conceptofmind / t5-pytorch

Implementation of Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer in PyTorch.

T5 - PyTorch (WIP)

A PyTorch implementation of Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. You can find the official T5x repository by Google here.

There is a small bug with dimensionality which needs to be resolved if someone wants to open a PR. I will not be able to personally get to this until later.

Acknowledgement

Phil Wang (lucidrains) advised and provided review for this implementation. Please be sure to follow and support his work.

Usage

import torch
from t5_pytorch import T5

model = T5(
    dim = 768,
    enc_num_tokens = 512,
    enc_depth = 6,
    enc_heads = 12,
    enc_dim_head = 64,
    enc_mlp_mult = 4,
    dec_num_tokens = 512,
    dec_depth = 6,
    dec_heads = 12,
    dec_dim_head = 64,
    dec_mlp_mult = 4,
    dropout = 0.,
    tie_token_emb = True
)

src = torch.randint(0, 512, (1, 1024))
src_mask = torch.ones_like(src).bool()
tgt = torch.randint(0, 512, (1, 1024))

output = model(src, tgt, mask = src_mask)

print(output.shape) #torch.Size([1, 1024, 512])

Abstract

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new ``Colossal Clean Crawled Corpus'', we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.

Citations

@misc{https://doi.org/10.48550/arxiv.1910.10683,
  doi = {10.48550/ARXIV.1910.10683},
  
  url = {https://arxiv.org/abs/1910.10683},
  
  author = {Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J.},
  
  keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
  
  publisher = {arXiv},
  
  year = {2019},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}