init

.gitattributes

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 data/tweet_hate_test3_seed1/train.jsonl filter=lfs diff=lfs merge=lfs -text
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process/tweet_emoji.py

@@ -1,58 +1,70 @@
-# TODO
 import json
 import os
 from random import shuffle, seed
 
+import pandas as pd
+from datasets import load_dataset
 
-with open("data/tweet_sentiment/test.jsonl") as f:
-    test = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_sentiment/test_1.jsonl") as f:
-    test_1 = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_sentiment/test_2.jsonl") as f:
-    test_2 = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_sentiment/test_3.jsonl") as f:
-    test_3 = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_sentiment/test_4.jsonl") as f:
-    test_4 = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_sentiment/train.jsonl") as f:
-    train = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_sentiment/validation.jsonl") as f:
-    validation = [json.loads(i) for i in f if len(i)]
+test = load_dataset("cardiffnlp/super_tweeteval", "tweet_emoji", split="test").shuffle(seed=42)
+test = list(test.to_pandas().T.to_dict().values())
+train = load_dataset("cardiffnlp/super_tweeteval", "tweet_emoji", split="train").shuffle(seed=42)
+train = list(train.to_pandas().T.to_dict().values())
+validation = load_dataset("cardiffnlp/super_tweeteval", "tweet_emoji", split="validation").shuffle(seed=42)
+validation = list(validation.to_pandas().T.to_dict().values())
+full = train + test + validation
+df = pd.DataFrame(full)
+df["date_dt"] = pd.to_datetime(df.date)
+df = df.sort_values(by="date_dt")
+dist_date = df.groupby("date_dt").size()
+total_n = len(df)
+n = 0
+while True:
+    n += 1
+    if dist_date[:n].sum() > total_n/2:
+        break
+split_date = dist_date.index[n]
+print(split_date)
 
+train = df[df["date_dt"] <= split_date]
+test = df[df["date_dt"] > split_date]
+print(train.date_dt.min(), train.date_dt.max())
+print(test.date_dt.min(), test.date_dt.max())
 
-os.makedirs(f"data/tweet_sentiment_small", exist_ok=True)
-with open(f"data/tweet_sentiment_small/test.jsonl", "w") as f:
-    f.write("\n".join([json.dumps(i) for i in test]))
+train.pop("date_dt")
+test.pop("date_dt")
+train = list(train.T.to_dict().values())
+test = list(test.T.to_dict().values())
+
+seed(42)
+shuffle(train)
+shuffle(test)
+valid = train[:int(len(train)*0.2)]
+train = train[len(valid):]
 
-with open(f"data/tweet_sentiment_small/test_1.jsonl", "w") as f:
+n_test = int(len(test)/4)
+n_train = len(train)
+n_validation = len(valid)
+test_1 = test[:n_test]
+test_2 = test[n_test:n_test*2]
+test_3 = test[n_test*2:n_test*3]
+test_4 = test[n_test*3:]
+os.makedirs("data/tweet_emoji", exist_ok=True)
+with open("data/tweet_emoji/test.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in test]))
+with open("data/tweet_emoji/test_1.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in test_1]))
-with open(f"data/tweet_sentiment_small/test_2.jsonl", "w") as f:
+with open("data/tweet_emoji/test_2.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in test_2]))
-with open(f"data/tweet_sentiment_small/test_3.jsonl", "w") as f:
+with open("data/tweet_emoji/test_3.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in test_3]))
-with open(f"data/tweet_sentiment_small/test_4.jsonl", "w") as f:
+with open("data/tweet_emoji/test_4.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in test_4]))
-
-with open(f"data/tweet_sentiment_small/validation.jsonl", "w") as f:
-    f.write("\n".join([json.dumps(i) for i in validation]))
-
-# down sample training set
-n_train_p = 2500
-n_train_n = 2500
-seed(123)
-shuffle(train)
-train_p = [i for i in train if i["gold_label_binary"] == 0][:n_train_p]
-train_n = [i for i in train if i["gold_label_binary"] == 1][:n_train_n]
-train = train_p + train_n
-shuffle(train)
-with open(f"data/tweet_sentiment_small/train.jsonl", "w") as f:
+with open("data/tweet_emoji/train.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in train]))
+with open("data/tweet_emoji/validation.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in valid]))
 
 
-n_train = len(train)
-n_validation = len(validation)
-n_test = int(len(test)/4)
-
 def sampler(dataset_test, r_seed):
     seed(r_seed)
     shuffle(dataset_test)
@@ -71,11 +83,17 @@ for n, _test in enumerate([
         test_1 + test_2 + test_4,
         test_1 + test_2 + test_3]):
     for s in range(3):
-        os.makedirs(f"data/tweet_sentiment_small_test{n}_seed{s}", exist_ok=True)
+        os.makedirs(f"data/tweet_emoji_test{n}_seed{s}", exist_ok=True)
         _train, _valid = sampler(_test, s)
-        with open(f"data/tweet_sentiment_small_test{n}_seed{s}/train.jsonl", "w") as f:
+        with open(f"data/tweet_emoji_test{n}_seed{s}/train.jsonl", "w") as f:
             f.write("\n".join([json.dumps(i) for i in _train]))
-        with open(f"data/tweet_sentiment_small_test{n}_seed{s}/validation.jsonl", "w") as f:
+        with open(f"data/tweet_emoji_test{n}_seed{s}/validation.jsonl", "w") as f:
             f.write("\n".join([json.dumps(i) for i in _valid]))
-        with open(f"data/tweet_sentiment_small_test{n}_seed{s}/test.jsonl", "w") as f:
+        with open(f"data/tweet_emoji_test{n}_seed{s}/test.jsonl", "w") as f:
             f.write("\n".join([json.dumps(i) for i in id2test[n]]))
+
+
+
+
+
+

tweet_temporal_shift.py

@@ -2,7 +2,7 @@
 import json
 import datasets
 
-_VERSION = "1.0.8"
+_VERSION = "1.0.9"
 _TWEET_TEMPORAL_DESCRIPTION = """"""
 _TWEET_TEMPORAL_CITATION = """"""
 _TWEET_TOPIC_DESCRIPTION = """
@@ -69,6 +69,10 @@ _TWEET_HATE_DESCRIPTION = """TBA"""
 _TWEET_HATE_CITATION = """\
 TBA
 """
+_TWEET_EMOJI_DESCRIPTION = """TBA"""
+_TWEET_EMOJI_CITATION = """\
+TBA
+"""
 _ROOT_URL = "https://huggingface.co/datasets/tweettemposhift/tweet_temporal_shift/resolve/main/data"
 
 
@@ -142,6 +146,13 @@ class TweetTemporalShift(datasets.GeneratorBasedBuilder):
             features=["gold_label_binary", "text", "date"],
             data_url=f"{_ROOT_URL}/tweet_sentiment_small",
         ),
+        TweetTemporalShiftConfig(
+            name="emoji_temporal",
+            description=_TWEET_EMOJI_DESCRIPTION,
+            citation=_TWEET_EMOJI_CITATION,
+            features=["text", "date", "gold_label"],
+            data_url=f"{_ROOT_URL}/tweet_emoji",
+        ),
     ]
     for s in range(3):
         for i in range(4):
@@ -188,6 +199,13 @@ class TweetTemporalShift(datasets.GeneratorBasedBuilder):
                     features=["gold_label_binary", "text", "date"],
                     data_url=f"{_ROOT_URL}/tweet_sentiment_small_test{i}_seed{s}",
                 ),
+                TweetTemporalShiftConfig(
+                    name=f"emoji_temporal_random{i}_seed{s}",
+                    description=_TWEET_EMOJI_DESCRIPTION,
+                    citation=_TWEET_EMOJI_CITATION,
+                    features=["text", "date", "gold_label"],
+                    data_url=f"{_ROOT_URL}/tweet_emoji_test{i}_seed{s}",
+                ),
             ]
 
     def _info(self):
@@ -197,7 +215,8 @@ class TweetTemporalShift(datasets.GeneratorBasedBuilder):
                 "arts_&_culture", "business_&_entrepreneurs", "celebrity_&_pop_culture", "diaries_&_daily_life",
                 "family", "fashion_&_style", "film_tv_&_video", "fitness_&_health", "food_&_dining", "gaming",
                 "learning_&_educational", "music", "news_&_social_concern", "other_hobbies", "relationships",
-                "science_&_technology", "sports", "travel_&_adventure", "youth_&_student_life"]
+                "science_&_technology", "sports", "travel_&_adventure", "youth_&_student_life"
+            ]
             features["gold_label_list"] = datasets.Sequence(
                 datasets.features.ClassLabel(names=names))
         elif "hate" in self.config.name:
@@ -206,6 +225,15 @@ class TweetTemporalShift(datasets.GeneratorBasedBuilder):
             features["date"] = datasets.Value("string")
             features["id"] = datasets.Value("string")
             features["source"] = datasets.Value("int32")
+        elif "emoji" in self.config.name:
+            features["text"] = datasets.Value("string")
+            features["date"] = datasets.Value("string")
+            url_map = "https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tweet_emoji/map.txt"
+            dl_manager = datasets.utils.download_manager.DownloadManager()
+            with open(dl_manager.download(url_map)) as f:
+                label_classes = f.readlines()
+            label_classes = [x.strip('\n') for x in label_classes]
+            features['gold_label'] = datasets.features.ClassLabel(names=label_classes)
         elif "sentiment" in self.config.name:
             features["text"] = datasets.Value("string")
             features["gold_label_binary"] = datasets.Value("int32")