ImKeTT / ZeroGen

[NLPCC'23] ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles PyTorch Implementation

ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles

Official PyTorch implementation of ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles (https://arxiv.org/abs/2306.16649), accepted to NLPCC 2023.

Setup

Make sure you have installed:

transformers
nltk
scikit-learn
torch
numpy
tqdm

Data and Model Weights

Data Structure

The extra data contains:

1. Objects, textual features, ect. for MSCOCO, Flickr30k, Flickr10k, VisNews.
2. The training/test data for Flickr10k and VisNews.
3. evaluation suite for captioning and text control evaluations.
4. npy_data folder for extracted GloVe features.

Data Processing and Preparation

For processing these data and obtain the whole test data:

1. For the test data (images and captions) of MSCOCO and Flickr30k, please refer to the downloading details from this repository. Put the datasets to the path you wish and change the DATA_DIR in config.json file accordingly.
2. For the test images of ViseNews, please refer to their official repository to donwload. Move the visnews folder to your data path, and images to the same visnews directory.
3. Move all files in flickr30_data_zerogen and mscoco_data_zerogen to the Flicrk30k and MSCOCO folders, respectively.
4. Move flickr10_data_zerogen and visnews_data_zerogen to data directory.
5. Put the evaluation folder to the current directory.

Note that, for all data employed, please follow their licenses for any other purpose.

Model Weights

Task	Weight
MSCOCO	https://huggingface.co/cambridgeltl/magic\_mscoco
Flickr30k	https://huggingface.co/cambridgeltl/magic\_flickr30k
Flickr10k-romantic	https://huggingface.co/PahaII/ZeroGen-flickr10k-romantic
Flickr10k-humor	https://huggingface.co/PahaII/ZeroGen-flickr10k-humor
VisNews	https://huggingface.co/PahaII/ZeroGen-visnews

ZeroGen Generation

TASK=mscoco
LENGTH=16
ALPHA=1.0
BETA=1.0
ETA=0.10
K=45
ALPHA_HAT=2.5
BETA_HAT=1.0
N=1

python run_zerogen.py --alpha ${ALPHA} --beta ${BETA} --eta ${ETA} --k ${K} --condition_method add \
                       --task ${TASK} --decoding_len ${LENGTH} --alpha_scale --alpha_activasize ${ALPHA_HAT}  \
                       --beta_scale --beta_activesize 0.2 --beta_upper ${BETA_HAT} --n_obj ${N} --kw_mode max --k2t

Here are recommended parameters for ZeroGen generation:

Task	$k$	$\alpha$	$\beta$	$\eta$	$\hat{\alpha}$	$\hat{\beta}$	$N$	length
MSCOCO	45	1.0	1.0	0.10	2.5	1.0	1~5	16
Flickr30k	25	2.0	1.0	0.10	2.0	0.5	1~5	16
Flickr10k-romantic	45	1.0	1.0	0.10	3.0	0.5	1	25
Flickr10k-humor	45	1.0	1.0	0.10	2.5	0.5	1	25
VisNews	5	8.0	1.0	0.65	8.0	0.5	40	64

We also support the inference of sequence-to-sequence models like FlanT5, just add --seq2seq flag and specify the model name via --language_model_name argument.

Baseline Models

For CapDec, ZeroCap, MAGIC baselines in captioning tasks, please refer to their official repositories.

For PPLM+MAGIC baseline in controllable news generation task, we provide a minimal implementation in the Pplm_Magic folder.

Citation

If you find our work useful, please consider cite our paper and star the repo :)

@article{tu2023zerogen,
  title={ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles},
  author={Tu, Haoqin and Yang, Bowen and Zhao, Xianfeng},
  journal={arXiv preprint arXiv:2306.16649},
  year={2023}
}

Please email me or open an issue if you have further questions. We thank open sourced codes related to zero-shot captioning and plug-and-play models, which inspired our work!