Maintainer: cardiffnlp

Total Score


Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The twitter-xlm-roberta-base-sentiment model is a multilingual XLM-roBERTa-base model trained on ~198M tweets and fine-tuned for sentiment analysis. The model supports sentiment analysis in 8 languages (Arabic, English, French, German, Hindi, Italian, Spanish, and Portuguese), but can potentially be used for more languages as well. This model was developed by cardiffnlp.

Similar models include the xlm-roberta-base-language-detection model, which is a fine-tuned version of the XLM-RoBERTa base model for language identification, and the xlm-roberta-large and xlm-roberta-base models, which are the base and large versions of the multilingual XLM-RoBERTa model.

Model inputs and outputs


  • Text sequences for sentiment analysis


  • A label indicating the predicted sentiment (Positive, Negative, or Neutral)
  • A score representing the confidence of the prediction


The twitter-xlm-roberta-base-sentiment model can perform sentiment analysis on text in 8 languages: Arabic, English, French, German, Hindi, Italian, Spanish, and Portuguese. It was trained on a large corpus of tweets, giving it the ability to analyze the sentiment of short, informal text.

What can I use it for?

This model can be used for a variety of applications that require multilingual sentiment analysis, such as social media monitoring, customer service analysis, and market research. By leveraging the model's ability to analyze sentiment in multiple languages, developers can build applications that can process text from a wide range of sources and users.

Things to try

One interesting thing to try with this model is to experiment with the different languages it supports. Since the model was trained on a diverse dataset of tweets, it may be able to capture nuances in sentiment that are specific to certain cultures or languages. Developers could try using the model to analyze sentiment in languages beyond the 8 it was specifically fine-tuned on, to see how it performs.

Another idea is to compare the performance of this model to other sentiment analysis models, such as the bart-large-mnli or valhalla models, to see how it fares on different types of text and tasks.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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The twitter-roberta-base-sentiment-latest model is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021 and fine-tuned for sentiment analysis using the TweetEval benchmark. This model builds on the original Twitter-based RoBERTa model and the TweetEval benchmark. The model is suitable for English language sentiment analysis and was created by the cardiffnlp team. Model inputs and outputs The twitter-roberta-base-sentiment-latest model takes in English text and outputs sentiment labels of 0 (Negative), 1 (Neutral), or 2 (Positive), along with confidence scores for each label. The model can be used for both simple sentiment analysis tasks as well as more advanced text classification projects. Inputs English text, such as tweets, reviews, or other short passages Outputs Sentiment label (0, 1, or 2) Confidence score for each sentiment label Capabilities The twitter-roberta-base-sentiment-latest model can accurately classify the sentiment of short English text. It excels at analyzing the emotional tone of tweets, social media posts, and other informal online content. The model was trained on a large, up-to-date dataset of tweets, giving it strong performance on the nuanced language used in many online conversations. What can I use it for? This sentiment analysis model can be used for a variety of applications, such as: Monitoring brand reputation and customer sentiment on social media Detecting emotional reactions to news, events, or products Analyzing customer feedback and reviews to inform business decisions Powering chatbots and virtual assistants with natural language understanding Things to try To get started with the twitter-roberta-base-sentiment-latest model, you can try experimenting with different types of text inputs, such as tweets, customer reviews, or news articles. See how the model performs on short, informal language versus more formal written content. You can also try combining this sentiment model with other NLP tasks, like topic modeling or named entity recognition, to gain deeper insights from your data.

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