twitter-xlm-roberta-base-sentiment
Maintainer: cardiffnlp
169
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No 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
Inputs
- Text sequences for sentiment analysis
Outputs
- A label indicating the predicted sentiment (Positive, Negative, or Neutral)
- A score representing the confidence of the prediction
Capabilities
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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