Update parquet files (step 1 of 5)

MNBVC.py

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-# 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.
-"""
-MNBVC: Massive Never-ending BT Vast Chinese corpus
-"""
-
-import json
-import datasets
-import numpy as np
-import traceback
-from .meta import MNBVC_META
-from .features import Features
-
-
-_CITATION = """\
-"""
-
-_DESCRIPTION = """\
-MNBVC: Massive Never-ending BT Vast Chinese corpus
-"""
-
-_HOMEPAGE = "https://github.com/esbatmop/MNBVC"
-
-_LICENSE = "MIT"
-
-
-class MNBVC(datasets.GeneratorBasedBuilder):
-    """Massive Never-ending BT Vast Chinese corpus."""
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name=key, version=datasets.Version("0.0.1"), description=value['description']) for key, value in MNBVC_META.items()]
-
-    def _info(self):
-        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[MNBVC_META[self.config.name]['feature_type']],  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(MNBVC_META[self.config.name]['files'])
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_files": data_dir,
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, data_files):
-        id = 0
-        features = self._info().features
-        feature_keys = set(features.keys())
-
-        def _drop_unused_keys(data):
-            rm_keys = []
-            for key in data.keys():
-                if key not in feature_keys:
-                    rm_keys.append(key)
-            for key in rm_keys:
-                del data[key]
-            return data
-
-        try:
-            for file_i, data_file in enumerate(data_files):
-                with open(data_file, encoding="utf-8") as f:
-                    for line_i, line in enumerate(f):
-                        id += 1
-                        data = json.loads(line)
-                        if self.config.name == 'law_judgement':
-                            text = data['详情']
-                            del data['详情']
-                            yield id, {
-                                "text": text,
-                                "meta": json.dumps(data, ensure_ascii=False),
-                            }
-                        else:
-                            data = _drop_unused_keys(data)
-                            if 'simhash' in data:  # for issue https://github.com/huggingface/datasets/issues/6007
-                                data['simhash'] = str(data['simhash'])
-                            
-                            yield id, data
-        except Exception as e:
-            error_msg = 'oops, we find an error when loading the dataset\n'
-            error_msg += f'Dataset: {self.config.name}\n'
-            error_msg += f'Data File: {file_i} {data_file}\n'
-            error_msg += f'Row: {line_i}'
-            print(error_msg)
-            traceback.print_exc()
-
-            raise e

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