library_name: transformers
language:
- en
inference: false
tags:
- emotion recognition
- valence
- arousal
- stories
- fairytales
pipeline_tag: text-classification
Modeling Emotional Trajectories in Written Stories
This model is intended to predict emotions (valence, arousal) in written stories. For all details see the paper (TODO) and the accompanying github repo.
Model Description
As described in the paper (TODO), 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 0.
All available checkpoints and their performance measured by Concordance Correlation Coefficient (CCC):
| Model | Valence dev/test | Arousal dev/test |
|---|---|---|
| stories-emotion-c0 | .7091/.7187 | .5815/.6189 |
| stories-emotion-c1 | .7715/.7875 | .6458/.6935 |
| stories-emotion-c2 | .7922/.8074 | .6667/.6954 |
| stories-emotion-c4 | .8078/.8146 | .6763/.7115 |
| stories-emotion-c8 | .8223/.8237 | .6829/.7120 |
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
Uses
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 (TODO).
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
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]
BibTeX:
Model Card Contact
For further inquiries, please contact lukas1[dot]christ[at]uni-a[dot].de