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-*.7z filter=lfs diff=lfs merge=lfs -text
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default/reddit-topics-targz-train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e029c3426df6aff1c4921a4da036fa23ef5003134719fd460e529caabe6604a
+size 1822976

reddit-topics-targz.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
-"""SQUAD: The Stanford Question Answering Dataset."""
-
-
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@article{2016arXiv160605250R,
-       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
-                 Konstantin and {Liang}, Percy},
-        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
-      journal = {arXiv e-prints},
-         year = 2016,
-          eid = {arXiv:1606.05250},
-        pages = {arXiv:1606.05250},
-archivePrefix = {arXiv},
-       eprint = {1606.05250},
-}
-"""
-
-_DESCRIPTION = """\
-Demo...
-"""
-
-_URL = "https://github.com/jamescalam/hf-datasets/raw/main/01_builder_script/dataset.tar.gz"
-
-
-class RedditTopicsTargz(datasets.GeneratorBasedBuilder):
-    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sub": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "selftext": datasets.Value("string"),
-                    "upvote_ratio": datasets.Value("float32"),
-                    "id": datasets.Value("string"),
-                    "created_utc": datasets.Value("float32"),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
-            supervised_keys=None,
-            homepage="https://github.com/jamescalam/hf-datasets/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        path = dl_manager.download_and_extract(_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, 
-                gen_kwargs={"filepath": path+'/dataset.jsonl'}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        idx = 0
-        # open the file and read the lines
-        with open(filepath, encoding="utf-8") as fp:
-            for line in fp:
-                # load json line
-                obj = json.loads(line)
-                yield idx, obj
-                idx += 1