METRO-T0 is a T5-style text-to-text Transformer pretrained using model-generated pretraining signals, prompt-finetuned on a family of public NLP tasks proposed in T0. METRO-T0 is highly parameter efficient. For example, METRO-T0-Large++ (775M parameters) outperforms GPT-3 (175B parameters) and T0-3B (3B parameters) on a wide range of NLP tasks.

The architecture of METRO-T0 during pretraining using BERT as the auxiliary model to generate signals

Prompt learning results of METRO-T0 versus our T0 baseline and T03B by Sanh et al. (2022) on 4 tasks in the T0 Eval benchmark. Each point denotes the accuracy using one prompt template, except that the median accuracy over all templates of T03B is indicated by the blue point. The plots of other tasks are in our paper.

Use METRO-T0-Base++

To use METRO-T0-Base++ in PyTorch (Python 3.7+, PyTorch 1.12+ and transformers 4.17+ are prerequisites), refer to the code snippet below:

python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/metro_t0_basepp", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/metro_t0_basepp", trust_remote_code=True)

input_text = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"
inputs = tokenizer([input_text], max_length=512, truncation=True, add_special_tokens=True, return_tensors="pt").input_ids
outputs = model.generate(inputs, max_new_tokens=256, do_sample=False)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))  # expected: positive

Other METRO-T0 Models

	# Parameters	Pretraining Data	Prompt-Finetuning Data
METRO-T0-Base	226M	Wikibook (16G)	T0 Train
METRO-T0+-Base	226M	Wikibook (16G)	T0+ Train
METRO-T0++-Base	226M	Wikibook (16G)	T0++ Train
METRO-T0-Base++	256M	160G corpus	T0 Train
METRO-T0+-Base++	256M	160G corpus	T0+ Train
METRO-T0++-Base++	256M	160G corpus	T0++ Train
METRO-T0-Large++	775M	160G corpus	T0 Train
METRO-T0+-Large++	775M	160G corpus	T0+ Train
METRO-T0++-Large++	775M	160G corpus	T0++ Train

Citation

If you find the code and models useful for your research, please cite the following paper:

@misc{gong2023modelgenerated,
      title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers}, 
      author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song},
      year={2023},
      eprint={2305.12567},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2305.12567}
}