SiEBERT English Language Sentiment Classification Overview 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. Predictions on a data set 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). Use in a Hugging Face pipeline The easiest way to use th…
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