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Fine-tuned DistilRoBERTa-base for Emotion Classification πŸ€¬πŸ€’πŸ˜€πŸ˜πŸ˜­πŸ˜²

Model Description

DistilRoBERTa-base is a transformer model that performs sentiment analysis. I fine-tuned the model on transcripts from the Friends show with the goal of classifying emotions from text data, specifically dialogue from Netflix shows or movies. The model predicts 6 Ekman emotions and a neutral class. These emotions include anger, disgust, fear, joy, neutrality, sadness, and surprise.

The model is a fine-tuned version of Emotion English DistilRoBERTa-base and DistilRoBERTa-base. This model was initially trained on the following table from Emotion English DistilRoBERTa-base:

Name anger disgust fear joy neutral sadness surprise
Crowdflower (2016) Yes - - Yes Yes Yes Yes
Emotion Dataset, Elvis et al. (2018) Yes - Yes Yes - Yes Yes
GoEmotions, Demszky et al. (2020) Yes Yes Yes Yes Yes Yes Yes
ISEAR, Vikash (2018) Yes Yes Yes Yes - Yes -
MELD, Poria et al. (2019) Yes Yes Yes Yes Yes Yes Yes
SemEval-2018, EI-reg, Mohammad et al. (2018) Yes - Yes Yes - Yes -

It was fine-tuned on:

Name anger disgust fear joy neutral sadness surprise
Emotion Lines (Friends) Yes Yes Yes Yes Yes Yes Yes

How to Use

from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="michellejieli/emotion_text_classifier")
classifier("I love this!")
Output:
[{'label': 'joy', 'score': 0.9887555241584778}]

Contact

Please reach out to michelleli1999@gmail.com if you have any questions or feedback.

Reference

Jochen Hartmann, "Emotion English DistilRoBERTa-base". https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/, 2022.
Ashritha R Murthy and K M Anil Kumar 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1110 012009
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