Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"sentiment140": {"description": "Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for\nsentiment classification. For more detailed information please refer to the paper.\n", "citation": "@article{go2009twitter,\n  title={Twitter sentiment classification using distant supervision},\n  author={Go, Alec and Bhayani, Richa and Huang, Lei},\n  journal={CS224N project report, Stanford},\n  volume={1},\n  number={12},\n  pages={2009},\n  year={2009}\n}\n", "homepage": "http://help.sentiment140.com/home", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "string", "id": null, "_type": "Value"}, "user": {"dtype": "string", "id": null, "_type": "Value"}, "sentiment": {"dtype": "int32", "id": null, "_type": "Value"}, "query": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "sentiment140", "config_name": "sentiment140", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 73365, "num_examples": 498, "dataset_name": "sentiment140"}, "train": {"name": "train", "num_bytes": 225742946, "num_examples": 1600000, "dataset_name": "sentiment140"}}, "download_checksums": {"http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip": {"num_bytes": 81363704, "checksum": "004a3772c8a7ff9bbfeb875880f47f0679d93fc63e5cf9cff72d54a8a6162e57"}}, "download_size": 81363704, "dataset_size": 225816311, "size_in_bytes": 307180015}}

dummy/sentiment140/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9df71bf3c23ae815688e22c86b35f1a8692056da15db7b6ae6b1b29ef7347705
+size 703

dummy/sentiment140/1.0.0/dummy_data/testdata.manual.2009.06.14.csv

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+0,1,23-04-2010,NO_QUERY,test_user,test_message

dummy/sentiment140/1.0.0/dummy_data/training.1600000.processed.noemoticon.csv

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+3,1,23-04-2010,NO_QUERY,train user,train message

sentiment140.py

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