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experiments/main.sh

@@ -7,11 +7,10 @@ MODEL="vinai/bertweet-large"
 MODEL="cardiffnlp/twitter-roberta-base-2019-90m"
 MODEL="cardiffnlp/twitter-roberta-base-dec2020"
 MODEL="cardiffnlp/twitter-roberta-base-2021-124m"
-MODEL="cardiffnlp/twitter-roberta-large-2022-154m"
 MODEL="cardiffnlp/twitter-roberta-base-2022-154m"
+MODEL="cardiffnlp/twitter-roberta-large-2022-154m"
 
 # EMOJI
-
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_temporal"
 rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random0_seed0"

process/tweet_hate.py

@@ -1,37 +1,75 @@
 import json
 import os
 from random import shuffle, seed
+import pandas as pd
 
+with open("data/tweet_hate/full.jsonl") as f:
+    data = [json.loads(i) for i in f if len(i)]
 
-with open("data/tweet_hate/test.jsonl") as f:
-    test = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_hate/test_1.jsonl") as f:
-    test_1 = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_hate/test_2.jsonl") as f:
-    test_2 = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_hate/test_3.jsonl") as f:
-    test_3 = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_hate/test_4.jsonl") as f:
-    test_4 = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_hate/train.jsonl") as f:
-    train = [json.loads(i) for i in f if len(i)]
-with open("data/tweet_hate/validation.jsonl") as f:
-    validation = [json.loads(i) for i in f if len(i)]
+df = pd.DataFrame(data)
+df.pop("source")
+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())
+
+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):]
 
-n_train = len(train)
-n_validation = len(validation)
 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_hate", exist_ok=True)
+with open("data/tweet_hate/test.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in test]))
+with open("data/tweet_hate/test_1.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in test_1]))
+with open("data/tweet_hate/test_2.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in test_2]))
+with open("data/tweet_hate/test_3.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in test_3]))
+with open("data/tweet_hate/test_4.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in test_4]))
+with open("data/tweet_hate/train.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in train]))
+with open("data/tweet_hate/validation.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in valid]))
 
 
 def sampler(dataset_test, r_seed):
     seed(r_seed)
     shuffle(dataset_test)
     shuffle(train)
-    shuffle(validation)
+    shuffle(valid)
     test_tr = dataset_test[:int(n_train / 2)]
     test_vl = dataset_test[int(n_train / 2): int(n_train / 2) + int(n_validation / 2)]
     new_train = test_tr + train[:n_train - len(test_tr)]
-    new_validation = test_vl + validation[:n_validation - len(test_vl)]
+    new_validation = test_vl + valid[:n_validation - len(test_vl)]
     return new_train, new_validation
 
 id2test = {n: t for n, t in enumerate([test_1, test_2, test_3, test_4])}

process/tweet_nerd.py

@@ -48,20 +48,20 @@ 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_hate", exist_ok=True)
-with open("data/tweet_hate/test.jsonl", "w") as f:
+os.makedirs("data/tweet_nerd", exist_ok=True)
+with open("data/tweet_nerd/test.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in test]))
-with open("data/tweet_hate/test_1.jsonl", "w") as f:
+with open("data/tweet_nerd/test_1.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in test_1]))
-with open("data/tweet_hate/test_2.jsonl", "w") as f:
+with open("data/tweet_nerd/test_2.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in test_2]))
-with open("data/tweet_hate/test_3.jsonl", "w") as f:
+with open("data/tweet_nerd/test_3.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in test_3]))
-with open("data/tweet_hate/test_4.jsonl", "w") as f:
+with open("data/tweet_nerd/test_4.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in test_4]))
-with open("data/tweet_hate/train.jsonl", "w") as f:
+with open("data/tweet_nerd/train.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in train]))
-with open("data/tweet_hate/validation.jsonl", "w") as f:
+with open("data/tweet_nerd/validation.jsonl", "w") as f:
     f.write("\n".join([json.dumps(i) for i in valid]))
 
 

tweet_temporal_shift.py

@@ -2,7 +2,7 @@
 import json
 import datasets
 
-_VERSION = "1.1.0"
+_VERSION = "1.1.1"
 _TWEET_TEMPORAL_DESCRIPTION = """"""
 _TWEET_TEMPORAL_CITATION = """"""
 _TWEET_TOPIC_DESCRIPTION = """
@@ -115,7 +115,7 @@ class TweetTemporalShift(datasets.GeneratorBasedBuilder):
             name="hate_temporal",
             description=_TWEET_HATE_DESCRIPTION,
             citation=_TWEET_HATE_CITATION,
-            features=["text", "gold_label_binary", "date", "id", "source"],
+            features=["text", "gold_label_binary", "date", "id"],
             data_url=f"{_ROOT_URL}/tweet_hate",
         ),
         TweetTemporalShiftConfig(
@@ -168,7 +168,7 @@ class TweetTemporalShift(datasets.GeneratorBasedBuilder):
                     name=f"hate_random{i}_seed{s}",
                     description=_TWEET_HATE_DESCRIPTION,
                     citation=_TWEET_HATE_CITATION,
-                    features=["text", "gold_label_binary", "date", "id", "source"],
+                    features=["text", "gold_label_binary", "date", "id"],
                     data_url=f"{_ROOT_URL}/tweet_hate_test{i}_seed{s}",
                 ),
                 TweetTemporalShiftConfig(
@@ -224,7 +224,6 @@ class TweetTemporalShift(datasets.GeneratorBasedBuilder):
             features["gold_label_binary"] = datasets.Value("int32")
             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")