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@@ -36,14 +36,14 @@ This model is an instruction-tuned version of [google/flan-t5-base](https://hugg
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  You can use the `transformers` library to load and utilize the T5-Reverse (T5R) model for generating prompts based on text. Here's an example of how to do it:
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  ```python
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- # Import required libraries
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  >>> import torch
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  >>> from transformers import pipeline
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- # Load the model and tokenizer using the pipeline from Hugging Face Hub
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  >>> inference = pipeline("text2text-generation", model="kargaranamir/T5R-base")
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- # Example instruction and prompt
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  >>> sample = '''
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  Instruction: X
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  Output: 1- Base your meals on higher fibre starchy carbohydrates. 2- Eat lots of fruit and veg. 3- Eat more fish, including a portion of oily fish.
@@ -51,10 +51,11 @@ What kind of instruction could this be the answer to?
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  X:
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  '''
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- # Generate a response using the model
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  >>> res = inference(sample)
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- # Print the generated response
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  >>> print(res)
 
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  [{'generated_text': 'Instruction: Generate three recommendations for a healthy diet.'}]
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  ```
 
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  You can use the `transformers` library to load and utilize the T5-Reverse (T5R) model for generating prompts based on text. Here's an example of how to do it:
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  ```python
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+ >>> # Import required libraries
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  >>> import torch
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  >>> from transformers import pipeline
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+ >>> # Load the model and tokenizer using the pipeline from Hugging Face Hub
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  >>> inference = pipeline("text2text-generation", model="kargaranamir/T5R-base")
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+ >>> # Example instruction and prompt
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  >>> sample = '''
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  Instruction: X
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  Output: 1- Base your meals on higher fibre starchy carbohydrates. 2- Eat lots of fruit and veg. 3- Eat more fish, including a portion of oily fish.
 
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  X:
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  '''
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+ >>> # Generate a response using the model
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  >>> res = inference(sample)
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+ >>> # Print the generated response
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  >>> print(res)
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+
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  [{'generated_text': 'Instruction: Generate three recommendations for a healthy diet.'}]
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  ```