saadMouraf
/

bert-based-emotion-detection-ar

@@ -18,190 +18,13 @@ tags:
 This model aims at classifying arabic text into its corresponding set of emotions by fine-tuning a variant of BERT pre-trained on arabic twitter data.
-## Model Details
-### Model Description
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 ### Model Sources [optional]
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 - **Repository:** https://github.com/TomoyeSaa/Emotion-Detection-for-Arabic-Language
-## 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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-## How to Get Started with the Model
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Training Hyperparameters
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 ## Evaluation
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-### Results
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-#### Summary
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-## Environmental Impact
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-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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-### Compute Infrastructure
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-## Citation [optional]
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 This model aims at classifying arabic text into its corresponding set of emotions by fine-tuning a variant of BERT pre-trained on arabic twitter data.
 ### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
 - **Repository:** https://github.com/TomoyeSaa/Emotion-Detection-for-Arabic-Language
 ## Evaluation
 <!-- This section describes the evaluation protocols and provides the results. -->
+Jaccard accuracy is the main eval metric and the model achieved 56.12%