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sentiment-roberta-large-english

siebert

This model ("SiEBERT", prefix for "Sentiment in English") is a fine-tuned checkpoint of RoBERTa-large (Liu et al. 2019). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). Consequently, it outperforms models trained on only one type of text (e.g., movie reviews from the popular SST-2 benchmark) when used on new data as shown below. If you want to predict sentiment for your own data, we provide an example script via Google Colab. You can load your data to a Google Drive and run the script for free on a Colab GPU. Set-up only takes a few minutes. We suggest that you manually label a subset of your data to evaluate performance for your use case. For performance benchmark values across various sentiment analysis contexts, please refer to our paper (Hartmann et al. 2023). The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:

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