Fine-tuned DistilRoBERTa-base for Emotion Classification 🤬🤢😀😐😭😲
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 [email protected] 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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