Fine-Tuning MVP+M and BART for Natural Language Generation Tasks

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So basically, what this means is that we have a bunch of pre-trained language models (PLMs) like MVP, MVP with task-specific prompts, and multi-task pre-trained variants. These PLMs are trained on large general corpora to learn how to generate text in an unsupervised manner. But sometimes, we want them to be able to perform specific tasks like summarization or question generation. That’s where fine-tuning comes in!

Fine-tuning involves taking a pre-trained PLM and adapting it to a particular task by training on labeled data for that task. For example, if we want our MVP model to be able to generate summaries of news articles, we can fine-tune it using labeled datasets like CNN/Daily Mail or XSum.

So let’s say we have this script:

# This script is used for fine-tuning a pre-trained PLM for a specific task, in this case, generating summaries of news articles.

# The first line imports the necessary libraries for running the script.
#!/bin/bash

# The next line specifies the Python script to be executed, in this case, "run_textbox.py".
python run_textbox.py

# The "--model" flag specifies the type of PLM to be used, in this case, "MVP".
--model=MVP

# The "--dataset" flag specifies the name of the dataset to be used for fine-tuning, which should be replaced with the actual dataset name.
--dataset=[dataset_name]

# The "--model_path" flag specifies the path to the pre-trained PLM, in this case, "RUCAIBox/mvp-summarization".
--model_path=RUCAIBox/mvp-summarization

This command tells TextBox to use the MVP model (which is pre-trained on a general corpus), fine-tune it using labeled data for summarization, and save the resulting model in RUCAIBox’s mvp-summarization folder.

Now let’s say we want to generate a summary of this article: “How to Make Your Own Pizza Dough”. We can use our fine-tuned MVP+S/M (which is specifically trained for summarization) to do that! Here’s how it works:
1. The input text (“How to Make Your Own Pizza Dough”) is fed into the pre-trained PLM (in this case, MVP).
2. The PLM generates a summary based on its learned knowledge of language and context.
3. If we want our model to be even better at summarization, we can fine-tune it using labeled data for that task. This helps the model learn how to generate more accurate and relevant summaries.
4. Once our model is trained, we can use it to generate summaries of any text input!

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