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Delete loading script

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  1. sentiment140.py +0 -115
sentiment140.py DELETED
@@ -1,115 +0,0 @@
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- import csv
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- import os
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-
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- import datasets
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-
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-
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- _CITATION = """\
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- @article{go2009twitter,
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- title={Twitter sentiment classification using distant supervision},
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- author={Go, Alec and Bhayani, Richa and Huang, Lei},
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- journal={CS224N project report, Stanford},
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- volume={1},
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- number={12},
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- pages={2009},
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- year={2009}
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- }
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- """
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-
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- _DESCRIPTION = """\
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- Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for
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- sentiment classification. For more detailed information please refer to the paper.
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- """
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- _URL = "http://help.sentiment140.com/home"
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- _DATA_URL = "https://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip"
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-
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- _TEST_FILE_NAME = "testdata.manual.2009.06.14.csv"
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- _TRAIN_FILE_NAME = "training.1600000.processed.noemoticon.csv"
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-
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-
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- class Sentiment140Config(datasets.BuilderConfig):
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-
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- """BuilderConfig for Break"""
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-
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- def __init__(self, data_url, **kwargs):
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- """BuilderConfig for BlogAuthorship
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-
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- Args:
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- data_url: `string`, url to the dataset (word or raw level)
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- **kwargs: keyword arguments forwarded to super.
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- """
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- super(Sentiment140Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
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- self.data_url = data_url
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-
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-
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- class Sentiment140(datasets.GeneratorBasedBuilder):
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-
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- VERSION = datasets.Version("0.1.0")
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- BUILDER_CONFIGS = [
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- Sentiment140Config(
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- name="sentiment140",
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- data_url=_DATA_URL,
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- description="sentiment classification dataset. Twitter messages are classified as either 'positive'=0, 'neutral'=1 or 'negative'=2.",
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- )
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- ]
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-
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- def _info(self):
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- return datasets.DatasetInfo(
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- # This is the description that will appear on the datasets page.
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- description=_DESCRIPTION,
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- # datasets.features.FeatureConnectors
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- features=datasets.Features(
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- {
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- "text": datasets.Value("string"),
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- "date": datasets.Value("string"),
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- "user": datasets.Value("string"),
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- "sentiment": datasets.Value("int32"),
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- "query": datasets.Value("string"),
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- }
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- ),
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- # If there's a common (input, target) tuple from the features,
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- # specify them here. They'll be used if as_supervised=True in
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- # builder.as_dataset.
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- supervised_keys=None,
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- # Homepage of the dataset for documentation
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- homepage=_URL,
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- """Returns SplitGenerators."""
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- data_dir = dl_manager.download_and_extract(_DATA_URL)
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-
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- test_csv_file = os.path.join(data_dir, _TEST_FILE_NAME)
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- train_csv_file = os.path.join(data_dir, _TRAIN_FILE_NAME)
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-
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- if self.config.name == "sentiment140":
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={"file_path": train_csv_file},
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.TEST,
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- # These kwargs will be passed to _generate_examples
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- gen_kwargs={"file_path": test_csv_file},
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- ),
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- ]
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- else:
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- raise NotImplementedError(f"{self.config.name} does not exist")
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-
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- def _generate_examples(self, file_path):
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- """Yields examples."""
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-
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- with open(file_path, encoding="ISO-8859-1") as f:
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- data = csv.reader(f, delimiter=",", quotechar='"')
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- for row_id, row in enumerate(data):
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- sentiment, tweet_id, date, query, user_name, message = row
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- yield f"{row_id}_{tweet_id}", {
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- "text": message,
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- "date": date,
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- "user": user_name,
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- "sentiment": int(sentiment),
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- "query": query,
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- }