chrlukas
/

stories-emotion-c8

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 ---
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-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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@@ -174,28 +71,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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 ---
 library_name: transformers
+language:
+- en
+inference: false
 ---
+## Modeling Emotional Trajectories in Written Stories
 <!-- Provide a quick summary of what the model is/does. -->
+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).
 ### Model Description
 <!-- Provide a longer summary of what this model is. -->
+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.
+This particular checkpoint was trained with a window size of 8.
+All available checkpoints and their performance measured by Concordance Correlation Coefficient (CCC):
+| Model                                                                  | Valence dev/test   | Arousal dev/test   |
+|------------------------------------------------------------------------|--------------------|--------------------|
+|[stories-emotion-c0](https://huggingface.co/chrlukas/stories-emotion-c0)| .7091/.7187        | .5815/.6189        |
+|[stories-emotion-c1](https://huggingface.co/chrlukas/stories-emotion-c1)| .7715/.7875        | .6458/.6935        |
+|[stories-emotion-c2](https://huggingface.co/chrlukas/stories-emotion-c2)| .7922/.8074        | .6667/.6954        |
+|[stories-emotion-c4](https://huggingface.co/chrlukas/stories-emotion-c4)| .8078/.8146        | .6763/.7115        |
+|[stories-emotion-c8](https://huggingface.co/chrlukas/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:
+![image](tales_vertical.png)
+The [accompanying repo](https://github.com/lc0197/emotional_trajectories_stories) provides a convenient script to use the model for prediction.
+### Model Sources
+<!-- Provide the basic links for the model. -->
+- **Repository:** [Github](https://github.com/lc0197/emotional_trajectories_stories)
+- **Paper:** [ArXiv](#)
+## 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 (TODO)](#).
+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:
+``
+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]
 **BibTeX:**
 ## Model Card Contact
+For further inquiries, please contact lukas1[dot]christ[at]uni-a[dot].de