Add custom dataset dwmw17

datasets/download_text_classification.sh

@@ -3,41 +3,46 @@ DIR="./TextClassification"
 mkdir $DIR
 cd $DIR
 
-rm -rf mnli
-wget --content-disposition https://cloud.tsinghua.edu.cn/f/33182c22cb594e88b49b/?dl=1
-tar -zxvf mnli.tar.gz
-rm -rf mnli.tar.gz
-
-rm -rf agnews
-wget --content-disposition https://cloud.tsinghua.edu.cn/f/0fb6af2a1e6647b79098/?dl=1
-tar -zxvf agnews.tar.gz
-rm -rf agnews.tar.gz
-
-rm -rf dbpedia
-wget --content-disposition https://cloud.tsinghua.edu.cn/f/362d3cdaa63b4692bafb/?dl=1
-tar -zxvf dbpedia.tar.gz
-rm -rf dbpedia.tar.gz
-
-rm -rf imdb
-wget --content-disposition https://cloud.tsinghua.edu.cn/f/37bd6cb978d342db87ed/?dl=1
-tar -zxvf imdb.tar.gz
-rm -rf imdb.tar.gz
-
-rm -rf SST-2
-wget --content-disposition https://cloud.tsinghua.edu.cn/f/bccfdb243eca404f8bf3/?dl=1
-tar -zxvf SST-2.tar.gz
-rm -rf SST-2.tar.gz
-
-rm -rf amazon
-wget --content-disposition https://cloud.tsinghua.edu.cn/f/e00a4c44aaf844cdb6c9/?dl=1
-tar -zxvf amazon.tar.gz
-mv datasets/amazon/ amazon
-rm  -rf ./datasets
-rm -rf amazon.tar.gz
-
-rm -rf yahoo_answers_topics
-wget --content-disposition https://cloud.tsinghua.edu.cn/f/79257038afaa4730a03f/?dl=1
-tar -zxvf yahoo_answers_topics.tar.gz
-rm -rf yahoo_answers_topics.tar.gz
+# rm -rf mnli
+# wget --content-disposition https://cloud.tsinghua.edu.cn/f/33182c22cb594e88b49b/?dl=1
+# tar -zxvf mnli.tar.gz
+# rm -rf mnli.tar.gz
+
+# rm -rf agnews
+# wget --content-disposition https://cloud.tsinghua.edu.cn/f/0fb6af2a1e6647b79098/?dl=1
+# tar -zxvf agnews.tar.gz
+# rm -rf agnews.tar.gz
+
+# rm -rf dbpedia
+# wget --content-disposition https://cloud.tsinghua.edu.cn/f/362d3cdaa63b4692bafb/?dl=1
+# tar -zxvf dbpedia.tar.gz
+# rm -rf dbpedia.tar.gz
+
+# rm -rf imdb
+# wget --content-disposition https://cloud.tsinghua.edu.cn/f/37bd6cb978d342db87ed/?dl=1
+# tar -zxvf imdb.tar.gz
+# rm -rf imdb.tar.gz
+
+# rm -rf SST-2
+# wget --content-disposition https://cloud.tsinghua.edu.cn/f/bccfdb243eca404f8bf3/?dl=1
+# tar -zxvf SST-2.tar.gz
+# rm -rf SST-2.tar.gz
+
+# rm -rf amazon
+# wget --content-disposition https://cloud.tsinghua.edu.cn/f/e00a4c44aaf844cdb6c9/?dl=1
+# tar -zxvf amazon.tar.gz
+# mv datasets/amazon/ amazon
+# rm  -rf ./datasets
+# rm -rf amazon.tar.gz
+
+# rm -rf yahoo_answers_topics
+# wget --content-disposition https://cloud.tsinghua.edu.cn/f/79257038afaa4730a03f/?dl=1
+# tar -zxvf yahoo_answers_topics.tar.gz
+# rm -rf yahoo_answers_topics.tar.gz
+
+rm -rf dwmw17
+wget --content-disposition https://raw.githubusercontent.com/t-davidson/hate-speech-and-offensive-language/master/data/labeled_data.csv
+mkdir -p dwmw17
+mv labeled_data.csv dwmw17
 
 cd ..

openprompt/data_utils/text_classification_dataset.py

@@ -17,6 +17,7 @@
 
 import os
 import json, csv
+import pandas as pd
 from abc import ABC, abstractmethod
 from collections import defaultdict, Counter
 from typing import List, Dict, Callable
@@ -27,6 +28,38 @@
 from openprompt.data_utils.data_processor import DataProcessor
 
 
+class Dwmw17Processor(DataProcessor):
+    def __init__(self):
+        super().__init__()
+        self.labels = [ "hate speech", "offensive language", "neither" ]
+
+    def get_examples(self, data_dir, split):
+        df = pd.read_csv(os.path.join(data_dir, 'labeled_data.csv'))
+        """
+        24783 rows in total, 0: 1430, 1: 19190, 2: 4163
+        I will take 50% as training and 50% as testing
+        """
+        train_splits = [ 715, 9595, 2081 ]
+        examples = []
+        for label_idx in range(len(self.labels)):
+            df_label = df[df['class'] == label_idx]
+            train_split = train_splits[label_idx]
+
+            tweets = df_label['tweet'].tolist()
+            indexs = df_label.iloc[:, 0].tolist()
+            if split == 'train':
+                tweets_split = tweets[:train_split]
+                indexs_split = indexs[:train_split]
+            else:
+                tweets_split = tweets[train_split:]
+                indexs_split = indexs[train_split:]
+            for tweet, index in zip(tweets_split, indexs_split):
+                examples.append(InputExample(
+                    guid=str(index), text_a=tweet, text_b="", label=label_idx
+                ))
+        return examples
+
+
 class MnliProcessor(DataProcessor):
     # TODO Test needed
     def __init__(self):