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@@ -10,7 +10,6 @@ metrics:
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  - accuracy
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  tags:
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  - medical
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- pipeline_tag: text2text-generation
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  ---
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  # Model Card for Model ID
@@ -223,7 +222,7 @@ Using Lora
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  #### Preprocessing [optional]
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- Original:
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  ```json
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  StableLMEpochForCausalLM(
@@ -253,6 +252,13 @@ StableLMEpochForCausalLM(
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  (lm_head): Linear(in_features=2560, out_features=50304, bias=False)
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  )
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  ```
 
 
 
 
 
 
 
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  #### Training Hyperparameters
 
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  - accuracy
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  tags:
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  - medical
 
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  ---
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  # Model Card for Model ID
 
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  #### Preprocessing [optional]
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+ Original Model Configuration:
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  ```json
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  StableLMEpochForCausalLM(
 
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  (lm_head): Linear(in_features=2560, out_features=50304, bias=False)
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  )
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  ```
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+ Data Formatting :
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+
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+ ```json
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+ Given a target sentence construct the underlying meaning representation of the input sentence as a single function with attributes and attribute values.
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+ This function should describe the target string accurately and the function must be one of the following ['inform', 'request', 'give_opinion', 'confirm', 'verify_attribute', 'suggest', 'request_explanation', 'recommend', 'request_attribute'].
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+ The attributes must be one of the following: ['name', 'pathology', 'therapeutic', 'dosage', 'side_effects', 'contraindications', 'manufacturer', 'price', 'availability', 'administration', 'warnings', 'interactions', 'storage', 'expiration_date', 'formulation', 'strength', 'route_of_administration', 'class', 'prescription_required', 'generic_name', 'brand_name', 'patient_instructions']
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+ ```
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  #### Training Hyperparameters