emotion_text_classifier
Maintainer: michellejieli
51
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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 emotion_text_classifier model is a fine-tuned version of the DistilRoBERTa-base model for emotion classification. It was developed by maintainer michellejieli and trained on transcripts from the Friends TV show. The model can predict 6 Ekman emotions (anger, disgust, fear, joy, sadness, surprise) as well as a neutral class from text data, such as dialogue from movies or TV shows.
The emotion_text_classifier model is similar to other fine-tuned BERT-based models for emotion recognition, such as the distilbert-base-uncased-emotion model. These models leverage the power of large language models like BERT and DistilRoBERTa to achieve strong performance on the emotion classification task.
Model inputs and outputs
Inputs
- Text: The model takes in a single text input, which can be a sentence, paragraph, or longer text excerpt.
Outputs
- Emotion labels: The model outputs a list of emotion labels and their corresponding probability scores. The possible emotion labels are anger, disgust, fear, joy, neutrality, sadness, and surprise.
Capabilities
The emotion_text_classifier model can accurately predict the emotional state expressed in a given text, which can be useful for applications like sentiment analysis, content moderation, and customer service chatbots. For example, the model can identify that the text "I love this!" expresses joy with a high probability.
What can I use it for?
The emotion_text_classifier model can be used in a variety of applications that require understanding the emotional tone of text data. Some potential use cases include:
- Sentiment analysis: Analyzing customer reviews or social media posts to gauge public sentiment towards a product or brand.
- Affective computing: Developing intelligent systems that can recognize and respond to human emotions, such as chatbots or digital assistants.
- Content moderation: Flagging potentially harmful or inappropriate content based on the emotional tone.
- Behavioral analysis: Understanding the emotional state of individuals in areas like mental health, education, or human resources.
Things to try
One interesting aspect of the emotion_text_classifier model is its ability to distinguish between nuanced emotional states, such as the difference between anger and disgust. Experimenting with a variety of input texts, from everyday conversations to more complex emotional expressions, can provide insights into the model's capabilities and limitations.
Additionally, you could explore using the model in combination with other NLP techniques, such as topic modeling or named entity recognition, to gain a more holistic understanding of the emotional content in a given text corpus.
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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