chrlukas
/

stories-emotion-c4

@@ -20,11 +20,17 @@ As described in [the paper (TODO)](#), this model is finetuned from [DeBERTaV3-l
 This particular checkpoint was trained with a window size of $4$.
-All available checkpoints and their performance:
-Technically, this model is predicting token-wise 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:
@@ -34,144 +40,30 @@ The [accompanying repo](TODO) provides a convenient script to use the model for
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 -
 ## Uses
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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 ## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-### Results
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-#### Summary
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-#### Software
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 ## Citation [optional]

 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**|
+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:
 <!-- 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]