Convert dataset to Parquet

README.md

@@ -11,6 +11,35 @@ task_categories:
 task_ids: []
 tags:
 - conditional-text-generation
+dataset_info:
+  config_name: document
+  features:
+  - name: report
+    dtype: string
+  - name: summary
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 953321013
+    num_examples: 17517
+  - name: validation
+    num_bytes: 55820431
+    num_examples: 973
+  - name: test
+    num_bytes: 51591123
+    num_examples: 973
+  download_size: 506610432
+  dataset_size: 1060732567
+configs:
+- config_name: document
+  data_files:
+  - split: train
+    path: document/train-*
+  - split: validation
+    path: document/validation-*
+  - split: test
+    path: document/test-*
+  default: true
 ---
 
 # GovReport dataset for summarization

test.zip → document/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d1a576daf0490954f4c72859334a2427b0d34d3bca8917849d94ec17e65dd197
-size 14171808
+oid sha256:d4f4fa6f96f2cb43ffcdbf5e1d39d2ce1ebff5314d0a92413211103aae3d5957
+size 24000716

train.zip → document/train-00000-of-00002.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:201f9a408ec5b86d6b9d6a056079dc104a34cc6c0440b615483b74eee0f02aee
-size 270614224
+oid sha256:8b02abb5691b3fa1ba95f76331cadc8bb3e80195c626b4656b480d36e0c92242
+size 227793632

document/train-00001-of-00002.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce7786d8626c94c58163b195b6d4c6ddaa01ff0a9c3574ac21399ae56c3dd3a3
+size 228706875

valid.zip → document/validation-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:55243db0da8c1b081fa0336456265923065c9d4c6c32420fd84f02cedb4ddcc4
-size 15445708
+oid sha256:8098b61d7aef5b16de870963a2f074b69e625767715f9c5ce94e2fc860087398
+size 26109209

govreport-summarization.py

@@ -1,96 +0,0 @@
-import json
-import os
-import datasets
-from datasets.tasks import TextClassification
-
-_DESCRIPTION = """
- GovReport dataset for summarization.
- From paper: Efficient Attentions for Long Document Summarization" by L. Huang et al.
- See: https://arxiv.org/pdf/2104.02112.pdf 
- See: https://github.com/luyang-huang96/LongDocSum
-"""
-_CITATION = """\
-    @misc{huang2021efficient,
-      title={Efficient Attentions for Long Document Summarization}, 
-      author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang},
-      year={2021},
-      eprint={2104.02112},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-    }
-}
-"""
-_ABSTRACT = "summary"
-_ARTICLE = "report"
-
-class GovReportSummarizationConfig(datasets.BuilderConfig):
-    """BuilderConfig for GovReportSummarization."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for GovReportSummarization.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(GovReportSummarizationConfig, self).__init__(**kwargs)
-
-
-class GovReportSummarizationDataset(datasets.GeneratorBasedBuilder):
-    """GovReportSummarization Dataset."""
-    
-    _TRAIN_FILE = "train.zip"
-    _VAL_FILE = "valid.zip"
-    _TEST_FILE = "test.zip"
-
-    BUILDER_CONFIGS = [
-        GovReportSummarizationConfig(
-            name="document",
-            version=datasets.Version("1.0.0"),
-            description="GovReport dataset for summarization, document",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "document"
-
-    def _info(self):
-        # Should return a datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    _ARTICLE: datasets.Value("string"),
-                    _ABSTRACT: datasets.Value("string"),
-                    #"id": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/luyang-huang96/LongDocSum",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        train_path = os.path.join(dl_manager.download_and_extract(self._TRAIN_FILE), "train.txt")
-        val_path = os.path.join(dl_manager.download_and_extract(self._VAL_FILE), "valid.txt")
-        test_path = os.path.join(dl_manager.download_and_extract(self._TEST_FILE), "test.txt")
-        
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
-            ),
-        ]
-    
-    def _generate_examples(self, filepath):
-        """Generate GovReportSummarization examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                report = data["report"]
-                summary = data["summary"]
-
-                yield id_, {"report": report, "summary": summary}