Datasets:

cardiffnlp
/

tweet_topic_single

Tasks:

Text Classification

Modalities:

Sub-tasks:

sentiment-classification

Languages:

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asahi417 commited on Sep 6, 2022

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2b1ff38

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@@ -77,7 +77,8 @@ The label2id dictionary can be found at [here](https://huggingface.co/datasets/t
 For the temporal-shift setting, we recommend to train models on `train` (an alias of `temporal_2020_train`) with `validation` (an alias of `temporal_2020_validation`) and evaluate on `test` (an alias of `temporal_2021_test`).
 For the random split, we recommend to train models on `random_train` with `random_validation` and evaluate on `test` (`temporal_2021_test`).
-To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `coling2022_temporal_train` and `coling2022_temporal_test` for temporal-shift, and `coling2022_random_train` and `coling2022_temporal_test` fir random split (note that the coling2022 split does not have validation set).
 ### Citation Information

 For the temporal-shift setting, we recommend to train models on `train` (an alias of `temporal_2020_train`) with `validation` (an alias of `temporal_2020_validation`) and evaluate on `test` (an alias of `temporal_2021_test`).
 For the random split, we recommend to train models on `random_train` with `random_validation` and evaluate on `test` (`temporal_2021_test`).
+**IMPORTANT NOTE:** To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `coling2022_temporal_train` and `coling2022_temporal_test` for temporal-shift, and `coling2022_random_train` and `coling2022_temporal_test` fir random split (the coling2022 split does not have validation set).
 ### Citation Information