chrlukas/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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Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> 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
<!-- This section is meant to convey both technical and sociotechnical limitations. --> 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