stories-emotion-c4 / README.md
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---
library_name: transformers
language:
- en
---
# Model Card for Model ID
<!-- 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 (TODO)](#).
## Model Details
### 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 $4$.
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 ``<s>`` token, where the valence/arousal predictions for an ``<s>`` 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](TODO) provides a convenient script to use the model for prediction.
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [accompanying repo](TODO)
- **Paper:** [accompanying repo](TODO)
-
## 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](TODO), 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.
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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