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Datasets:
cardiffnlp
/
tweet_topic_single

Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
ArXiv:
Libraries:
Datasets
License:
Dataset card Data Studio Files Community
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@@ -59,21 +59,21 @@ The label2id dictionary can be found at [here](https://huggingface.co/datasets/t
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  | split | number of texts | description |
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  |:--------------------------|-----:|:-----|
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- | test | 1679 | alias of `temporal_2021_test` |
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- | train | 1505 | alias of `temporal_2020_train` |
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- | validation | 188 | alias of `temporal_2020_validation` |
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- | temporal_2020_test | 573 | test set in 2020 period of temporal split |
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- | temporal_2021_test | 1679 | test set in 2021 period of temporal split |
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- | temporal_2020_train | 4585 | training set in 2020 period of temporal split |
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- | temporal_2021_train | 1505 | training set in 2021 period of temporal split |
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- | temporal_2020_validation | 573 | validation set in 2020 period of temporal split |
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- | temporal_2021_validation | 188 | validation set in 2021 period of temporal split |
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- | random_train | 4564 | training set of random split (mix of 2020 and 2021) |
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- | random_validation | 573 | validation set of random split (mix of 2020 and 2021) |
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- | coling2022_random_test | 5536 | test set of random split used in COLING 2022 Tweet Topic paper |
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- | coling2022_random_train | 5731 | training set of random split used in COLING 2022 Tweet Topic paper |
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- | coling2022_temporal_test | 5536 | test set of temporal split used in COLING 2022 Tweet Topic paper |
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- | coling2022_temporal_train | 5731 | training set of temporal split used in COLING 2022 Tweet Topic paper|
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  For the temporal-shift setting, we recommend to train models on `train` (`temporal_2020_train`) with `validation` (`temporal_2020_validation`) and evaluate on `test` (`temporal_2021_test`).
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  For the random split, we recommend to train models on `random_train` with `random_validation` and evaluate on `test` (`temporal_2021_test`).
 
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  | split | number of texts | description |
61
  |:--------------------------|-----:|:-----|
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+ | test | 1693 | alias of `temporal_2021_test` |
63
+ | train | 1516 | alias of `temporal_2020_train` |
64
+ | validation | 189 | alias of `temporal_2020_validation` |
65
+ | temporal_2020_test | 376 | test set in 2020 period of temporal split |
66
+ | temporal_2021_test | 1693 | test set in 2021 period of temporal split |
67
+ | temporal_2020_train | 2858 | training set in 2020 period of temporal split |
68
+ | temporal_2021_train | 1516 | training set in 2021 period of temporal split |
69
+ | temporal_2020_validation | 352 | validation set in 2020 period of temporal split |
70
+ | temporal_2021_validation | 189 | validation set in 2021 period of temporal split |
71
+ | random_train | 2830 | training set of random split (mix of 2020 and 2021) |
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+ | random_validation | 354 | validation set of random split (mix of 2020 and 2021) |
73
+ | coling2022_random_test | 3399 | test set of random split used in COLING 2022 Tweet Topic paper |
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+ | coling2022_random_train | 3598 | training set of random split used in COLING 2022 Tweet Topic paper |
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+ | coling2022_temporal_test | 3399 | test set of temporal split used in COLING 2022 Tweet Topic paper |
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+ | coling2022_temporal_train | 3598 | training set of temporal split used in COLING 2022 Tweet Topic paper|
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  For the temporal-shift setting, we recommend to train models on `train` (`temporal_2020_train`) with `validation` (`temporal_2020_validation`) and evaluate on `test` (`temporal_2021_test`).
79
  For the random split, we recommend to train models on `random_train` with `random_validation` and evaluate on `test` (`temporal_2021_test`).