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@@ -91,7 +91,8 @@ The label2id dictionary can be found at [here](https://huggingface.co/datasets/t
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  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`).
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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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- 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).
 
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  ### Citation Information
 
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  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`).
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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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+ **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).
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  ### Citation Information