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experiments/main.sh

@@ -2,41 +2,59 @@
 MODEL="roberta-base"
 MODEL="vinai/bertweet-base"
 MODEL="jhu-clsp/bernice"
-MODEL="roberta-large"
-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-base-2022-154m"
+MODEL="roberta-large"
+MODEL="vinai/bertweet-large"
 MODEL="cardiffnlp/twitter-roberta-large-2022-154m"
 
+# todo
+MODEL="cardiffnlp/twitter-roberta-base-2022-154m"
+
 # EMOJI
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_temporal"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random0_seed0"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random1_seed0"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random2_seed0"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random3_seed0"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random0_seed1"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random1_seed1"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random2_seed1"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random3_seed1"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random0_seed2"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random1_seed2"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random2_seed2"
-rm -rf ckpt/emoji*${MODEL##*/}*
 python model_finetuning_emoji.py -m "${MODEL}" -d "emoji_random3_seed2"
-rm -rf ckpt/emoji*${MODEL##*/}*
+
+# HATE
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_temporal"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random0_seed0"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random1_seed0"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random2_seed0"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random3_seed0"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random0_seed1"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random1_seed1"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random2_seed1"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random3_seed1"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random0_seed2"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random1_seed2"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random2_seed2"
+rm -rf ckpt/hate*${MODEL##*/}*
+python model_finetuning_hate.py -m "${MODEL}" -d "hate_balance_random3_seed2"
+rm -rf ckpt/hate*${MODEL##*/}*
+
 
 # HATE
 python model_finetuning_hate.py -m "${MODEL}" -d "hate_temporal"

process/tweet_hate_balance.py

@@ -34,15 +34,17 @@ def sampler(chunk_index, r_seed):
 
     label_train_tmp_dist = dict(zip(*np.unique(df_train_tmp["gold_label_binary"], return_counts=True)))
     assert label_train_tmp_dist[0] < label_train_dist[0] and label_train_tmp_dist[1] < label_train_dist[1]
-    df_train_tmp_p = df_train_positive.sample(label_train_dist[0] - label_train_tmp_dist[0], random_state=r_seed)
-    df_train_tmp_n = df_train_positive.sample(label_train_dist[1] - label_train_tmp_dist[1], random_state=r_seed)
+    df_train_tmp_p = df_train_positive.sample(label_train_dist[1] - label_train_tmp_dist[1], random_state=r_seed)
+    df_train_tmp_n = df_train_negative.sample(label_train_dist[0] - label_train_tmp_dist[0], random_state=r_seed)
     df_train_tmp = pd.concat([df_train_tmp, df_train_tmp_p, df_train_tmp_n])
+    assert dict(zip(*np.unique(df_train_tmp["gold_label_binary"], return_counts=True))) == label_train_dist
 
     label_val_tmp_dist = dict(zip(*np.unique(df_val_tmp["gold_label_binary"], return_counts=True)))
     assert label_val_tmp_dist[0] < label_val_dist[0] and label_val_tmp_dist[1] < label_val_dist[1]
-    df_val_tmp_p = df_val_positive.sample(label_val_dist[0] - label_val_tmp_dist[0], random_state=r_seed)
-    df_val_tmp_n = df_val_positive.sample(label_val_dist[1] - label_val_tmp_dist[1], random_state=r_seed)
+    df_val_tmp_p = df_val_positive.sample(label_val_dist[1] - label_val_tmp_dist[1], random_state=r_seed)
+    df_val_tmp_n = df_val_negative.sample(label_val_dist[0] - label_val_tmp_dist[0], random_state=r_seed)
     df_val_tmp = pd.concat([df_val_tmp, df_val_tmp_p, df_val_tmp_n])
+    assert dict(zip(*np.unique(df_val_tmp["gold_label_binary"], return_counts=True))) == label_val_dist
     return list(df_train_tmp.T.to_dict().values()), list(df_val_tmp.T.to_dict().values())
 
 for n in range(4):

tweet_temporal_shift.py

@@ -2,7 +2,7 @@
 import json
 import datasets
 
-_VERSION = "1.1.3"
+_VERSION = "1.1.4"
 _TWEET_TEMPORAL_DESCRIPTION = """"""
 _TWEET_TEMPORAL_CITATION = """"""
 _TWEET_TOPIC_DESCRIPTION = """