Add dataset loading script

vietnamese_students_feedback.py

@@ -0,0 +1,112 @@
+# coding=utf-8
+# 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.
+"""Vietnamese Students’ Feedback Corpus."""
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{8573337,
+  author={Nguyen, Kiet Van and Nguyen, Vu Duc and Nguyen, Phu X. V. and Truong, Tham T. H. and Nguyen, Ngan Luu-Thuy},
+  booktitle={2018 10th International Conference on Knowledge and Systems Engineering (KSE)},
+  title={UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis},
+  year={2018},
+  volume={},
+  number={},
+  pages={19-24},
+  doi={10.1109/KSE.2018.8573337}
+}
+"""
+
+_DESCRIPTION = """\
+Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different
+research fields between sentiment analysis and education.
+
+Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are
+human-annotated with two different tasks: sentiment-based and topic-based classifications.
+
+To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the
+UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over
+91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved
+approximately 88% of the sentiment F1-score and over 84% of the topic F1-score.
+"""
+
+_HOMEPAGE = "https://sites.google.com/uit.edu.vn/uit-nlp/datasets-projects#h.p_4Brw8L-cbfTe"
+
+# TODO: Add the licence for the dataset here if you can find it
+# _LICENSE = ""
+
+_URLS = {
+    datasets.Split.TRAIN: {
+        "sentences": "https://drive.google.com/uc?id=1nzak5OkrheRV1ltOGCXkT671bmjODLhP&export=download",
+        "sentiments": "https://drive.google.com/uc?id=1ye-gOZIBqXdKOoi_YxvpT6FeRNmViPPv&export=down load",
+        "topics": "https://drive.google.com/uc?id=14MuDtwMnNOcr4z_8KdpxprjbwaQ7lJ_C&export=download",
+    },
+    datasets.Split.VALIDATION: {
+        "sentences": "https://drive.google.com/uc?id=1sMJSR3oRfPc3fe1gK-V3W5F24tov_517&export=download",
+        "sentiments": "https://drive.google.com/uc?id=1GiY1AOp41dLXIIkgES4422AuDwmbUseL&export=download",
+        "topics": "https://drive.google.com/uc?id=1DwLgDEaFWQe8mOd7EpF-xqMEbDLfdT-W&export=download",
+    },
+    datasets.Split.TEST: {
+        "sentences": "https://drive.google.com/uc?id=1aNMOeZZbNwSRkjyCWAGtNCMa3YrshR-n&export=download",
+        "sentiments": "https://drive.google.com/uc?id=1vkQS5gI0is4ACU58-AbWusnemw7KZNfO&export=download",
+        "topics": "https://drive.google.com/uc?id=1_ArMpDguVsbUGl-xSMkTF_p5KpZrmpSB&export=download",
+    },
+}
+
+
+class VietnameseStudentsFeedback(datasets.GeneratorBasedBuilder):
+    """Vietnamese Students’ Feedback Corpus."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "sentiment": datasets.ClassLabel(names=["negative", "neutral", "positive"]),
+                    "topic": datasets.ClassLabel(names=["lecturer", "training_program", "facility", "others"]),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            # license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "sentences_path": data_dir[split]["sentences"],
+                    "sentiments_path": data_dir[split]["sentiments"],
+                    "topics_path": data_dir[split]["topics"],
+                },
+            ) for split in _URLS
+        ]
+
+    def _generate_examples(self, sentences_path, sentiments_path, topics_path):
+        with open(sentences_path, encoding="utf-8") as sentences, open(
+            sentiments_path, encoding="utf-8"
+        ) as sentiments, open(topics_path, encoding="utf-8") as topics:
+            for key, (sentence, sentiment, topic) in enumerate(zip(sentences, sentiments, topics)):
+                yield key, {
+                    "sentence": sentence.strip(),
+                    "sentiment": int(sentiment.strip()),
+                    "topic": int(topic.strip()),
+                }