adding corpus reader

scificorpus.py

@@ -0,0 +1,170 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the 'License');
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an 'AS IS' BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+
+import json
+
+import datasets
+
+_CITATION = '''
+@inproceedings{Ammanabrolu2020AAAI, 
+title={Story Realization: Expanding Plot Events into Sentences}, 
+author={Prithviraj Ammanabrolu and Ethan Tien and Wesley Cheung and Zhaochen Luo and William Ma and Lara J. Martin and Mark O. Riedl}, 
+journal={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}, 
+year={2020}, 
+volume={34},
+number={05},
+url={https://ojs.aaai.org//index.php/AAAI/article/view/6232}
+}
+'''
+
+_DESCRIPTION = 'Loading script for the science fiction TV show plot dataset.'
+
+_URLS = {"scifiTVshows":
+    'https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/scifiTVshows.zip'}
+
+_INPUT_OUTPUT = ["all-sci-fi-data-test_input.txt", "all-sci-fi-data-test_output.txt", "all-sci-fi-data-train_input.txt", "all-sci-fi-data-train_output.txt", "all-sci-fi-data-val_input.txt", "all-sci-fi-data-val_output.txt"]
+
+
+class ScifiTV(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            version=datasets.Version('1.1.0'),
+            name=lang, 
+            description=f'Mr TyDi dataset in language {lang}.'
+        ) for lang in languages
+    ]
+
+    def _info(self):
+        features = datasets.Features({
+            'event': datasets.Value('list'), 
+            'gen_event': datasets.Value('list'),
+            'sent': datasets.Value('string'),
+            'gen_sent': datasets.Value('string'),
+            'story_num': datasets.Value('int'),
+            'entities': datasets.Value('dict')
+        })
+
+        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,  # Here we define them above because they are different between the two configurations
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage='https://github.com/rajammanabrolu/StoryRealization',
+            # License for the dataset if available
+            license='The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/',
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        lang = self.config.name
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    'filepath': 'Test-Train-Val/all-sci-fi-data-test.txt',
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    'filepath': 'Test-Train-Val/all-sci-fi-data-val.txt',
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    'filepath': 'Test-Train-Val/all-sci-fi-data-train.txt',
+                },
+            ),
+            datasets.SplitGenerator(
+                name="all",
+                gen_kwargs={
+                    'filepath': 'all-sci-fi-data.txt',
+                },
+            ),
+            """
+            datasets.SplitGenerator(
+                name="test-input",
+                gen_kwargs={
+                    'filepath': 'Input_OutputFiles/all-sci-fi-data-test_input.txt',
+                },
+            ),
+            datasets.SplitGenerator(
+                name="test-output",
+                gen_kwargs={
+                    'filepath': 'Input_OutputFiles/all-sci-fi-data-test_output.txt',
+                },
+            ), 
+            datasets.SplitGenerator(
+                name="val-input",
+                gen_kwargs={
+                    'filepath': 'Input_OutputFiles/all-sci-fi-data-val_input.txt',
+                },
+            ), 
+            datasets.SplitGenerator(
+                name="val-input",
+                gen_kwargs={
+                    'filepath': 'Input_OutputFiles/all-sci-fi-data-val_output.txt',
+                },
+            ), 
+            datasets.SplitGenerator(
+                name="train-input",
+                gen_kwargs={
+                    'filepath': 'Input_OutputFiles/all-sci-fi-data-train_input.txt',
+                },
+            ), 
+            datasets.SplitGenerator(
+                name="train-input",
+                gen_kwargs={
+                    'filepath': 'Input_OutputFiles/all-sci-fi-data-train_output.txt',
+                },
+            ),  
+            """                                                                                
+        ]
+        return splits
+
+    def _generate_examples(self, filepath):
+        story_count = 0
+        with open(filepath, encoding="utf-8") as f:
+            story = []                 
+            for id_, line in enumerate(f):
+                if "%%%%%%" in line:
+                    for l in story:
+                        event, gen_event, sent, gen_sent = l.split("|||")
+                        line = line.replace("%%%%%%%%%%%%%%%%%", "")
+                        line = line.replace("defaultdict(<type 'list'>, ", "")[:-1]
+                        entities = eval(line)
+                        yield id_, {
+                        'event': eval(event),
+                        'gen_event': eval(gen_event),
+                        'sent': sent,
+                        'gen_sent': gen_sent,
+                        'story_num': story_count,
+                        'entities': entities,
+                        }
+                     story = []
+                     story_count+=1
+                elif "<EOS>" in line: continue
+                else:
+                    story.append(line.strip())
+     
+