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Before applying `fastBPE` to the pre-training corpus of 850M English Tweets, we tokenized these Tweets using `TweetTokenizer` from the NLTK toolkit and used the `emoji` package to translate emotion icons into text strings (here, each icon is referred to as a word token). We also normalized the Tweets by converting user mentions and web/url links into special tokens `@USER` and `HTTPURL`, respectively. Thus it is recommended to also apply the same pre-processing step for BERTweet-based downstream applications w.r.t. the raw input Tweets. BERTweet provides this pre-processing step by enabling the `normalization` argument. This argument currently only supports models "`vinai/bertweet-base`", "`vinai/bertweet-covid19-base-cased`" and "`vinai/bertweet-covid19-base-uncased`".
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- Install `emoji`: `pip3 install emoji`
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```python
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import torch
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Before applying `fastBPE` to the pre-training corpus of 850M English Tweets, we tokenized these Tweets using `TweetTokenizer` from the NLTK toolkit and used the `emoji` package to translate emotion icons into text strings (here, each icon is referred to as a word token). We also normalized the Tweets by converting user mentions and web/url links into special tokens `@USER` and `HTTPURL`, respectively. Thus it is recommended to also apply the same pre-processing step for BERTweet-based downstream applications w.r.t. the raw input Tweets. BERTweet provides this pre-processing step by enabling the `normalization` argument. This argument currently only supports models "`vinai/bertweet-base`", "`vinai/bertweet-covid19-base-cased`" and "`vinai/bertweet-covid19-base-uncased`".
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- Install `emoji`: `pip3 install emoji==0.6.0`
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- The `emoji` version must be either 0.5.4 or 0.6.0. Newer `emoji` versions have been updated to newer versions of the Emoji Charts, thus not consistent with the one used for pre-processing our pre-training Tweet corpus.
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```python
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import torch
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