Convert dataset to Parquet

README.md

@@ -32,18 +32,28 @@ dataset_info:
           '0': negative
           '1': positive
           '2': no_impact
+          '3': mixed
   splits:
   - name: train
-    num_bytes: 48555
+    num_bytes: 48551
     num_examples: 892
   - name: validation
-    num_bytes: 5788
+    num_bytes: 5784
     num_examples: 105
   - name: test
-    num_bytes: 5588
+    num_bytes: 5584
     num_examples: 104
-  download_size: 49870
-  dataset_size: 59931
+  download_size: 48150
+  dataset_size: 59919
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 train-eval-index:
 - config: default
   task: text-classification

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d641bfc593a0a35b8af376f76fab573953476eb8affc91d1ae07c16d8479ac0
+size 6161

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ca6372cf331a5db4de59d7a4de11710315c84866a868e65ae05eb4964465737
+size 35646

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97408b0166f4a456289d2d8ab8429bcac5c47b921d14dc92ecdad8b59732abb3
+size 6343

poem_sentiment.py

@@ -1,84 +0,0 @@
-# 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.
-"""Poem Sentiment: A sentiment dataset of poem verses"""
-
-
-import datasets
-
-
-_CITATION = """\
-@misc{sheng2020investigating,
-      title={Investigating Societal Biases in a Poetry Composition System},
-      author={Emily Sheng and David Uthus},
-      year={2020},
-      eprint={2011.02686},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-
-_DESCRIPTION = """\
-Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg. \
-This dataset can be used for tasks such as sentiment classification or style transfer for poems.
-"""
-
-
-_HOMEPAGE = "https://github.com/google-research-datasets/poem-sentiment"
-
-_BASE_URL = "https://raw.githubusercontent.com/google-research-datasets/poem-sentiment/master/data/"
-_URLS = {
-    "train": f"{_BASE_URL}/train.tsv",
-    "dev": f"{_BASE_URL}/dev.tsv",
-    "test": f"{_BASE_URL}/test.tsv",
-}
-_LABEL_MAPPING = {-1: 0, 0: 2, 1: 1, 2: 3}
-
-
-class PoemSentiment(datasets.GeneratorBasedBuilder):
-    """Poem Sentiment: A sentiment dataset of poem verses"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "verse_text": datasets.Value("string"),
-                    "label": datasets.ClassLabel(names=["negative", "positive", "no_impact", "mixed"]),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download(_URLS)
-        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):
-        with open(filepath, encoding="utf-8") as f:
-            lines = f.readlines()
-            for line in lines:
-                fields = line.strip().split("\t")
-                idx, verse_text, label = fields
-                label = _LABEL_MAPPING[int(label)]
-                yield int(idx), {"id": int(idx), "verse_text": verse_text, "label": label}