Make optimization data

.gitattributes

@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+train_data.csv filter=lfs diff=lfs merge=lfs -text
+validation_data.csv filter=lfs diff=lfs merge=lfs -text

summarization_optimization.py

@@ -0,0 +1,73 @@
+import json
+import pandas as pd
+import datasets
+import csv
+from datasets.tasks import Summarization
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_DESCRIPTION = """\
+Aihub Document summarization data
+"""
+_URL = "https://huggingface.co/datasets/metamong1/summarization_optimization/resolve/main/"
+_URLS = {
+    "train_data": _URL + "train_data.csv",
+    "validation_data": _URL + "validation_data.csv",
+}
+
+class SummarizationOptimization(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="Summarization Part Data",
+            version=datasets.Version("1.0.0", ""),
+            description="Text Summarization & Generation Title for optimization",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "doc_id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "doc_type": datasets.Value("string"),
+                    "file": datasets.Value("string"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/metamong1/summarization_optimization",
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train_data"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation_data"]}),
+        ]
+
+    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, newline='', encoding="utf-8") as csvfile:
+            reader = csv.reader(csvfile, delimiter=",")
+            feature_name = next(reader)
+
+            idx = 0
+            for row in reader:
+                features = {
+                    "doc_id" : row[0],
+                    "title" : row[1],
+                    "text" : row[2],
+                    "doc_type" : row[3],
+                    "file" : row[4],
+                }
+                
+                yield idx, features
+                idx += 1

train_data.csv

@@ -0,0 +1,3 @@
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+oid sha256:63aa4cabc7c14d9c42586f06b12b095beb3469ab2bd7154abd97d3f6bf09d9e2
+size 178145961

validation_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8f3ebe76e1f6f25406a0c951bede30fe1ce738ad83a239b2a08256cba54a964
+size 44623623