roberta-base-go_emotions
Maintainer: SamLowe
342
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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 roberta-base-go_emotions model is a fine-tuned version of the RoBERTa base model that has been trained on the go_emotions dataset for multi-label classification. This model can be used to classify text into one or more of the 28 emotion labels present in the dataset, such as joy, anger, and fear.
Similar models include the xlm-roberta-large-xnli model, which is a multilingual zero-shot text classification model, and the bert-base-NER model, which is a fine-tuned BERT model for named entity recognition.
Model inputs and outputs
Inputs
- Text: The model takes a text sequence as input, which can be a sentence, paragraph, or longer document.
Outputs
- Emotion probabilities: The model outputs a list of 28 float values, each representing the probability that the input text corresponds to the corresponding emotion label.
Capabilities
The roberta-base-go_emotions model can be used to classify text into one or more emotion categories. This could be useful for applications such as sentiment analysis, customer service chatbots, or mental health monitoring tools. The multi-label approach allows the model to capture the nuance and complexity of human emotions, which can often involve a mix of different feelings.
What can I use it for?
You can use the roberta-base-go_emotions model for a variety of text classification tasks, particularly those involving emotion analysis. For example, you could use it to automatically detect the emotional tone of customer service interactions, social media posts, or online reviews. This could help businesses better understand their customers' experiences and target their marketing or support efforts more effectively.
The model could also be integrated into mental health applications, such as mood tracking apps or conversational agents, to provide insights into a user's emotional state over time. This could help identify potential mental health issues or provide personalized recommendations for coping strategies.
Things to try
One interesting aspect of this model is its ability to handle multi-label classification, which means it can identify multiple emotions in a single piece of text. This could be useful for analyzing more complex or nuanced emotional expressions, such as a mix of joy and frustration or anger and sadness.
To experiment with this capability, you could try feeding the model a variety of text samples, from short social media posts to longer form narratives, and observe how the model's emotion probability outputs change. This could provide valuable insights into the emotional complexity of human communication and help inform the design of more empathetic and responsive AI systems.
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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