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@@ -15,7 +15,8 @@ pipeline_tag: text-classification
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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 (TODO)](#) and [the accompanying github repo](https://github.com/lc0197/emotional_trajectories_stories).
 
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@@ -23,7 +24,7 @@ This model is intended to predict emotions (valence, arousal) in written stories
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  <!-- Provide a longer summary of what this model is. -->
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- As described in [the paper (TODO)](#), 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 4.
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@@ -51,14 +52,14 @@ The [accompanying repo](https://github.com/lc0197/emotional_trajectories_stories
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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](#)
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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 (TODO)](#).
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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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@@ -69,7 +70,7 @@ python3 predict.py --input_csv input_file.csv --output_csv output_file.csv --che
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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](#). 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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  <!-- 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
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+ [the accompanying github repo](https://github.com/lc0197/emotional_trajectories_stories).
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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 4.
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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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  ## 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]