Add test split (#2)

- Add test split (451e3d04b584c0efe9ee8d6f22342e82a1be7085)
- Update metadata in dataset card (f657281952e26e4fe48b673a5cbacade5118e5bb)

README.md

@@ -71,10 +71,13 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 3191776
+    num_bytes: 3191888
     num_examples: 31962
-  download_size: 3103165
-  dataset_size: 3191776
+  - name: test
+    num_bytes: 1711606
+    num_examples: 17197
+  download_size: 4738708
+  dataset_size: 4903494
 ---
 
 # Dataset Card for Tweets Hate Speech Detection

tweets_hate_speech_detection.py

@@ -29,6 +29,8 @@ The objective of this task is to detect hate speech in tweets. For the sake of s
 Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.
 """
 
+_HOMEPAGE = "https://github.com/sharmaroshan/Twitter-Sentiment-Analysis"
+
 _CITATION = """\
 @InProceedings{Z
 Roshan Sharma:dataset,
@@ -38,9 +40,10 @@ year={2018}
 }
 """
 
-_TRAIN_DOWNLOAD_URL = (
-    "https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/train_tweet.csv"
-)
+_URL = {
+    "train": "https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/train_tweet.csv",
+    "test": "https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/test_tweets.csv",
+}
 
 
 class TweetsHateSpeechDetection(datasets.GeneratorBasedBuilder):
@@ -55,30 +58,26 @@ class TweetsHateSpeechDetection(datasets.GeneratorBasedBuilder):
                     "tweet": datasets.Value("string"),
                 }
             ),
-            homepage="https://github.com/sharmaroshan/Twitter-Sentiment-Analysis",
+            homepage=_HOMEPAGE,
             citation=_CITATION,
             task_templates=[TextClassification(text_column="tweet", label_column="label")],
         )
 
     def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-
+        path = dl_manager.download(_URL)
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path["test"]}),
         ]
 
     def _generate_examples(self, filepath):
         """Generate Tweet examples."""
         with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(
+            csv_reader = csv.DictReader(
                 csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
             )
-            next(csv_reader, None)
             for id_, row in enumerate(csv_reader):
-                row = row[1:]
-                (label, tweet) = row
-
                 yield id_, {
-                    "label": int(label),
-                    "tweet": (tweet),
+                    "label": int(row.setdefault("label", -1)),
+                    "tweet": row["tweet"],
                 }