Siebert

sentiment-roberta-large-english

siebert

104

The sentiment-roberta-large-english model is a fine-tuned checkpoint of the RoBERTa-large (Liu et al. 2019) model. It enables reliable binary sentiment analysis for various types of English-language text. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts, such as reviews and tweets. As a result, it outperforms models trained on only one type of text, like the popular SST-2 benchmark, when used on new data. Model inputs and outputs Inputs Text**: The model takes English-language text as input and performs sentiment analysis on it. Outputs Sentiment label**: The model outputs a binary sentiment label, either positive (1) or negative (0), for the input text. Capabilities The sentiment-roberta-large-english model can reliably classify the sentiment of various types of English-language text, including reviews, tweets, and more. It achieves strong performance on sentiment analysis tasks, outperforming models trained on a single data source. What can I use it for? You can use the sentiment-roberta-large-english model to perform sentiment analysis on your own English-language text data, such as customer reviews, social media posts, or any other textual content. This can be useful for tasks like understanding customer sentiment, monitoring brand reputation, or analyzing public opinion. The model is easy to use with the provided Google Colab script and the Hugging Face sentiment analysis pipeline. Things to try Consider evaluating the model's performance on a subset of your own data to understand how it performs for your specific use case. The maintainer has shared that the model was validated on emails and chat data, and outperformed other models on this type of text, especially for entities that don't start with an uppercase letter. You could explore using the model for similar types of informal, conversational text.

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