INIT: add afqmc dataset

AFQMC.py

@@ -0,0 +1,117 @@
+# 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.
+"""AFQMC"""
+
+
+import os
+import json
+import datasets
+
+
+_CITATION = """\
+"""
+
+_DESCRIPTION = """\
+Download from https://www.cluebenchmarks.com/introduce.html
+"""
+
+_LICENSE = "apache-license-2.0"
+_HOMEPAGE = "https://github.com/IDEA-CCNL/Fengshenbang-LM"
+
+
+class AFQMCConfig(datasets.BuilderConfig):
+    """BuilderConfig for AFQMCConfig"""
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        """
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+
+
+class AFQMCConfig(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIG_CLASS = AFQMCConfig
+    BUILDER_CONFIGS = [
+        AFQMCConfig(description=_DESCRIPTION)
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "sentence1": datasets.Value("string"),
+                "sentence2": datasets.Value("string"),
+                "label": datasets.ClassLabel(num_classes=2, names=['not similar', 'similar']),
+            }),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE
+        )
+
+    def _split_generators(self, dl_manager):
+
+        files = {
+            "test": os.path.join("afqmc_public", f"test.json"),
+            "validation": os.path.join("afqmc_public", f"dev.json"),
+            "train": os.path.join("afqmc_public", f"train.json"),
+        }
+        data_dir = dl_manager.download_and_extract(files)
+
+        output = []
+        test = datasets.SplitGenerator(
+            name=datasets.Split.TEST,
+            gen_kwargs={
+                "filepath": data_dir["test"]
+            }
+        )
+        output.append(test)
+
+        # if os.path.exists(data_dir["validation"]):
+        valid = datasets.SplitGenerator(
+            name=datasets.Split.VALIDATION,
+            gen_kwargs={
+                "filepath": data_dir["validation"]
+            }
+        )
+        output.append(valid)
+
+        train = datasets.SplitGenerator(
+            name=datasets.Split.TRAIN,
+            gen_kwargs={
+                "filepath": data_dir["train"]
+            }
+        )
+        output.append(train)
+
+        return output
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            lines = f.readlines()
+            for id_, line in enumerate(lines):
+                data = json.loads(line)
+                texta = data['sentence1']
+                textb = data['sentence2']
+                label = 'not similar' if data['label'] == '0' else 'similar'
+                yield id_, {
+                    'sentence1': data['sentence1'],
+                    'sentence2': data['sentence2'],
+                    'label': label,
+                }