "test": "data/test.jsonl",
+    "train": "train.jsonl",
+    "valid": "data/valid.jsonl"
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class LDKP3k(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="small", version=VERSION, description="This part of my dataset covers long document"),
+        datasets.BuilderConfig(name="medium", version=VERSION, description="This part of my dataset covers abstract only"),
+        datasets.BuilderConfig(name="large", version=VERSION, description="This part of my dataset covers abstract only")
+       
+
+    ]
+
+    DEFAULT_CONFIG_NAME = "small"  
+
+    def _info(self):
+        
+        _URLS['train']="data/"+str(self.config.name)+"/train.jsnol"
+
+        
+        features = datasets.Features(
+            {̉
+                "id": datasets.Value("string")
+                "sections": datasets.features.Sequence(datasets.Value("string")),
+                "sec_text": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
+                "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
+                "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
+                "sec_bio_tags": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string")))
+                
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+    
+        data_dir = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir['train'],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir['test'],
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir['valid'],
+                    "split": "valid",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                    "id": data['paper_id']
+                    "document": data["document"],
+                    "document_tags": data["document_tags"],
+                    "extractive_keyphrases": data["extractive_keyphrases"],
+                    "abstractive_keyphrases": data["abstractive_keyphrases"],
+                    "other_metadata": data["other_metadata"]
+                }