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GeoNLPTweets.py

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+# coding=utf-8
+# Copyright 2020 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
+"""MasakhaNEWS: News Topic Classification for African languages"""
+
+import datasets
+import pandas
+import pandas as pd
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """
+@inproceedings{lawallanre-2023-geoNLPSent,
+    author = "Olanrewaju",
+    month = "Nov",
+    year = "2023",
+    address = "Lagos, Nigeria",
+}
+"""
+
+_DESCRIPTION = """\
+geoNLPSent is  dataset of transport tweets extrcted from twitter
+The language is:
+- English (eng)
+"""
+
+
+_URL = "https://github.com/lawallanre00490038/GeoNLP/raw/main/data/"
+_TRAINING_FILE = "train.tsv"
+_DEV_FILE = "dev.tsv"
+_TEST_FILE = "test.tsv"
+
+
+class GeoNLPSentiConfig(datasets.BuilderConfig):
+    """BuilderConfig for GeoNLPsenti"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for GeoNLPsenti.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(GeoNLPSentiConfig, self).__init__(**kwargs)
+
+
+class GeoNLPSenti(datasets.GeneratorBasedBuilder):
+    """GeoNLPsenti dataset."""
+
+    BUILDER_CONFIGS = [
+        GeoNLPSentiConfig(name="en", version=datasets.Version("1.0.0"), description="Nollysenti English dataset")
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "label": datasets.features.ClassLabel(
+                        names=["Positive", "negative", "Neutral"]
+                    ),
+                    "review": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/lawallanre00490038/GeoNLP",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{self.config.name}/{_TRAINING_FILE}",
+            "dev": f"{_URL}{self.config.name}/{_DEV_FILE}",
+            "test": f"{_URL}{self.config.name}/{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        df = pd.read_csv(filepath, sep='\t')
+        df = df.dropna()
+        N = df.shape[0]
+
+        for id_ in range(N):
+            yield id_, {
+                "label": df['sentiment'].iloc[id_],
+                "review": df['tweet'].iloc[id_],
+            }