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--- |
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library_name: transformers |
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language: |
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- en |
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inference: false |
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tags: |
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- emotion recognition |
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- valence |
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- arousal |
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- stories |
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- fairytales |
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pipeline_tag: text-classification |
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--- |
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## Modeling Emotional Trajectories in Written Stories |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is intended to predict emotions (valence, arousal) in written stories. For all details see [the paper](http://arxiv.org/abs/2406.02251) and [the accompanying github repo](https://github.com/lc0197/emotional_trajectories_stories). |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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As described in [the paper](http://arxiv.org/abs/2406.02251), this model is finetuned from [DeBERTaV3-large](https://huggingface.co./microsoft/deberta-v3-large) and predicts sentence-wise valence/arousal values between 0 and 1. |
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This particular checkpoint was trained with a window size of 2. |
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All available checkpoints and their performance measured by Concordance Correlation Coefficient (CCC): |
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| Model | Valence dev/test | Arousal dev/test | |
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|------------------------------------------------------------------------|--------------------|--------------------| |
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|[stories-emotion-c0](https://huggingface.co./chrlukas/stories-emotion-c0)| .7091/.7187 | .5815/.6189 | |
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|[stories-emotion-c1](https://huggingface.co./chrlukas/stories-emotion-c1)| .7715/.7875 | .6458/.6935 | |
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|[stories-emotion-c2](https://huggingface.co./chrlukas/stories-emotion-c2)| .7922/.8074 | .6667/.6954 | |
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|[stories-emotion-c4](https://huggingface.co./chrlukas/stories-emotion-c4)| .8078/.8146 | .6763/.7115 | |
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|[stories-emotion-c8](https://huggingface.co./chrlukas/stories-emotion-c8)| **.8223**/**.8237**| **.6829**/**.7120**| |
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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. |
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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 |
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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: |
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The [accompanying repo](https://github.com/lc0197/emotional_trajectories_stories) provides a convenient script to use the model for prediction. |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [Github](https://github.com/lc0197/emotional_trajectories_stories) |
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- **Paper:** [ArXiv](http://arxiv.org/abs/2406.02251) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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This model is intended to predict emotions (valence, arousal) in written stories. It was mainly trained on stories for children. |
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Please note that the model is not production-ready and provided here for demonstration purposes only. |
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For details on the datasets used, please refer to the [paper](http://arxiv.org/abs/2406.02251). |
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In the [github repository](https://github.com/lc0197/emotional_trajectories_stories), a convenient script to predict V/A in existing texts is provided. Example call: |
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`` |
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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 |
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`` |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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Please see the *Limitations* section in [the paper](http://arxiv.org/abs/2406.02251). Please note that the model is not production-ready and provided here for demonstration purposes only. |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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## Model Card Contact |
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For further inquiries, please contact lukas1[dot]christ[at]uni-a[dot].de |
