emotion-english-distilroberta-base
Maintainer: j-hartmann
294
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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-english-distilroberta-base model is a fine-tuned checkpoint of the DistilRoBERTa-base model that can classify emotions in English text data. It was trained on 6 diverse datasets to predict Ekman's 6 basic emotions plus a neutral class. This model is a more compact version of the Emotion English RoBERTa-large model, offering faster inference while retaining strong performance.
Model Inputs and Outputs
Inputs
- English text data
Outputs
- A prediction of one of the following 7 emotion classes: anger, disgust, fear, joy, neutral, sadness, or surprise.
Capabilities
The emotion-english-distilroberta-base model can accurately classify the emotions expressed in English text. For example, when given the input "I love this!", the model correctly predicts that the text expresses joy with a high confidence score.
What Can I Use It For?
The model can be used to add emotion analysis capabilities to a variety of applications that process English text data, such as customer service chatbots, content moderation systems, or social media analysis tools. By understanding the emotional sentiment behind text, developers can build more empathetic and engaging experiences for users.
To get started, you can use the model with just 3 lines of code in a Colab notebook:
from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
classifier("I love this!")
You can also run the model on larger datasets and explore more advanced use cases in another Colab notebook:
Things to Try
One interesting aspect of this model is its ability to handle a range of emotional expressions beyond just positive and negative sentiment. By predicting the specific emotion (e.g. anger, fear, surprise), the model can provide more nuanced insights that could be valuable for applications like customer service or content moderation.
Additionally, the fact that this is a distilled version of a larger RoBERTa model means it can offer faster inference speeds, which could be important for real-time applications processing large volumes of text. Developers could experiment with using this model in production environments to see how it performs compared to larger, slower models.
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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