Text Classification
PyTorch
English
bert
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@@ -21,7 +21,7 @@ Social media texts were gathered from [ClimaConvo](https://github.com/shucoll/Cl
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  Non-social media texts were gathered from diverse sources including article abstracts (G11/climate_adaptation_abstracts), Wikipedia articles (pierre-pessarossi/wikipedia-climate-data), and IPCC reports (rlacombe/ClimateX).
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  The dataset contained about 60K instances, with a 50/50 distribution between the two classes. It was shuffled with a random seed of 42 and split into 80/20 for training/testing.
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- The NVIDIA V100-16GB GPU was used for training three epochs with a batch size of 8. Other hyperparameters were default values from the HuggingFace Trainer.
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  The model was trained in order to evaluate a text style transfer task, converting formal-language texts to tweets.
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  Non-social media texts were gathered from diverse sources including article abstracts (G11/climate_adaptation_abstracts), Wikipedia articles (pierre-pessarossi/wikipedia-climate-data), and IPCC reports (rlacombe/ClimateX).
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  The dataset contained about 60K instances, with a 50/50 distribution between the two classes. It was shuffled with a random seed of 42 and split into 80/20 for training/testing.
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+ The V100-16GB GPU was used for training three epochs with a batch size of 8. Other hyperparameters were default values from the HuggingFace Trainer.
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  The model was trained in order to evaluate a text style transfer task, converting formal-language texts to tweets.
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