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Datasets:
tweettemposhift
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tweet_temporal_shift

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asahi417 commited on
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46fb100
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init

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  1. data/tweet_hate_balance/test.jsonl +0 -3
  2. data/tweet_hate_balance/test_1.jsonl +0 -3
  3. data/tweet_hate_balance/test_2.jsonl +0 -3
  4. data/tweet_hate_balance/test_3.jsonl +0 -3
  5. data/tweet_hate_balance/test_4.jsonl +0 -3
  6. data/tweet_hate_balance/train.jsonl +0 -3
  7. data/tweet_hate_balance/validation.jsonl +0 -3
  8. data/tweet_hate_balance_test0_seed0/train.jsonl +2 -2
  9. data/tweet_hate_balance_test0_seed0/validation.jsonl +2 -2
  10. data/tweet_hate_balance_test0_seed1/train.jsonl +2 -2
  11. data/tweet_hate_balance_test0_seed1/validation.jsonl +2 -2
  12. data/tweet_hate_balance_test0_seed2/train.jsonl +2 -2
  13. data/tweet_hate_balance_test0_seed2/validation.jsonl +2 -2
  14. data/tweet_hate_balance_test1_seed0/train.jsonl +2 -2
  15. data/tweet_hate_balance_test1_seed0/validation.jsonl +2 -2
  16. data/tweet_hate_balance_test1_seed1/train.jsonl +2 -2
  17. data/tweet_hate_balance_test1_seed1/validation.jsonl +2 -2
  18. data/tweet_hate_balance_test1_seed2/train.jsonl +2 -2
  19. data/tweet_hate_balance_test1_seed2/validation.jsonl +2 -2
  20. data/tweet_hate_balance_test2_seed0/train.jsonl +2 -2
  21. data/tweet_hate_balance_test2_seed0/validation.jsonl +2 -2
  22. data/tweet_hate_balance_test2_seed1/train.jsonl +2 -2
  23. data/tweet_hate_balance_test2_seed1/validation.jsonl +2 -2
  24. data/tweet_hate_balance_test2_seed2/train.jsonl +2 -2
  25. data/tweet_hate_balance_test2_seed2/validation.jsonl +2 -2
  26. data/tweet_hate_balance_test3_seed0/train.jsonl +2 -2
  27. data/tweet_hate_balance_test3_seed0/validation.jsonl +2 -2
  28. data/tweet_hate_balance_test3_seed1/train.jsonl +2 -2
  29. data/tweet_hate_balance_test3_seed1/validation.jsonl +2 -2
  30. data/tweet_hate_balance_test3_seed2/train.jsonl +2 -2
  31. data/tweet_hate_balance_test3_seed2/validation.jsonl +2 -2
  32. process/tweet_hate_balance.py +2 -1
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process/tweet_hate_balance.py CHANGED
@@ -37,15 +37,16 @@ def sampler(chunk_index, r_seed):
37
  df_train_tmp_p = df_train_positive.sample(label_train_dist[1] - label_train_tmp_dist[1], random_state=r_seed)
38
  df_train_tmp_n = df_train_negative.sample(label_train_dist[0] - label_train_tmp_dist[0], random_state=r_seed)
39
  df_train_tmp = pd.concat([df_train_tmp, df_train_tmp_p, df_train_tmp_n])
 
40
  assert dict(zip(*np.unique(df_train_tmp["gold_label_binary"], return_counts=True))) == label_train_dist
41
  print(dict(zip(*np.unique(df_train_tmp["gold_label_binary"], return_counts=True))))
42
- print(label_train_dist)
43
 
44
  label_val_tmp_dist = dict(zip(*np.unique(df_val_tmp["gold_label_binary"], return_counts=True)))
45
  assert label_val_tmp_dist[0] < label_val_dist[0] and label_val_tmp_dist[1] < label_val_dist[1]
46
  df_val_tmp_p = df_val_positive.sample(label_val_dist[1] - label_val_tmp_dist[1], random_state=r_seed)
47
  df_val_tmp_n = df_val_negative.sample(label_val_dist[0] - label_val_tmp_dist[0], random_state=r_seed)
48
  df_val_tmp = pd.concat([df_val_tmp, df_val_tmp_p, df_val_tmp_n])
 
49
  assert dict(zip(*np.unique(df_val_tmp["gold_label_binary"], return_counts=True))) == label_val_dist
50
  return list(df_train_tmp.T.to_dict().values()), list(df_val_tmp.T.to_dict().values())
51
 
 
37
  df_train_tmp_p = df_train_positive.sample(label_train_dist[1] - label_train_tmp_dist[1], random_state=r_seed)
38
  df_train_tmp_n = df_train_negative.sample(label_train_dist[0] - label_train_tmp_dist[0], random_state=r_seed)
39
  df_train_tmp = pd.concat([df_train_tmp, df_train_tmp_p, df_train_tmp_n])
40
+ df_train_tmp.index = range(len(df_train_tmp))
41
  assert dict(zip(*np.unique(df_train_tmp["gold_label_binary"], return_counts=True))) == label_train_dist
42
  print(dict(zip(*np.unique(df_train_tmp["gold_label_binary"], return_counts=True))))
 
43
 
44
  label_val_tmp_dist = dict(zip(*np.unique(df_val_tmp["gold_label_binary"], return_counts=True)))
45
  assert label_val_tmp_dist[0] < label_val_dist[0] and label_val_tmp_dist[1] < label_val_dist[1]
46
  df_val_tmp_p = df_val_positive.sample(label_val_dist[1] - label_val_tmp_dist[1], random_state=r_seed)
47
  df_val_tmp_n = df_val_negative.sample(label_val_dist[0] - label_val_tmp_dist[0], random_state=r_seed)
48
  df_val_tmp = pd.concat([df_val_tmp, df_val_tmp_p, df_val_tmp_n])
49
+ df_val_tmp.index = range(len(df_val_tmp))
50
  assert dict(zip(*np.unique(df_val_tmp["gold_label_binary"], return_counts=True))) == label_val_dist
51
  return list(df_train_tmp.T.to_dict().values()), list(df_val_tmp.T.to_dict().values())
52