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library_name: transformers
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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<!-- Provide a longer summary of what this model is. -->
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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## Uses
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## Model Card Contact
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[More Information Needed]
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library_name: transformers
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language:
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- en
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# Model Card for Model ID
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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 (TODO)](#).
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## Model Details
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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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All available checkpoints and their performance:
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Technically, this model is predicting token-wise values. Sentences are concatenated via the ``<s>`` token, where the valence/arousal predictions for an ``<s>`` 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](TODO) 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:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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## Uses
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## Model Card Contact
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[More Information Needed]
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