Create character_id.py

character_id.py

@@ -0,0 +1,154 @@
+# 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.
+"""Identifying character archetypes from movie scripts"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+"""
+
+_DESCRIPTION = """\
+The character types identification dataset consists of movie
+scripts annotated with character archetypes (Hero, Villain, Mentor, etc.).
+"""
+
+_URLS = {
+    "full_text": "https://drive.google.com/uc?export=download&id=1pivLkYl6l6_jJlQkHGsvziEn82GBapWc",
+    # "repo": "https://github.com/ghomasHudson/character-type-identification/archive/master.zip",
+    "repo": "https://github.com/ghomasHudson/character-type-identification/archive/refs/heads/master.zip"
+}
+
+
+class CharacterTypeID(datasets.GeneratorBasedBuilder):
+    """Character Type Identification"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            features=datasets.Features(
+                {
+                    "document": {
+                        "id": datasets.Value("string"),
+                        "url": datasets.Value("string"),
+                        "file_size": datasets.Value("int32"),
+                        "word_count": datasets.Value("int32"),
+                        "start": datasets.Value("string"),
+                        "end": datasets.Value("string"),
+                        "summary": {
+                            "text": datasets.Value("string"),
+                            "url": datasets.Value("string"),
+                            "title": datasets.Value("string"),
+                        },
+                        "text": datasets.Value("string"),
+                    },
+                    "character_name": datasets.Value("string"),
+                    "unit_quality_score": datasets.Value("float32"),
+                    "character_type": datasets.ClassLabel(names=[
+                        "Hero",
+                        "Villain/Antagonist",
+                        "Spouse/Partner/Lover of Hero",
+                        "Spouse/Partner/Lover of Villain",
+                        "Sidekick of Hero",
+                        "Sidekick of Villain",
+                        "Supporting role character of Hero",
+                        "Supporting role character of Villain",
+                        "Mentor",
+                        "No Applicable Type"
+                    ])
+                }
+            ),
+            homepage="https://github.com/ghomasHudson/character-type-identification",
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        dl_dir = dl_manager.download_and_extract(_URLS)
+        dl_dir["repo"] = os.path.join(dl_dir["repo"], "character-type-identification-master")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"repo_dir": dl_dir["repo"], "full_text_dir": dl_dir["full_text"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"repo_dir": dl_dir["repo"], "full_text_dir": dl_dir["full_text"], "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"repo_dir": dl_dir["repo"], "full_text_dir": dl_dir["full_text"], "split": "valid"},
+            ),
+        ]
+
+    def _generate_examples(self, repo_dir, full_text_dir, split):
+        """Yields examples."""
+        documents = {}
+        with open(os.path.join(repo_dir, "documents.csv"), encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                if row["set"] != split:
+                    continue
+                documents[row["document_id"]] = row
+
+        summaries = {}
+        with open(os.path.join(repo_dir, "summaries.csv"), encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                if row["set"] != split:
+                    continue
+                summaries[row["document_id"]] = row
+
+        char_fn = "character_labels.csv"
+        if split == "test":
+            char_fn = "character_labels_gold.csv"
+        with open(os.path.join(repo_dir, char_fn), encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for id_, row in enumerate(reader):
+                if row["set"] != split:
+                    continue
+                document_id = row["document_id"]
+                if document_id not in documents.keys():
+                    print("NO KEY")
+                document = documents[document_id]
+                summary = summaries[document_id]
+                full_text = open(os.path.join(full_text_dir, document_id + ".txt"), encoding="latin-1").read()
+                res = {
+                    "document": {
+                        "id": document["document_id"],
+                        "url": document["script_url"],
+                        "file_size": document["script_file_size"],
+                        "word_count": document["script_word_count"],
+                        "start": document["script_start"],
+                        "end": document["script_end"],
+                        "summary": {
+                            "text": summary["summary"],
+                            "url": document["wiki_url"],
+                            "title": document["wiki_title"],
+                        },
+                        "text": full_text,
+                    },
+                    "character_name": row["character_name"],
+                    "unit_quality_score": row["unit_quality_score"],
+                    "character_type": row["character_type"]
+                }
+                yield id_, res