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ML Emotion

ML emotion is a multilabel model that returns the emotions expressed in a sentence or subsentence. The model is composed by multiple adapters trained on diverses datasets, then fine-tuned on original goemotion dataset and its french translation. "value"'s value is always 1, since it's float value is not relevant on this task.

neutralNo particular emotion in this sentence.
admirationFinding something impressive or worthy of respect.
amusementFinding something funny or being entertained.
angerA strong feeling of displeasure or antagonism.
annoyanceMild anger irritation.
approvalHaving or expressing a favorable opinion.
caringDisplaying kindness and concern for others.
confusionLack of understanding uncertainty.
curiosityA strong desire to know or learn something.
desireA strong feeling of wanting something or wishing for something to happen.
disappointmentSadness or displeasure caused by the nonfulfillment of one’s hopes or expectations.
disapprovalHaving or expressing an unfavorable opinion.
disgustRevulsion or strong disapproval aroused by something unpleasant or offensive.
embarrassmentSelf-consciousness shame or awkwardness.
excitementFeeling of great enthusiasm and eagerness.
fearBeing afraid or worried.
gratitudeA feeling of thankfulness and appreciation.
griefIntense sorrow especially caused by someone’s death.
joyA feeling of pleasure and happiness.
loveA strong positive emotion of regard and affection.
nervousnessApprehension worry anxiety.
optimismHopefulness and confidence about the future or the success of something.
pridePleasure or satisfaction due to ones own achievements or the achievements of those with whom one is closely associated.
realizationBecoming aware of something.
reliefReassurance and relaxation following release from anxiety or distress.
remorseRegret or guilty feeling.
sadnessEmotional pain sorrow.
surpriseFeeling astonished startled by something unexpected.

Main data source : @misc{demszky2020goemotions, title={GoEmotions: A Dataset of Fine-Grained Emotions}, author={Dorottya Demszky and Dana Movshovitz-Attias and Jeongwoo Ko and Alan Cowen and Gaurav Nemade and Sujith Ravi}, year={2020}, eprint={2005.00547}, archivePrefix={arXiv}, primaryClass={cs.CL} } Adapters models: @inproceedings{pfeiffer2020AdapterHub, title={AdapterHub: A Framework for Adapting Transformers}, author={Jonas Pfeiffer and Andreas R\"uckl\'{e} and Clifton Poth and Aishwarya Kamath and Ivan Vuli\'{c} and Sebastian Ruder and Kyunghyun Cho and Iryna Gurevych}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations}, year={2020}, address = "Online", publisher = "Association for Computational Linguistics", url = "", pages = "46--54", }