metadata
license: mit
datasets:
- tatsu-lab/alpaca
tags:
- generated_from_trainer
- text2text-generation
model-index:
- name: T5R-base
results: []
pipeline_tag: text2text-generation
language:
- en
widget:
- text: >
Instruction: X
Output: Adolf Hitler (German: [ˈadɔlf ˈhɪtlɐ] (listen); 20 April 1889 – 30
April 1945) was an Austrian-born German politician who was the dictator of
Germany from 1933 until his suicide in 1945. He rose to power as the
leader of the Nazi Party,[a] becoming the chancellor in 1933 and then
taking the title of Führer und Reichskanzler in 1934.[b] During his
dictatorship, he initiated World War II in Europe by invading Poland on 1
September 1939. He was closely involved in military operations throughout
the war and was central to the perpetration of the Holocaust: the genocide
of about six million Jews and millions of other victims.
X:
example_title: Example 1
- text: >
Instruction: X
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.
What kind of instruction could this be the answer to?
X:
example_title: Example 2
T5-Reverse (T5R)
This model can generate prompts (instructions) for any text!
This model is an instruction-tuned version of google/flan-t5-base on alpaca dataset but in reverse format!
How to Use the Model
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:
>>> # Import required libraries
>>> import torch
>>> from transformers import pipeline
>>> # Load the model and tokenizer using the pipeline from Hugging Face Hub
>>> inference = pipeline("text2text-generation", model="kargaranamir/T5R-base")
>>> # Example instruction and prompt
>>> sample = '''
>>> Instruction: X
>>> 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.
>>> What kind of instruction could this be the answer to?
>>> X:
>>> '''
>>> # Generate a response using the model
>>> res = inference(sample)
>>> # Print the generated response
>>> print(res)
[{'generated_text': 'Instruction: Generate three recommendations for a healthy diet.'}]
Citation
If you find this model/approach useful, make a link to the huggingface model.