add duconv dataset

duconv.py

@@ -0,0 +1,131 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import json
+import os
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """\
+Duconv is a chinese convolution \
+dataset, designed to evaluate the dialogue models.
+"""
+
+_URL = "https://bj.bcebos.com/paddlenlp/datasets/DuConv.zip"
+
+
+class DuconvConfig(datasets.BuilderConfig):
+    """BuilderConfig for Duconv."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Duconv.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(DuconvConfig, self).__init__(**kwargs)
+
+
+class Duconv(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        DuconvConfig(
+            name="DuConv",
+            version=datasets.Version("1.0.0", ""),
+            description=_DESCRIPTION,
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "id":
+                datasets.Value("string"),
+                "goal":
+                datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+                "knowledge":
+                datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+                "conversation":
+                datasets.Sequence(datasets.Value("string")),
+                "history":
+                datasets.Sequence(datasets.Value("string")),
+                "response":
+                datasets.Value("string"),
+            }),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://arxiv.org/pdf/1906.05572.pdf",
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(_URL)
+
+        return [
+            datasets.SplitGenerator(name="train",
+                                    gen_kwargs={
+                                        "filepath":
+                                        os.path.join(dl_dir, 'DuConv',
+                                                     'train.txt'),
+                                    }),
+            datasets.SplitGenerator(name="dev",
+                                    gen_kwargs={
+                                        "filepath":
+                                        os.path.join(dl_dir, 'DuConv',
+                                                     'dev.txt'),
+                                    }),
+            datasets.SplitGenerator(name="test_1",
+                                    gen_kwargs={
+                                        "filepath":
+                                        os.path.join(dl_dir, 'DuConv',
+                                                     'test_1.txt'),
+                                    }),
+            datasets.SplitGenerator(name="test_2",
+                                    gen_kwargs={
+                                        "filepath":
+                                        os.path.join(dl_dir, 'DuConv',
+                                                     'test_2.txt'),
+                                    }),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        key = 0
+        with open(filepath, 'r', encoding="utf-8") as fin:
+            for line in fin:
+                duconv = json.loads(line)
+
+                goal = duconv["goal"] if "goal" in duconv.keys() else [[]]
+                knowledge = duconv["knowledge"] if "knowledge" in duconv.keys(
+                ) else [[]]
+                conversation = duconv[
+                    "conversation"] if "conversation" in duconv.keys() else []
+                history = duconv["history"] if "history" in duconv.keys(
+                ) else []
+                response = duconv["response"] if "response" in duconv.keys(
+                ) else ""
+
+                yield key, {
+                    "id": str(key),
+                    "goal": goal,
+                    "knowledge": knowledge,
+                    "conversation": conversation,
+                    "history": history,
+                    "response": response,
+                }
+                key += 1