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  # Twitter-roBERTa-base for Sentiment Analysis
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- This is a roBERTa-base model trained on ~58M tweets and finetuned for the Sentiment Analysis task at Semeval 2018.
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- For full description: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
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- To evaluate this and other models on Twitter-specific data, please refer to the [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
 
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  ## Example of classification
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@@ -18,6 +19,8 @@ import urllib.request
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  # Preprocess text (username and link placeholders)
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  def preprocess(text):
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  new_text = []
 
 
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  for t in text.split(" "):
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  t = '@user' if t.startswith('@') and len(t) > 1 else t
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  t = 'http' if t.startswith('http') else t
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  # Twitter-roBERTa-base for Sentiment Analysis
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+ This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark.
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+
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+ - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
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+ - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
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  ## Example of classification
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  # Preprocess text (username and link placeholders)
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  def preprocess(text):
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  new_text = []
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+
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+
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  for t in text.split(" "):
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  t = '@user' if t.startswith('@') and len(t) > 1 else t
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  t = 'http' if t.startswith('http') else t