J-hartmann

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Average Model Cost: $0.0000

Number of Runs: 1,124,278

Models by this creator

emotion-english-distilroberta-base

emotion-english-distilroberta-base

j-hartmann

The Emotion English DistilRoBERTa-base model is a text classification model that can be used to classify emotions in English text data. It predicts six basic emotions (anger, disgust, fear, joy, sadness, surprise) along with a neutral class. The model is fine-tuned from the DistilRoBERTa-base checkpoint. It was trained on a diverse set of datasets from sources such as Twitter, Reddit, student self-reports, and TV dialogues. The model can be used with just three lines of code in Google Colab and can handle single examples as well as multiple examples and full datasets. The model achieves an evaluation accuracy of 66% on a balanced subset of the training data.

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$-/run

1.1M

Huggingface

emotion-english-roberta-large

emotion-english-roberta-large

The emotion-english-roberta-large model is a text classification model trained on English text data to predict emotions. It is based on the RoBERTa architecture, a transformer-based model that excels in various natural language processing tasks. The model can take as input a piece of text and determine the emotion associated with it, such as happiness, sadness, anger, fear, or neutral. It has been trained on a large dataset and is capable of accurately identifying emotions in a wide range of contexts.

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$-/run

19.7K

Huggingface

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