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@@ -22,7 +22,7 @@ The model is trained to classify sentences containing idiomatic expressions as e
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  ## Model Details
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  - **Developed by:** Abdallah Ashraf
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- - **Language(s) (NLP):** [English]
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  - **Finetuned from model:** bert-base-uncased
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  ## Uses
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  The model can be used directly for classifying idiomatic expressions in text data.
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- ### Downstream Use [optional]
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  The model can also be fine-tuned for specific downstream tasks, such as sentiment analysis or natural language understanding, by incorporating it into larger NLP pipelines.
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@@ -113,13 +113,13 @@ The model was trained using the AdamW optimizer with a learning rate of 2e-6 and
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  #### Training Hyperparameters
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- Training Hyperparameters
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- Training regime: Full fine-tuning
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- Optimizer: AdamW
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- Learning rate: 2e-6
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- momentum: 90-95
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- Weight decay: 0.01
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- loss function : Binary cross entropy loss
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  ## Evaluation
 
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  ## Model Details
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  - **Developed by:** Abdallah Ashraf
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+ - **Language(s) (NLP):** english
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  - **Finetuned from model:** bert-base-uncased
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  ## Uses
 
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  The model can be used directly for classifying idiomatic expressions in text data.
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+ ### Downstream Use
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  The model can also be fine-tuned for specific downstream tasks, such as sentiment analysis or natural language understanding, by incorporating it into larger NLP pipelines.
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  #### Training Hyperparameters
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+ * Training Hyperparameters
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+ * Training regime: Full fine-tuning
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+ * Optimizer: AdamW
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+ * Learning rate: 2e-6
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+ * momentum: 90-95
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+ * Weight decay: 0.01
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+ * loss function : Binary cross entropy loss
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  ## Evaluation