Upload crd3.py

crd3.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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
+"""CRD3  dataset"""
+
+
+import json
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """
+@inproceedings{
+title = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},
+author = {Rameshkumar, Revanth  and Bailey, Peter},
+year = {2020},
+publisher = {Association for Computational Linguistics},
+conference = {ACL}
+}
+ """
+
+_DESCRIPTION = """
+Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.
+Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.
+The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding
+abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player
+collaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,
+and semantic ties to the previous dialogues.
+"""
+
+_URL = "https://github.com/RevanthRameshkumar/CRD3/archive/master.zip"
+
+
+def get_train_test_dev_files(files, test_split, train_split, dev_split):
+    test_files = dev_files = train_files = []
+    for file in files:
+        filename = os.path.split(file)[1].split("_")[0]
+        if filename in test_split:
+            test_files.append(file)
+        elif filename in train_split:
+            train_files.append(file)
+        elif filename in dev_split:
+            dev_files.append(file)
+        else:
+            logger.info(f"skipped file {file}")
+    return test_files, train_files, dev_files
+
+
+class CRD3(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "chunk": datasets.Value("string"),
+                    "chunk_id": datasets.Value("int32"),
+                    "turn_start": datasets.Value("int32"),
+                    "turn_end": datasets.Value("int32"),
+                    "alignment_score": datasets.Value("float32"),
+                    "turns": [
+                        {
+                            "names": datasets.features.Sequence(datasets.Value("string")),
+                            "utterances": datasets.features.Sequence(datasets.Value("string")),
+                            "number": datasets.Value("int32"),
+                        }
+                    ],
+                }
+            ),
+            homepage="https://github.com/RevanthRameshkumar/CRD3",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download_and_extract(_URL)
+        test_file = os.path.join(path, "CRD3-master", "data", "aligned data", "test_files")
+        train_file = os.path.join(path, "CRD3-master", "data", "aligned data", "train_files")
+        dev_file = os.path.join(path, "CRD3-master", "data", "aligned data", "val_files")
+        with open(test_file, encoding="utf-8") as f:
+            test_splits = [file.replace("\n", "") for file in f.readlines()]
+
+        with open(train_file, encoding="utf-8") as f:
+            train_splits = [file.replace("\n", "") for file in f.readlines()]
+        with open(dev_file, encoding="utf-8") as f:
+            dev_splits = [file.replace("\n", "") for file in f.readlines()]
+        c2 = "CRD3-master/data/aligned data/c=2"
+        c3 = "CRD3-master/data/aligned data/c=3"
+        c4 = "CRD3-master/data/aligned data/c=4"
+        files = [os.path.join(path, c2, file) for file in sorted(os.listdir(os.path.join(path, c2)))]
+        files.extend([os.path.join(path, c3, file) for file in sorted(os.listdir(os.path.join(path, c3)))])
+        files.extend([os.path.join(path, c4, file) for file in sorted(os.listdir(os.path.join(path, c4)))])
+
+        test_files, train_files, dev_files = get_train_test_dev_files(files, test_splits, train_splits, dev_splits)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"files_path": train_files},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"files_path": test_files},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"files_path": dev_files},
+            ),
+        ]
+
+    def _generate_examples(self, files_path):
+        """Yields examples."""
+
+        for id0, file in enumerate(files_path):
+            with open(file, encoding="utf-8") as f:
+                data = json.load(f)
+                for id1, row in enumerate(data):
+                    chunk = row["CHUNK"]
+                    chunk_id = row["ALIGNMENT"]["CHUNK ID"]
+                    turn_start = row["ALIGNMENT"]["TURN START"]
+                    turn_end = row["ALIGNMENT"]["TURN END"]
+                    score = row["ALIGNMENT"]["ALIGNMENT SCORE"]
+                    for turn in row["TURNS"]:
+                        turn["names"] = turn["NAMES"]
+                        turn["utterances"] = turn["UTTERANCES"]
+                        turn["number"] = turn["NUMBER"]
+
+                        del turn["NAMES"]
+                        del turn["UTTERANCES"]
+                        del turn["NUMBER"]
+
+                    yield str(id0) + "_" + str(id1), {
+                        "chunk": chunk,
+                        "chunk_id": chunk_id,
+                        "turn_start": turn_start,
+                        "turn_end": turn_end,
+                        "alignment_score": score,
+                        "turns": row["TURNS"],
+                    }