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MVP

docs/source/en/model_doc/mvp.md

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This model was released on 2022-06-24 and added to Hugging Face Transformers on 2022-06-29.

MVP

<div class="flex flex-wrap space-x-1"> </div>

Overview

The MVP model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.

According to the abstract,

  • MVP follows a standard Transformer encoder-decoder architecture.
  • MVP is supervised pre-trained using labeled datasets.
  • MVP also has task-specific soft prompts to stimulate the model's capacity in performing a certain task.
  • MVP is specially designed for natural language generation and can be adapted to a wide range of generation tasks, including but not limited to summarization, data-to-text generation, open-ended dialogue system, story generation, question answering, question generation, task-oriented dialogue system, commonsense generation, paraphrase generation, text style transfer, and text simplification. Our model can also be adapted to natural language understanding tasks such as sequence classification and (extractive) question answering.

This model was contributed by Tianyi Tang. The detailed information and instructions can be found here.

Usage tips

  • We have released a series of models here, including MVP, MVP with task-specific prompts, and multi-task pre-trained variants.
  • If you want to use a model without prompts (standard Transformer), you can load it through MvpForConditionalGeneration.from_pretrained('RUCAIBox/mvp', device_map="auto").
  • If you want to use a model with task-specific prompts, such as summarization, you can load it through MvpForConditionalGeneration.from_pretrained('RUCAIBox/mvp-summarization', device_map="auto").
  • Our model supports lightweight prompt tuning following Prefix-tuning with method set_lightweight_tuning().

Usage examples

For summarization, it is an example to use MVP and MVP with summarization-specific prompts.

python
from transformers import MvpForConditionalGeneration, MvpTokenizer


tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp", device_map="auto")
model_with_prompt = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-summarization", device_map="auto")

inputs = tokenizer(
    "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.",
    return_tensors="pt",
)
generated_ids = model.generate(**inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Why You Shouldn't Quit Your Job"]

generated_ids = model_with_prompt.generate(**inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Don't do it if these are your reasons"]

For data-to-text generation, it is an example to use MVP and multi-task pre-trained variants.

python
from transformers import MvpForConditionalGeneration, MvpTokenizerFast


tokenizer = MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")
model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp", device_map="auto")
model_with_mtl = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-data-to-text", device_map="auto")

inputs = tokenizer(
    "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man",
    return_tensors="pt",
)
generated_ids = model.generate(**inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Stan Lee created the character of Iron Man, a fictional superhero appearing in American comic']

generated_ids = model_with_mtl.generate(**inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Iron Man is a fictional superhero appearing in American comic books published by Marvel Comics.']

For lightweight tuning, i.e., fixing the model and only tuning prompts, you can load MVP with randomly initialized prompts or with task-specific prompts. Our code also supports Prefix-tuning with BART following the original paper.

python
from transformers import MvpForConditionalGeneration


model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp", use_prompt=True, device_map="auto")
# the number of trainable parameters (full tuning)
sum(p.numel() for p in model.parameters() if p.requires_grad)
468116832

# lightweight tuning with randomly initialized prompts
model.set_lightweight_tuning()
# the number of trainable parameters (lightweight tuning)
sum(p.numel() for p in model.parameters() if p.requires_grad)
61823328

# lightweight tuning with task-specific prompts
model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-data-to-text", device_map="auto")
model.set_lightweight_tuning()
# original lightweight Prefix-tuning
model = MvpForConditionalGeneration.from_pretrained("facebook/bart-large", use_prompt=True, device_map="auto")
model.set_lightweight_tuning()

Resources

MvpConfig

[[autodoc]] MvpConfig

MvpTokenizer

[[autodoc]] MvpTokenizer

MvpTokenizerFast

[[autodoc]] MvpTokenizerFast

MvpModel

[[autodoc]] MvpModel - forward

MvpForConditionalGeneration

[[autodoc]] MvpForConditionalGeneration - forward

MvpForSequenceClassification

[[autodoc]] MvpForSequenceClassification - forward

MvpForQuestionAnswering

[[autodoc]] MvpForQuestionAnswering - forward

MvpForCausalLM

[[autodoc]] MvpForCausalLM - forward