stories-emotion-c2

Modeling Emotional Trajectories in Written Stories

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This model is intended to predict emotions (valence, arousal) in written stories. For all details see the paper and the accompanying github repo.

Model Description

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As described in the paper, this model is finetuned from DeBERTaV3-large and predicts sentence-wise valence/arousal values between 0 and 1.

This particular checkpoint was trained with a window size of 2.

All available checkpoints and their performance measured by Concordance Correlation Coefficient (CCC):

We provide the best out of 5 seeds for each context size. Hence, the numbers in this table differ from the result table in the paper, where the mean performance across 5 seeds is reported.

Technically, this model predicts token-wise valence/arousal values. Sentences are concatenated via the [SEP] token, where the valence/arousal predictions for an [SEP] token are meant to be the predictions for the sentence preceding it. All other tokens' predictions should be ignored. For reference, see the figure in the paper:

The accompanying repo provides a convenient script to use the model for prediction.

Model Sources

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• Repository: Github
• Paper: ArXiv

Uses

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This model is intended to predict emotions (valence, arousal) in written stories. It was mainly trained on stories for children. Please note that the model is not production-ready and provided here for demonstration purposes only. For details on the datasets used, please refer to the paper.

In the github repository, a convenient script to predict V/A in existing texts is provided. Example call:

python3 predict.py --input_csv input_file.csv --output_csv output_file.csv --checkpoint_dir chrlukas/stories-emotion-c4 --window_size 4 --batch_size 4

Bias, Risks, and Limitations

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Please see the Limitations section in the paper. Please note that the model is not production-ready and provided here for demonstration purposes only.

Citation [optional]

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BibTeX:

Model Card Contact

For further inquiries, please contact lukas1[dot]christ[at]uni-a[dot].de