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