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Update README.md
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README.md
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@@ -37,14 +37,14 @@ You can use the `transformers` library to load and utilize the T5-Reverse (T5R)
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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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What kind of instruction could this be the answer to?
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@@ -52,9 +52,9 @@ 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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```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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What kind of instruction could this be the answer to?
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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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